Skillget

harness-mcp-v2

mcp
SUMMARY

MCP server for Harness.io

README.md

Harness MCP Server 2.0

An MCP (Model Context Protocol) server that gives AI agents full access to the Harness.io platform through 10 consolidated tools and 137 resource types.

CI

Why Use This MCP Server

Most MCP servers map one tool per API endpoint. For a platform as broad as Harness, that means 240+ tools — and LLMs get worse at tool selection as the count grows. Context windows fill up with schemas, and every new endpoint means new code.

This server is built differently:

  • 10 tools, 137 resource types. A registry-based dispatch system routes harness_list, harness_get, harness_create, etc. to any Harness resource — pipelines, services, environments, orgs, projects, feature flags, cost data, and more. The LLM picks from 10 tools instead of hundreds.
  • Full platform coverage. 29 toolsets spanning CI/CD, GitOps, Feature Flags, Cloud Cost Management, Security Testing, Chaos Engineering, Internal Developer Portal, Software Supply Chain, Governance, Service Overrides, Visualizations, and more. Not just pipelines — the entire Harness platform.
  • Multi-project workflows out of the box. Agents discover organizations and projects dynamically — no hardcoded env vars needed. Ask "show failed executions across all projects" and the agent can navigate the full account hierarchy.
  • 26 prompt templates. Pre-built prompts for common workflows: build & deploy apps end-to-end, debug failed pipelines, review DORA metrics, triage vulnerabilities, optimize cloud costs, audit access control, plan feature flag rollouts, review pull requests, approve pending pipelines, and more.
  • Works everywhere. Stdio transport for local clients (Claude Desktop, Cursor, Windsurf), HTTP transport for remote/shared deployments, Docker and Kubernetes ready.
  • Zero-config start. Just provide a Harness API key. Account ID is auto-extracted from PAT tokens, org/project defaults are optional, and toolset filtering lets you expose only what you need.
  • Extensible by design. Adding a new Harness resource means adding a declarative data file — no new tool registration, no schema changes, no prompt updates.

Prerequisites

Before installing or running the server, you need a Harness API key:

  1. Log in to your Harness account
  2. Go to My Profile → API Keys → + New API Key
  3. Create a new Token under the API key — this generates a PAT in the format pat.<accountId>.<tokenId>.<secret>
  4. Save the token somewhere secure — you'll need it in the next step

For detailed instructions, see the Harness API Quickstart.

Quick Start

Option 1: npx (Recommended)

No install required — just run it:

HARNESS_API_KEY=pat.xxx.xxx.xxx npx harness-mcp-v2@latest

Or configure the API key in your AI client (see Client Configuration below).

# Stdio transport (default — for Claude Desktop, Cursor, Windsurf, etc.)
HARNESS_API_KEY=pat.xxx npx harness-mcp-v2

# HTTP transport (for remote/shared deployments)
HARNESS_API_KEY=pat.xxx npx harness-mcp-v2 http --port 8080

Note: The account ID is auto-extracted from PAT tokens (pat.<accountId>.<tokenId>.<secret>), so HARNESS_ACCOUNT_ID is only needed for non-PAT API keys.

Option 2: Global Install

npm install -g harness-mcp-v2

# Then run directly
harness-mcp-v2

Option 3: Build from Source

For development or customization:

git clone https://github.com/thisrohangupta/harness-mcp-v2.git
cd harness-mcp-v2
pnpm install
pnpm build

# Run
pnpm start              # Stdio transport
pnpm start:http         # HTTP transport
pnpm inspect            # Test with MCP Inspector

CLI Usage

harness-mcp-v2 [stdio|http] [--port <number>]

Options:
  --port <number>  Port for HTTP transport (default: 3000, or PORT env var)
  --help           Show help message and exit
  --version        Print version and exit

Transport defaults to stdio if not specified. Use http for remote/shared deployments.

HTTP Transport

When running in HTTP mode, the server exposes:

Endpoint Method Description
/mcp POST MCP JSON-RPC endpoint (initialize + session requests)
/mcp GET SSE stream for server-initiated messages (progress, elicitation)
/mcp DELETE Terminate an active MCP session
/mcp OPTIONS CORS preflight
/health GET Health check — returns { "status": "ok", "sessions": <count> }

The HTTP transport runs in session-based mode. A new MCP session is created on initialize, the server returns an mcp-session-id header, and subsequent requests for that session must include the same header.

Operational constraints in HTTP mode:

  • POST /mcp without mcp-session-id must be an initialize request.
  • POST /mcp, GET /mcp, and DELETE /mcp for existing sessions require the mcp-session-id header.
  • GET /mcp is used for SSE notifications (progress updates and elicitation prompts).
  • Idle sessions are reaped after 30 minutes.
  • GET /health is the only non-MCP endpoint.
  • Request body size is capped by HARNESS_MAX_BODY_SIZE_MB (default 10 MB).
# Health check
curl http://localhost:3000/health

# MCP initialize request (capture mcp-session-id response header)
curl -i -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

# Subsequent MCP request (use returned session ID)
curl -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -H "mcp-session-id: <session-id>" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}'

# Terminate session
curl -X DELETE http://localhost:3000/mcp \
  -H "mcp-session-id: <session-id>"

Client Configuration

Note: HARNESS_DEFAULT_ORG_ID and HARNESS_DEFAULT_PROJECT_ID are optional. Agents can discover orgs and projects dynamically using harness_list(resource_type="organization") and harness_list(resource_type="project"). Set them only if you want to pin a default scope for convenience.

Troubleshooting npx ENOENT or node: No such file or directory

GUI apps (Cursor, Claude Desktop, Windsurf, VS Code) don't inherit your shell's PATH, so they often can't find npx or node. Fix this by using absolute paths and explicitly setting PATH in the env block:

{
  "mcpServers": {
    "harness": {
      "command": "/absolute/path/to/npx",
      "args": ["-y", "harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx",
        "PATH": "/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin"
      }
    }
  }
}

Find your paths with which npx and which node in a terminal, then make sure the directory containing node is included in the PATH value above. Common locations:

  • Homebrew (macOS): /opt/homebrew/bin/npx
  • nvm: ~/.nvm/versions/node/v20.x.x/bin/npx (run nvm which current to find the exact path)
  • System Node: /usr/local/bin/npx

Claude Desktop (claude_desktop_config.json)

npx (zero install) 
{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}
node (local install) 
npm install -g harness-mcp-v2

{
  "mcpServers": {
    "harness": {
      "command": "harness-mcp-v2",
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}

Claude Code (via claude mcp add)

npx (zero install) 
claude mcp add harness -- npx harness-mcp-v2
node (local install) 
npm install -g harness-mcp-v2
claude mcp add harness -- harness-mcp-v2

Then set HARNESS_API_KEY in your environment or .env file.

