mcpc

mcp
Security Audit
Warn
Health Pass
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 87 GitHub stars
Code Warn
  • process.env — Environment variable access in packages/acp-ai-provider/examples/claude-code-stream-text.ts
  • process.env — Environment variable access in packages/acp-ai-provider/examples/codebuddy-stream-text.ts
  • process.env — Environment variable access in packages/acp-ai-provider/examples/codex-stream-text.ts
  • process.env — Environment variable access in packages/acp-ai-provider/examples/gemini-stream-text.ts
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This tool provides an SDK for building agentic MCP servers by composing and orchestrating existing MCP tools, allowing developers to create multi-agent systems and fine-tune tool behaviors.

Security Assessment
Overall risk: Low. The automated scan found no hardcoded secrets, dangerous permissions, or shell execution commands. The only warnings stem from `process.env` access within example files (`examples/`), which is a standard and expected method for managing API keys (like those for Claude, Gemini, or Codex) rather than a security vulnerability. The tool acts as an orchestrator and relies on underlying AI providers, meaning it makes standard network requests to those external APIs during operation.

Quality Assessment
The project demonstrates strong health indicators. It is actively maintained (last updated today) and uses the highly permissive MIT license. It features clear documentation and an accessible quick-start guide. While the repository has a modest 87 stars, this indicates a legitimate and growing early-stage community interest rather than a neglected project.

Verdict
Safe to use.
SUMMARY

Build agentic-MCP servers by composing existing MCP tools.

README.md

MCPC

JSR
npm

Build agentic MCP servers by composing existing MCP tools.

MCPC is the SDK for building agentic MCP (Model Context Protocol) Servers. You
can use it to:

  1. Create Powerful Agentic MCP Tools: Simply describe your vision in text
    and reference tools from the
    expanding MCP community.
    As standard MCP tools, your agents work everywhere and collaborate
    seamlessly.
  2. Fine-Tune Existing Tools: Flexibly modify existing tool descriptions and
    parameters, or wrap and filter results to precisely adapt them to your
    specific business scenarios.
  3. Build Multi-Agent Systems: By defining each agent as a MCP tool, you can
    compose and orchestrate them to construct sophisticated, collaborative
    multi-agent systems.

Key Features

  • Portability and agent interoperability: Build once, run everywhere as MCP
    tools - agents work across all MCP clients and can discover and collaborate
    with each other through standard MCP interfaces
  • Simple composition and fine-tuning: Compose MCP servers as building
    blocks, select and customize tools, or modify their descriptions and
    parameters
  • Logging and tracing: Built-in MCP logging and OpenTelemetry tracing
    support
  • Skills support: Define domain-specific knowledge following the
    Agent Skills specification - deploy to production,
    share via MCP, and declare tool dependencies
  • Flexible execution modes: Multiple specialized modes to fit different
    scenarios - interactive agent (agentic), AI SDK sampling (ai_sampling), AI
    ACP mode (ai_acp), and secure code execution
    (code_execution) - each with dedicated
    implementations

Quick Start

Three Ways to Get Started

1. Use the Website (Fastest)

Visit mcpc.tech to browse servers from the official MCP
registry, discover tools, and generate ready-to-use agents.

2. Use the Agent (Interactive)

Let AI help you discover servers and build agents:

Add to your MCP client:

{
  "mcpServers": {
    "mcpc-builder-agent": {
      "command": "npx",
      "args": ["-y", "@mcpc-tech/builder", "mcpc-builder-agent"]
    }
  }
}

3. Write Code (Full Control)

Use the SDK directly for complete customization. See examples below.


Installation

# npm (from npm registry)
npm install @mcpc-tech/core
# npm (from jsr)
npx jsr add @mcpc/core

# deno
deno add jsr:@mcpc/core

# pnpm (from npm registry)
pnpm add @mcpc-tech/core
# pnpm (from jsr)
pnpm add jsr:@mcpc/core

Or run directly with the CLI (no installation required):

# Run with remote configuration
npx -y @mcpc-tech/cli --config-url \
  "https://raw.githubusercontent.com/mcpc-tech/mcpc/main/packages/cli/examples/configs/codex-fork.json"

Examples: Create a Simple Codex/Claude Code Fork

import { mcpc } from "@mcpc/core";

const server = await mcpc(
  [{ name: "coding-agent", version: "0.1.0" }, { capabilities: { tools: {} } }],
  [{
    name: "coding-agent",
    description: `
      You are a coding assistant with advanced capabilities.