Cursor (.cursor/mcp.json)

npx (zero install) 
{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}
node (local install) 
npm install -g harness-mcp-v2

{
  "mcpServers": {
    "harness": {
      "command": "harness-mcp-v2",
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}

Windsurf (~/.windsurf/mcp.json)

npx (zero install) 
{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}
node (local install) 
npm install -g harness-mcp-v2

{
  "mcpServers": {
    "harness": {
      "command": "harness-mcp-v2",
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}
Using a local build from source?

Replace the command with the path to your built index.js:

{
  "command": "node",
  "args": ["/absolute/path/to/harness-mcp-v2/build/index.js", "stdio"]
}

MCP Gateway

The Harness MCP server is fully compatible with MCP Gateways — reverse proxies that provide centralized authentication, governance, tool routing, and observability across multiple MCP servers. Since the server implements the standard MCP protocol with both stdio and HTTP transports, it works behind any MCP-compliant gateway with no code changes.

Why use a gateway?

  • Centralized credential management — no API keys in agent configs
  • Governance & audit logging for all tool calls across teams
  • Single endpoint for agents instead of N connections to N MCP servers
  • Access control — restrict which teams can use which tools

Docker MCP Gateway

Register the server in your Docker MCP Gateway configuration:

{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}

Portkey

Add the Harness MCP server to your Portkey MCP Gateway for enterprise governance, cost tracking, and multi-LLM routing:

{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx"
      }
    }
  }
}

LiteLLM

Add to your LiteLLM proxy config:

mcp_servers:
  - name: harness
    command: npx
    args:
      - harness-mcp-v2
    env:
      HARNESS_API_KEY: "pat.xxx.xxx.xxx"

Envoy AI Gateway

The server works with Envoy AI Gateway's MCP support via HTTP transport:

# Start the server in HTTP mode
HARNESS_API_KEY=pat.xxx.xxx.xxx npx harness-mcp-v2 http --port 8080

Then configure Envoy to route to http://localhost:8080/mcp as an upstream MCP backend.

Kong

Use Kong's AI MCP Proxy plugin to expose the Harness MCP server through your existing Kong gateway infrastructure.

Other Gateways

Any gateway that supports the MCP specification (Microsoft MCP Gateway, IBM ContextForge, Cloudflare Workers, etc.) can proxy this server. For stdio-based gateways, use the default transport. For HTTP-based gateways, start the server with http transport and point the gateway at the /mcp endpoint.

Docker

Build and run the server as a Docker container:

# Build the image
pnpm docker:build

# Run with your .env file
pnpm docker:run

# Or run directly with env vars
docker run --rm -p 3000:3000 \
  -e HARNESS_API_KEY=pat.xxx.xxx.xxx \
  -e HARNESS_ACCOUNT_ID=your-account-id \
  harness-mcp-server

The container runs in HTTP mode on port 3000 by default with a built-in health check.

Kubernetes

Deploy to a Kubernetes cluster using the provided manifests:

# 1. Edit the Secret with your real credentials
#    k8s/secret.yaml — replace HARNESS_API_KEY and HARNESS_ACCOUNT_ID

# 2. Apply all manifests
kubectl apply -f k8s/

# 3. Verify the deployment
kubectl -n harness-mcp get pods

# 4. Port-forward for local testing
kubectl -n harness-mcp port-forward svc/harness-mcp-server 3000:80
curl http://localhost:3000/health

The deployment runs 2 replicas with readiness/liveness probes, resource limits, and non-root security context. The Service exposes port 80 internally (targeting container port 3000).

Configuration

The server automatically loads environment variables from a .env file in the project root if one exists. Copy .env.example to .env and fill in your values. Environment variables can also be set via your shell or MCP client config.

Variable Required Default Description
HARNESS_API_KEY Yes -- Harness personal access token or service account token
HARNESS_ACCOUNT_ID No (from PAT) Harness account identifier. Auto-extracted from PAT tokens; only needed for non-PAT API keys
HARNESS_BASE_URL No https://app.harness.io Base URL (override for self-managed Harness)
HARNESS_DEFAULT_ORG_ID No default Default organization identifier. Optional convenience — agents can discover orgs dynamically via harness_list(resource_type="organization")
HARNESS_DEFAULT_PROJECT_ID No -- Default project identifier. Optional convenience — agents can discover projects dynamically via harness_list(resource_type="project")
HARNESS_API_TIMEOUT_MS No 30000 HTTP request timeout in milliseconds
HARNESS_MAX_RETRIES No 3 Retry count for transient failures (429, 5xx)
HARNESS_MAX_BODY_SIZE_MB No 10 Max HTTP request body size in MB for http transport
HARNESS_RATE_LIMIT_RPS No 10 Client-side request throttle (requests per second) to Harness APIs
LOG_LEVEL No info Log verbosity: debug, info, warn, error
HARNESS_TOOLSETS No (all) Comma-separated list of enabled toolsets (see Toolset Filtering)
HARNESS_READ_ONLY No false Block all mutating operations (create, update, delete, execute). Only list and get are allowed. Useful for shared/demo environments
HARNESS_SKIP_ELICITATION No false Skip all elicitation confirmation prompts. When true, write and delete operations proceed without user approval — enabling fully autonomous agent workflows. See Elicitation
HARNESS_ALLOW_HTTP No false Allow non-HTTPS HARNESS_BASE_URL. By default, the server enforces HTTPS for security. Set to true only for local development against a non-TLS Harness instance

HTTPS Enforcement

HARNESS_BASE_URL must use HTTPS by default. If you set a non-HTTPS URL (e.g. http://localhost:8080), the server will refuse to start with:

HARNESS_BASE_URL must use HTTPS (got "http://..."). If you need HTTP for local development, set HARNESS_ALLOW_HTTP=true.

Audit Logging

All write operations (harness_create, harness_update, harness_delete, harness_execute) emit structured audit log entries to stderr. Each entry includes the tool name, resource type, operation, identifiers, and timestamp. This provides an audit trail without requiring external logging infrastructure.

Tools Reference

The server exposes 11 MCP tools. Most API tools accept org_id and project_id as optional overrides — if omitted, they fall back to HARNESS_DEFAULT_ORG_ID and HARNESS_DEFAULT_PROJECT_ID. harness_describe is local metadata only and does not use org/project scope.

URL support: Most API-facing tools accept a url parameter — paste a Harness UI URL and the server auto-extracts org, project, resource type, resource ID, pipeline ID, and execution ID. harness_describe does not accept url.

Tool Description
harness_describe Discover available resource types, operations, and fields. No API call — returns local registry metadata.
harness_schema Fetch exact JSON Schema definitions for creating/updating resources. Supports deep drilling via path parameter.
harness_list List resources of a given type with filtering, search, and pagination.
harness_get Get a single resource by its identifier.
harness_create Create a new resource. Supports inline and remote (Git-backed) pipelines. Prompts for user confirmation via elicitation.
harness_update Update an existing resource. Supports inline and remote (Git-backed) pipelines. Prompts for user confirmation via elicitation.
harness_delete Delete a resource. Prompts for user confirmation via elicitation. Destructive.
harness_execute Execute an action on a resource (run/retry pipeline, import pipeline from Git, toggle flag, sync app). Prompts for user confirmation via elicitation. For pipeline runs, use the runtime-input workflow below (supports branch/tag/pr_number/commit_sha shorthand expansion).
harness_search Search across multiple resource types in parallel with a single query.
harness_diagnose Diagnose pipeline, connector, delegate, and gitops_application resources (aliases: execution -> pipeline, gitops_app -> gitops_application). For pipelines, returns stage/step timing and failure details; for connectors/delegates/GitOps apps, returns targeted health and troubleshooting signals.
harness_status Get a real-time project health dashboard — recent executions, failure rates, and deep links.

Tool Examples

Discover what resources are available:

{ "resource_type": "pipeline" }

List organizations in the account:

{ "resource_type": "organization" }

List projects in an organization:

{ "resource_type": "project", "org_id": "default" }

List pipelines in a project:

{ "resource_type": "pipeline", "search_term": "deploy", "size": 10 }

Get a specific service:

{ "resource_type": "service", "resource_id": "my-service-id" }

Run a pipeline:

{
  "resource_type": "pipeline",
  "action": "run",
  "resource_id": "my-pipeline",
  "inputs": { "tag": "v1.2.3" }
}

Toggle a feature flag:

{
  "resource_type": "feature_flag",
  "action": "toggle",
  "resource_id": "new_checkout_flow",
  "enable": true,
  "environment": "production"
}

Search across all resource types:

{ "query": "payment-service" }

Diagnose an execution by ID (summary mode — default):

{ "execution_id": "abc123XYZ" }

Diagnose from a Harness URL:

{ "url": "https://app.harness.io/ng/account/.../pipelines/myPipeline/executions/abc123XYZ/pipeline" }

Diagnose connector connectivity:

{ "resource_type": "connector", "resource_id": "my_github_connector" }

Diagnose delegate health:

{ "resource_type": "delegate", "resource_id": "delegate-us-east-1" }

Diagnose a GitOps application (with options):

{
  "resource_type": "gitops_application",
  "resource_id": "checkout-app",
  "options": { "agent_id": "gitops-agent-1" }
}

Get the latest execution report for a pipeline:

{ "pipeline_id": "my-pipeline" }

Full diagnostic mode with YAML and failed step logs:

{ "execution_id": "abc123XYZ", "summary": false }

Summary mode with logs enabled (best of both):

{ "execution_id": "abc123XYZ", "include_logs": true }

Get project health status:

{ "org_id": "default", "project_id": "my-project", "limit": 5 }

Pipeline Run Workflow (Recommended)

Use this sequence to reduce execution-time input errors:

  1. Discover required runtime inputs
    • harness_get(resource_type="runtime_input_template", resource_id="<pipeline_id>")
    • The returned template shows <+input> placeholders that need values.
  2. Choose input strategy

    • Simple variables: pass flat key-value inputs (for example {"branch":"main","env":"prod"}).

    • Complex/structural inputs: use input_set_ids (CI codebase/build blocks and nested template inputs are best handled this way).

    • CI codebase shorthand keys (pipeline run only):

      Shorthand key Expanded structure
      branch build.type=branch, build.spec.branch=<value>
      tag build.type=tag, build.spec.tag=<value>
      pr_number build.type=PR, build.spec.number=<value>
      commit_sha build.type=commitSha, build.spec.commitSha=<value>

    • Constraint: shorthand expansion is skipped when inputs.build is already present (explicit build wins).

  3. Execute the run
    • harness_execute(resource_type="pipeline", action="run", resource_id="<pipeline_id>", ...)
  4. Optional: combine both
    • Use input_set_ids for the base shape and inputs for simple overrides.

If required fields are unresolved, the tool returns a pre-flight error with expected keys and suggested input sets. You can inspect available shorthand mappings with harness_describe(resource_type="pipeline") (executeActions.run.inputShorthands).

Ask the AI DevOps Agent to create a pipeline:

{
  "prompt": "Create a pipeline that builds a Go app with Docker and deploys to Kubernetes",
  "action": "CREATE_PIPELINE"
}

Update a service via natural language:

{
  "prompt": "Add a sidecar container for logging",
  "action": "UPDATE_SERVICE",
  "conversation_id": "prev-conversation-id",
  "context": [{ "type": "yaml", "payload": "<existing service YAML>" }]
}

Pipeline Storage Modes

Harness pipelines can be stored in three ways:

Mode Description When to use
Inline Pipeline YAML stored in Harness Default. Simplest setup, no Git required.
Remote (External Git) Pipeline YAML stored in GitHub, GitLab, Bitbucket, etc. Teams using Git-backed pipeline-as-code with an external provider.
Remote (Harness Code) Pipeline YAML stored in a Harness Code repository Teams using Harness's built-in Git hosting.