      Your capabilities include:
      - Reading and writing files
      - Executing terminal commands to build, test, and run projects
      - Interacting with GitHub to create pull requests and manage issues

      Available tools:
      <tool name="desktop-commander.execute_command" />
      <tool name="desktop-commander.read_file" />
      <tool name="desktop-commander.write_file" />
      <tool name="github.create_pull_request" />
    `,
    deps: {
      mcpServers: {
        "desktop-commander": {
          command: "npx",
          args: ["-y", "@wonderwhy-er/desktop-commander@latest"],
          transportType: "stdio",
        },
        github: {
          transportType: "streamable-http",
          url: "https://api.githubcopilot.com/mcp/",
        },
      },
    },
  }],
);

Complete Example: See the full
Codex fork tutorial.

Install the MCPC Core Skill

Install the mcpc-core skill into your project using
skills.sh:

npx skills add mcpc-tech/mcpc

This installs the skill into .agent/skills/mcpc-core/, giving your agent
on-demand access to the full @mcpc/core API reference, usage patterns, plugin
guide, and gotchas.


Examples: Load Agent Skills

For complex agents where inline description becomes unwieldy, use
Agent Skills to organize domain knowledge in separate
files that are loaded on-demand.

import { createBashPlugin, createSkillsPlugin } from "@mcpc/core/plugins";

const server = await mcpc(
  [{ name: "my-agent", version: "1.0.0" }, { capabilities: { tools: {} } }],
  [{
    name: "my-agent",
    description: 'An agent with domain knowledge\n\n<tool name="bash"/>',
    plugins: [
      createSkillsPlugin({ paths: ["./skills"] }),
      createBashPlugin(),
    ],
  }],
);

Skills load domain knowledge on-demand, while bash plugin enables script
execution. For scripts in scripts/ directory, skills returns the path - use
bash tool to execute.

Complete Examples: See
14-skills-plugin.ts and
25-skills-with-bash.ts.

Examples: Progressive Manual Disclosure

For agents with detailed instructions, use the manual field to reduce initial
prompt length - the full manual is fetched on-demand via man { manual: true }:

const server = await mcpc(
  [{ name: "code-reviewer", version: "1.0.0" }, {
    capabilities: { tools: {} },
  }],
  [{
    name: "code-reviewer",
    description: "AI code reviewer for quality and security analysis.",
    manual: `Detailed review guidelines...
<tool name="desktop-commander.read_file"/>
<tool name="desktop-commander.write_file"/>

## Review Categories
1. Code Quality - readability, naming, complexity
2. Security - SQL injection, XSS, credentials
...`,
    deps: {
      mcpServers: {
        "desktop-commander": {
          command: "npx",
          args: ["-y", "@wonderwhy-er/[email protected]"],
          transportType: "stdio",
        },
      },
    },
  }],
);

Complete Example: See
21-progressive-manual.ts.

How It Works

Three simple steps:

  1. Define dependencies - List the MCP servers you want to use
  2. Write agent description - Describe what your agent does and reference
    tools
  3. Create server - Use mcpc() to build and connect your server

Execution Modes

MCPC provides multiple flexible execution modes to fit different scenarios:

Mode Description Use Case
agentic Interactive step-by-step execution Standard agent interactions
ai_sampling AI SDK sampling mode Autonomous AI SDK execution
ai_acp AI SDK ACP mode Coding agents (Claude Code)
code_execution Secure JavaScript sandbox with tool access Code generation and execution

Quick Example

// Interactive agent (default)
{ options: { mode: "agentic" } }

// Autonomous agent
{ options: { mode: "ai_sampling", samplingConfig: { maxIterations: 10 } } }

// Code execution with sandbox
import { createCodeExecutionPlugin } from "@mcpc/plugin-code-execution/plugin";
{
  plugins: [createCodeExecutionPlugin()],
  options: { mode: "code_execution" }
}

Detailed Documentation: See
Execution Modes Guide for comprehensive information
on each mode, configuration options, and best practices.

Documentation

Examples

See working examples in the examples directory or
check out the Codex fork tutorial.

Contributing

We welcome contributions! See CONTRIBUTING.md for details.

License

MIT License - see LICENSE for details.

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