Create an inline pipeline (default):

// harness_create
{
  "resource_type": "pipeline",
  "body": {
    "yamlPipeline": "pipeline:\n  name: My Pipeline\n  identifier: my_pipeline\n  stages:\n    - stage:\n        name: Build\n        type: CI\n        spec:\n          execution:\n            steps:\n              - step:\n                  type: Run\n                  name: Echo\n                  spec:\n                    command: echo hello"
  }
}

Create a remote pipeline (External Git — e.g. GitHub):

// harness_create
{
  "resource_type": "pipeline",
  "body": {
    "yamlPipeline": "pipeline:\n  name: Deploy Service\n  identifier: deploy_service\n  stages: []"
  },
  "params": {
    "store_type": "REMOTE",
    "connector_ref": "my_github_connector",
    "repo_name": "my-repo",
    "branch": "main",
    "file_path": ".harness/deploy-service.yaml",
    "commit_msg": "Add deploy pipeline via MCP"
  }
}

Create a remote pipeline (Harness Code — no connector needed):

// harness_create
{
  "resource_type": "pipeline",
  "body": {
    "yamlPipeline": "pipeline:\n  name: Build App\n  identifier: build_app\n  stages: []"
  },
  "params": {
    "store_type": "REMOTE",
    "is_harness_code_repo": true,
    "repo_name": "product-management",
    "branch": "main",
    "file_path": ".harness/build-app.yaml",
    "commit_msg": "Add build pipeline via MCP"
  }
}

Update a remote pipeline:

// harness_update
{
  "resource_type": "pipeline",
  "resource_id": "deploy_service",
  "body": {
    "yamlPipeline": "pipeline:\n  name: Deploy Service\n  identifier: deploy_service\n  stages:\n    - stage:\n        name: Deploy\n        type: Deployment"
  },
  "params": {
    "store_type": "REMOTE",
    "connector_ref": "my_github_connector",
    "repo_name": "my-repo",
    "branch": "main",
    "file_path": ".harness/deploy-service.yaml",
    "commit_msg": "Update deploy pipeline via MCP",
    "last_object_id": "abc123",
    "last_commit_id": "def456"
  }
}

Import a pipeline from an external Git repo:

// harness_execute
{
  "resource_type": "pipeline",
  "action": "import",
  "params": {
    "connector_ref": "my_github_connector",
    "repo_name": "my-repo",
    "branch": "main",
    "file_path": ".harness/existing-pipeline.yaml"
  },
  "body": {
    "pipeline_name": "Existing Pipeline",
    "pipeline_description": "Imported from GitHub"
  }
}

Import a pipeline from a Harness Code repo:

// harness_execute
{
  "resource_type": "pipeline",
  "action": "import",
  "params": {
    "is_harness_code_repo": true,
    "repo_name": "product-management",
    "branch": "main",
    "file_path": ".harness/existing-pipeline.yaml"
  },
  "body": {
    "pipeline_name": "Existing Pipeline"
  }
}

Create a connector:

{
  "resource_type": "connector",
  "body": { "connector": { "name": "My Docker Hub", "identifier": "my_docker", "type": "DockerRegistry" } }
}

Delete a trigger:

{
  "resource_type": "trigger",
  "resource_id": "nightly-trigger",
  "pipeline_id": "my-pipeline"
}

Resource Types

137 resource types organized across 29 toolsets. Each resource type supports a subset of CRUD operations and optional execute actions.

Platform

Resource Type List Get Create Update Delete Execute Actions
organization x x x x x
project x x x x x

Pipelines

Resource Type List Get Create Update Delete Execute Actions
pipeline x x x x x run, retry
execution x x interrupt
trigger x x x x x
pipeline_summary x
input_set x x
runtime_input_template x
approval_instance x approve, reject

Services

Resource Type List Get Create Update Delete Execute Actions
service x x x x x

Environments

Resource Type List Get Create Update Delete Execute Actions
environment x x x x x move_configs

Connectors

Resource Type List Get Create Update Delete Execute Actions
connector x x x x x test_connection
connector_catalogue x

Infrastructure

Resource Type List Get Create Update Delete Execute Actions
infrastructure x x x x x move_configs

Secrets

Resource Type List Get Create Update Delete Execute Actions
secret x x

Execution Logs

Resource Type List Get Create Update Delete Execute Actions
execution_log x

Audit Trail

Resource Type List Get Create Update Delete Execute Actions
audit_event x x

Delegates

Resource Type List Get Create Update Delete Execute Actions
delegate x
delegate_token x x x x revoke, get_delegates

Code Repositories

Resource Type List Get Create Update Delete Execute Actions
repository x x x x
branch x x x x
commit x x diff, diff_stats
file_content x blame
tag x x x
repo_rule x x
space_rule x x

Artifact Registries

Resource Type List Get Create Update Delete Execute Actions
registry x x
artifact x
artifact_version x
artifact_file x

Templates

Resource Type List Get Create Update Delete Execute Actions
template x x x x x

Dashboards

Resource Type List Get Create Update Delete Execute Actions
dashboard x x
dashboard_data x

Internal Developer Portal (IDP)

Resource Type List Get Create Update Delete Execute Actions
idp_entity x x
scorecard x x
scorecard_check x x
scorecard_stats x
scorecard_check_stats x
idp_score x x
idp_workflow x execute
idp_tech_doc x

Pull Requests

Resource Type List Get Create Update Delete Execute Actions
pull_request x x x x merge
pr_reviewer x x submit_review
pr_comment x x
pr_check x
pr_activity x

Feature Flags

Resource Type List Get Create Update Delete Execute Actions
fme_workspace x
fme_environment x
fme_feature_flag x x x x x kill, restore, archive, unarchive
fme_feature_flag_definition x
fme_rollout_status x
fme_rule_based_segment x x x x
fme_rule_based_segment_definition x x enable, disable, change_request
feature_flag x x x x toggle

FME (Split.io) resources — fme_* resources use the Split.io API (api.split.io) and are scoped by workspace ID rather than org/project. Auth uses HARNESS_API_KEY as a Bearer token. fme_feature_flag supports full lifecycle management: create (requires traffic_type_id), list, get, update metadata, delete, and kill/restore/archive/unarchive execute actions. fme_rule_based_segment provides CRUD for targeting segments, while fme_rule_based_segment_definition manages environment-specific segment rules with enable/disable and change request approval flows. Use feature_flag for the Harness CF admin API which supports environment-specific definitions, create, delete, and toggle.

GitOps

Resource Type List Get Create Update Delete Execute Actions
gitops_agent x x
gitops_application x x sync
gitops_cluster x x
gitops_repository x x
gitops_applicationset x x
gitops_repo_credential x x
gitops_app_event x
gitops_pod_log x
gitops_managed_resource x
gitops_resource_action x
gitops_dashboard x
gitops_app_resource_tree x

Chaos Engineering

Resource Type List Get Create Update Delete Execute Actions
chaos_experiment x x run
chaos_probe x x enable, verify
chaos_experiment_template x create_from_template
chaos_infrastructure x
chaos_experiment_variable x
chaos_experiment_run x x
chaos_loadtest x x x x run, stop
chaos_k8s_infrastructure x x check_health
chaos_hub x x
chaos_fault x x
chaos_network_map x x
chaos_guard_condition x x
chaos_guard_rule x x
chaos_recommendation x x
chaos_risk x x

Cloud Cost Management (CCM)

Resource Type List Get Create Update Delete Execute Actions
cost_perspective x x x x x
cost_breakdown x
cost_timeseries x
cost_summary x x
cost_recommendation x x update_state, override_savings, create_jira_ticket, create_snow_ticket
cost_anomaly x
cost_anomaly_summary x
cost_category x x
cost_account_overview x
cost_filter_value x
cost_recommendation_stats x
cost_recommendation_detail x
cost_commitment x

Software Engineering Insights (SEI)

SEI resources are consolidated for token efficiency. Use metric or aspect params for DORA, team/org-tree details, and AI insights.

Resource Type List Get Create Update Delete Execute Actions
sei_metric x
sei_productivity_metric x
sei_dora_metric x Pass metric: deployment_frequency, change_failure_rate, mttr, lead_time, or *_drilldown
sei_team x x
sei_team_detail x Pass aspect: integrations, developers, integration_filters
sei_org_tree x x
sei_org_tree_detail x x Pass aspect: efficiency_profile, productivity_profile, business_alignment_profile, integrations, teams
sei_business_alignment x x Pass aspect: feature_metrics, feature_summary, drilldown for get
sei_ai_usage x x Pass aspect: metrics, breakdown, summary, top_languages
sei_ai_adoption x x Pass aspect: metrics, breakdown, summary
sei_ai_impact x Pass aspect: pr_velocity, rework
sei_ai_raw_metric x

Software Supply Chain Assurance (SCS)

Resource Type List Get Create Update Delete Execute Actions
scs_artifact_source x
artifact_security x x
scs_artifact_component x
scs_artifact_remediation x
scs_chain_of_custody x
scs_compliance_result x
code_repo_security x x
scs_sbom x

Security Testing Orchestration (STO)

Resource Type List Get Create Update Delete Execute Actions
security_issue x
security_issue_filter x
security_exemption x approve, reject, promote

Access Control

Resource Type List Get Create Update Delete Execute Actions
user x x
user_group x x x x
service_account x x x x
role x x x x
role_assignment x x
resource_group x x x x
permission x

Governance

Resource Type List Get Create Update Delete Execute Actions
policy x x x x x
policy_set x x x x x
policy_evaluation x x

Deployment Freeze

Resource Type List Get Create Update Delete Execute Actions
freeze_window x x x x x toggle_status
global_freeze x manage

Service Overrides

Resource Type List Get Create Update Delete Execute Actions
service_override x x x x x

Settings

Resource Type List Get Create Update Delete Execute Actions
setting x

Visualizations

Inline PNG chart visualizations rendered from Harness data. These are metadata-only resource types with no API operations — they exist so the LLM can discover available chart types via harness_describe. Use include_visual=true on supported tools (harness_diagnose, harness_list, harness_status) to generate charts.

Resource Type Description How to Generate
visual_timeline Gantt chart of pipeline stage execution over time harness_diagnose with visual_type: "timeline"
visual_stage_flow DAG flowchart of pipeline stages and steps harness_diagnose with visual_type: "flow"
visual_health_dashboard Project health overview with status indicators harness_status with include_visual: true
visual_pie_chart Donut chart of execution status breakdown harness_list with visual_type: "pie"
visual_bar_chart Bar chart of execution counts by pipeline harness_list with visual_type: "bar"
visual_timeseries Daily execution trend over 30 days harness_list with visual_type: "timeseries"
visual_architecture Pipeline YAML architecture diagram (stages → steps) harness_diagnose with visual_type: "architecture"

MCP Prompts

DevOps

Prompt Description Parameters
build-deploy-app End-to-end CI/CD workflow: scan a git repo, generate CI pipeline (build & push Docker image), discover or generate K8s manifests, create CD pipeline, and deploy — with auto-retry on CI failures (up to 5 attempts) and CD failures (up to 3 attempts with user permission). On exhausted retries, provides Harness UI deep links to all created resources for manual investigation. repoUrl (required), imageName (required), projectId (optional), namespace (optional)
debug-pipeline-failure Analyze a failed execution: accepts an execution ID, pipeline ID, or Harness URL. Gets stage/step breakdown, failure details, delegate info, and failed step logs via harness_diagnose, then provides root cause analysis and suggested fixes. Automatically follows chained pipeline failures. executionId (optional), projectId (optional)
create-pipeline Generate a new pipeline YAML from natural language requirements, reviewing existing resources for context description (required), projectId (optional)
onboard-service Walk through onboarding a new service with environments and a deployment pipeline serviceName (required), projectId (optional)
dora-metrics-review Review DORA metrics (deployment frequency, change failure rate, MTTR, lead time) with Elite/High/Medium/Low classification and improvement recommendations teamRefId (optional), dateStart (optional), dateEnd (optional)
setup-gitops-application Guide through onboarding a GitOps application — verify agent, cluster, repo, and create the application agentId (required), projectId (optional)
chaos-resilience-test Design a chaos experiment to test service resilience with fault injection, probes, and expected outcomes serviceName (required), projectId (optional)
feature-flag-rollout Plan and execute a progressive feature flag rollout across environments with safety gates flagIdentifier (required), projectId (optional)
migrate-pipeline-to-template Analyze an existing pipeline and extract reusable stage/step templates from it pipelineId (required), projectId (optional)
delegate-health-check Check delegate connectivity, health, token status, and troubleshoot infrastructure issues projectId (optional)
developer-portal-scorecard Review IDP scorecards for services and identify gaps to improve developer experience projectId (optional)
pending-approvals Find pipeline executions waiting for approval, show details, and offer to approve or reject projectId (optional), orgId (optional), pipelineId (optional)

FinOps

Prompt Description Parameters
optimize-costs Analyze cloud cost data, surface recommendations and anomalies, prioritized by potential savings projectId (optional)
cloud-cost-breakdown Deep-dive into cloud costs by service, environment, or cluster with trend analysis and anomaly detection perspectiveId (optional), projectId (optional)
commitment-utilization-review Analyze reserved instance and savings plan utilization to find waste and optimize commitments projectId (optional)
cost-anomaly-investigation Investigate cost anomalies — determine root cause, impacted resources, and remediation projectId (optional)
rightsizing-recommendations Review and prioritize rightsizing recommendations, optionally create Jira or ServiceNow tickets projectId (optional), minSavings (optional)

DevSecOps

Prompt Description Parameters
security-review Review security issues across Harness resources and suggest remediations by severity projectId (optional), severity (optional, default: critical,high)
vulnerability-triage Triage security vulnerabilities across pipelines and artifacts, prioritize by severity and exploitability projectId (optional), severity (optional)
sbom-compliance-check Audit SBOM and compliance posture for artifacts — license risks, policy violations, component vulnerabilities artifactId (optional), projectId (optional)
supply-chain-audit End-to-end software supply chain security audit — provenance, chain of custody, policy compliance projectId (optional)
security-exemption-review Review pending security exemptions and make batch approval or rejection decisions projectId (optional)
access-control-audit Audit user permissions, over-privileged accounts, and role assignments to enforce least-privilege projectId (optional), orgId (optional)

Harness Code

Prompt Description Parameters
code-review Review a pull request — analyze diff, commits, checks, and comments to provide structured feedback on bugs, security, performance, and style repoId (required), prNumber (required), projectId (optional)
pr-summary Auto-generate a PR title and description from the commit history and diff of a branch repoId (required), sourceBranch (required), targetBranch (optional, default: main), projectId (optional)
branch-cleanup Analyze branches in a repository and recommend stale or merged branches to delete repoId (required), projectId (optional)

MCP Resources

Resource URI Description MIME Type
pipeline:///{pipelineId} Pipeline YAML definition application/x-yaml
pipeline:///{orgId}/{projectId}/{pipelineId} Pipeline YAML (with explicit scope) application/x-yaml
executions:///recent Last 10 pipeline execution summaries application/json
schema:///pipeline Harness pipeline JSON Schema application/schema+json
schema:///template Harness template JSON Schema application/schema+json
schema:///trigger Harness trigger JSON Schema application/schema+json

Toolset Filtering

By default, all 29 toolsets (and their 137 resource types) are enabled. Use HARNESS_TOOLSETS to expose only the toolsets you need. This reduces the resource types the LLM sees, improving tool selection accuracy.

# Only expose pipelines, services, and connectors
HARNESS_TOOLSETS=pipelines,services,connectors

Available toolset names:

Toolset Resource Types
platform organization, project
pipelines pipeline, execution, trigger, pipeline_summary, input_set, approval_instance
services service
environments environment
connectors connector, connector_catalogue
infrastructure infrastructure
secrets secret
logs execution_log
audit audit_event
delegates delegate, delegate_token
repositories repository, branch, commit, file_content, tag, repo_rule, space_rule
registries registry, artifact, artifact_version, artifact_file
templates template
dashboards dashboard, dashboard_data
idp idp_entity, scorecard, scorecard_check, scorecard_stats, scorecard_check_stats, idp_score, idp_workflow, idp_tech_doc
pull-requests pull_request, pr_reviewer, pr_comment, pr_check, pr_activity
feature-flags fme_workspace, fme_environment, fme_feature_flag, fme_feature_flag_definition, fme_rollout_status, fme_rule_based_segment, fme_rule_based_segment_definition, feature_flag
gitops gitops_agent, gitops_application, gitops_cluster, gitops_repository, gitops_applicationset, gitops_repo_credential, gitops_app_event, gitops_pod_log, gitops_managed_resource, gitops_resource_action, gitops_dashboard, gitops_app_resource_tree
chaos chaos_experiment, chaos_probe, chaos_experiment_template, chaos_infrastructure, chaos_experiment_variable, chaos_experiment_run, chaos_loadtest, chaos_k8s_infrastructure, chaos_hub, chaos_fault, chaos_network_map, chaos_guard_condition, chaos_guard_rule, chaos_recommendation, chaos_risk
ccm cost_perspective, cost_breakdown, cost_timeseries, cost_summary, cost_recommendation, cost_anomaly, cost_anomaly_summary, cost_category, cost_account_overview, cost_filter_value, cost_recommendation_stats, cost_recommendation_detail, cost_commitment
sei sei_metric, sei_productivity_metric, sei_dora_metric, sei_team, sei_team_detail, sei_org_tree, sei_org_tree_detail, sei_business_alignment, sei_ai_usage, sei_ai_adoption, sei_ai_impact, sei_ai_raw_metric
scs scs_artifact_source, artifact_security, scs_artifact_component, scs_artifact_remediation, scs_chain_of_custody, scs_compliance_result, code_repo_security, scs_sbom
sto security_issue, security_issue_filter, security_exemption
access_control user, user_group, service_account, role, role_assignment, resource_group, permission
governance policy, policy_set, policy_evaluation
freeze freeze_window, global_freeze
overrides service_override
settings setting
visualizations visual_timeline, visual_stage_flow, visual_health_dashboard, visual_pie_chart, visual_bar_chart, visual_timeseries, visual_architecture

Architecture

                 +------------------+
                 |   AI Agent       |
                 |  (Claude, etc.)  |
                 +--------+---------+
                          |  MCP (stdio or HTTP)
                 +--------v---------+
                |    MCP Server     |
                | 10 Generic Tools  |
                 +--------+---------+
                          |
                 +--------v---------+
                |    Registry       |  <-- Declarative resource definitions
                |  29 Toolsets      |      (data files, not code)
                |  137 Resource Types|
                 +--------+---------+
                          |
                 +--------v---------+
                 |  HarnessClient    |  <-- Auth, retry, rate limiting
                 +--------+---------+
                          |  HTTPS
                 +--------v---------+
                 |  Harness REST API |
                 +-------------------+

How It Works

  1. Tools are generic verbs: harness_list, harness_get, etc. They accept a resource_type parameter that routes to the correct API endpoint.

  2. The Registry maps each resource_type to a ResourceDefinition — a declarative data structure specifying the HTTP method, URL path, path/query parameter mappings, and response extraction logic.

  3. Dispatch resolves the resource definition, builds the HTTP request (path substitution, query params, scope injection), calls the Harness API through HarnessClient, and extracts the relevant response data.

  4. Toolset filtering (HARNESS_TOOLSETS) controls which resource definitions are loaded into the registry at startup.

  5. Deep links are automatically appended to responses, providing direct Harness UI URLs for every resource.

  6. Compact mode strips verbose metadata from list results, keeping only actionable fields (identity, status, type, timestamps, deep links) to minimize token usage.

Adding a New Resource Type

Create a new file in src/registry/toolsets/ or add a resource to an existing toolset:

// src/registry/toolsets/my-module.ts
import type { ToolsetDefinition } from "../types.js";

export const myModuleToolset: ToolsetDefinition = {
  name: "my-module",
  displayName: "My Module",
  description: "Description of the module",
  resources: [
    {
      resourceType: "my_resource",
      displayName: "My Resource",
      description: "What this resource represents",
      toolset: "my-module",
      scope: "project",                    // "project" | "org" | "account"
      identifierFields: ["resource_id"],
      listFilterFields: ["search_term"],
      operations: {
        list: {
          method: "GET",
          path: "/my-module/api/resources",
          queryParams: { search_term: "search", page: "page", size: "size" },
          responseExtractor: (raw) => raw,
          description: "List resources",
        },
        get: {
          method: "GET",
          path: "/my-module/api/resources/{resourceId}",
          pathParams: { resource_id: "resourceId" },
          responseExtractor: (raw) => raw,
          description: "Get resource details",
        },
      },
    },
  ],
};

Then import it in src/registry/index.ts and add it to the ALL_TOOLSETS array. No changes needed to any tool files.

Development

# Build
pnpm build

# Watch mode
pnpm dev

# Type check
pnpm typecheck

# Run tests
pnpm test

# Watch tests
pnpm test:watch

# Interactive MCP Inspector
pnpm inspect

Project Structure

src/
  index.ts                          # Entrypoint, transport setup
  config.ts                         # Env var validation (Zod)
  client/
    harness-client.ts               # HTTP client (auth, retry, rate limiting)
    types.ts                        # Shared API types
  registry/
    index.ts                        # Registry class + dispatch logic
    types.ts                        # ResourceDefinition, ToolsetDefinition, etc.
    toolsets/                        # One file per toolset (declarative data)
      platform.ts
      pipelines.ts
      services.ts
      ccm.ts
      access-control.ts
      ...
  tools/                            # 10 generic MCP tools
    harness-list.ts
    harness-get.ts
    harness-create.ts
    harness-update.ts
    harness-delete.ts
    harness-execute.ts
    harness-search.ts
    harness-diagnose.ts
    harness-describe.ts
    harness-status.ts

  resources/                        # MCP resource providers
    pipeline-yaml.ts
    execution-summary.ts
  prompts/                          # MCP prompt templates
    build-deploy-app.ts             # DevOps: end-to-end build & deploy workflow
    debug-pipeline.ts               # DevOps: debug failed executions
    create-pipeline.ts              # DevOps: generate pipeline from requirements
    onboard-service.ts              # DevOps: onboard new service
    dora-metrics.ts                 # DevOps: DORA metrics review
    setup-gitops.ts                 # DevOps: GitOps application setup
    chaos-resilience.ts             # DevOps: chaos experiment design
    feature-flag-rollout.ts         # DevOps: progressive flag rollout
    migrate-to-template.ts          # DevOps: extract templates from pipeline
    delegate-health.ts              # DevOps: delegate health check
    developer-scorecard.ts          # DevOps: IDP scorecard review
    optimize-costs.ts               # FinOps: cost optimization
    cloud-cost-breakdown.ts         # FinOps: cost deep-dive
    commitment-utilization.ts       # FinOps: RI/savings plan analysis
    cost-anomaly.ts                 # FinOps: anomaly investigation
    rightsizing.ts                  # FinOps: rightsizing recommendations
    security-review.ts              # DevSecOps: security issue review
    vulnerability-triage.ts         # DevSecOps: vulnerability triage
    sbom-compliance.ts              # DevSecOps: SBOM compliance audit
    supply-chain-audit.ts           # DevSecOps: supply chain audit
    exemption-review.ts             # DevSecOps: exemption approval
    access-control-audit.ts         # DevSecOps: access control audit
    code-review.ts                  # Harness Code: PR code review
    pr-summary.ts                   # Harness Code: auto-generate PR summary
    branch-cleanup.ts               # Harness Code: stale branch cleanup
    pending-approvals.ts            # Approvals: find and act on pending approvals
  utils/
    cli.ts                          # CLI arg parsing (transport, port)
    errors.ts                       # Error normalization
    logger.ts                       # stderr-only logger
    progress.ts                     # MCP progress & logging notifications
    rate-limiter.ts                 # Client-side rate limiting
    deep-links.ts                   # Harness UI deep link builder
    response-formatter.ts           # Consistent MCP response formatting
    compact.ts                      # Compact list output for token efficiency
tests/
  config.test.ts                    # Config schema validation tests
  utils/
    response-formatter.test.ts
    deep-links.test.ts
    errors.test.ts
  registry/
    registry.test.ts                # Registry loading, filtering, dispatch tests

Elicitation

Write tools (harness_create, harness_update, harness_delete, harness_execute) use MCP elicitation to prompt the user for confirmation before making changes. This gives real human-in-the-loop approval — the user sees what's about to happen and accepts or declines.

How it works:

  1. The LLM calls a write tool (e.g. harness_create with a pipeline body)
  2. The server sends an elicitation request to the client with a summary of the operation
  3. The user sees the details and clicks Accept or Decline
  4. If accepted, the operation proceeds. If declined, it's blocked and the LLM is told

Client support:

Client Elicitation Support
Cursor Yes
VS Code (Copilot) Yes
Claude Desktop Not yet
Windsurf Not yet
MCP Inspector Yes

Elicitation behavior varies by operation severity when client support is missing:
For clients that don't support elicitation:

  • harness_create, harness_update, and harness_execute proceed without a dialog (best effort).
  • Destructive operations are blocked if confirmation cannot be obtained (harness_delete).

If elicitation fails at runtime, the same rules apply: non-destructive writes continue, destructive writes are blocked.

Skipping Elicitation for Autonomous Workflows

For fully autonomous agent workflows (CI/CD bots, headless agents, batch automation), elicitation prompts can be disabled entirely:

HARNESS_SKIP_ELICITATION=true

Or in your MCP client config:

{
  "mcpServers": {
    "harness": {
      "command": "npx",
      "args": ["harness-mcp-v2"],
      "env": {
        "HARNESS_API_KEY": "pat.xxx.xxx.xxx",
        "HARNESS_SKIP_ELICITATION": "true"
      }
    }
  }
}

When enabled, all write and delete operations proceed without user confirmation — including destructive operations like harness_delete. Use with caution and consider pairing with HARNESS_TOOLSETS to restrict which resource types are available.

Safety

  • Secrets are never exposed. The secret resource type returns metadata only (name, type, scope) — secret values are never included in any response.

  • Write operations use elicitation when available. harness_create, harness_update, harness_delete, and harness_execute attempt MCP elicitation before proceeding (see Elicitation).

  • Destructive writes fail closed. If confirmation cannot be obtained, harness_delete is blocked instead of executing blindly. Override with HARNESS_SKIP_ELICITATION=true for autonomous workflows.

  • CORS restricted to same-origin. The HTTP transport only allows same-origin requests, preventing CSRF attacks from malicious websites targeting the MCP server on localhost.

  • HTTP rate limiting. The HTTP transport enforces 60 requests per minute per IP to prevent request flooding.

  • API rate limiting. The Harness API client enforces a 10 requests/second limit to avoid hitting upstream rate limits.

  • Pagination bounds enforced. List queries are capped at 10,000 items total and 100 per page to prevent memory exhaustion.

  • Retries with backoff. Transient failures (HTTP 429, 5xx) are retried with exponential backoff and jitter.

  • Localhost binding. The HTTP transport binds to 127.0.0.1 by default — not accessible from the network.

  • No stdout logging. All logs go to stderr to avoid corrupting the stdio JSON-RPC transport.

Complementary Skills

The Harness MCP server pairs well with Harness Skills — a collection of ready-made Claude Code skills (slash commands) designed for common Harness workflows. Install them alongside this MCP server to get high-level automation like /deploy, /rollback, /triage, and more without writing custom prompts.

Troubleshooting & Common Pitfalls

Symptom Likely Cause What to Do
HARNESS_ACCOUNT_ID is required when the API key is not a PAT... API key is not in PAT format (pat.<accountId>.<tokenId>.<secret>) so account ID cannot be inferred Set HARNESS_ACCOUNT_ID explicitly
Unknown transport: "..." on startup Unsupported CLI transport arg Use stdio or http only
Invalid HARNESS_TOOLSETS: ... on startup One or more toolset names are not recognized Use only names from Toolset Filtering (exact match)
HTTP mcp-session-id header is required... A session request was sent without session header Send initialize first, then include mcp-session-id on POST/GET/DELETE /mcp
HTTP Session not found... Session expired (30 min idle TTL) or already closed Re-run initialize to create a new session, then retry with new header
HTTP 405 Method Not Allowed on /mcp Unsupported method for MCP endpoint Use POST, GET, DELETE, or OPTIONS only
HTTP Invalid request Invalid JSON body or request body exceeded HARNESS_MAX_BODY_SIZE_MB Validate JSON payload size/shape; increase HARNESS_MAX_BODY_SIZE_MB if needed
Unknown resource_type "..." from tools Resource type is misspelled or filtered out via HARNESS_TOOLSETS Call harness_describe (with optional search_term) to discover valid types
Missing required field "... for path parameter ..." A project/org scoped call is missing identifiers Set HARNESS_DEFAULT_ORG_ID/HARNESS_DEFAULT_PROJECT_ID or pass org_id/project_id per tool call
Read-only mode is enabled ... operations are not allowed HARNESS_READ_ONLY=true blocks create/update/delete/execute Set HARNESS_READ_ONLY=false if write operations are intended
Pipeline run fails pre-flight with unresolved required inputs Provided inputs did not cover required runtime placeholders Fetch runtime_input_template, supply missing simple keys, or use input_set_ids for structural inputs
Pipeline CI shorthand (branch, tag, pr_number, commit_sha) did not apply inputs.build was already provided, so shorthand expansion was intentionally skipped Remove inputs.build to use shorthand expansion, or keep full explicit build structure
Operation declined by user User declined the elicitation confirmation dialog The user chose not to proceed — verify the operation details and retry if intended
body.template_yaml (or body.yaml) is required for template create/update Template APIs expect full YAML payload Provide full template_yaml string in body; for deletes, pass version_label to delete one version (omit to delete all versions)
HARNESS_BASE_URL must use HTTPS on startup HARNESS_BASE_URL is set to an HTTP URL Use HTTPS, or set HARNESS_ALLOW_HTTP=true for local development

License

Apache 2.0

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