Skillget

toolmaker-docs

mcp
Guvenlik Denetimi
Uyari
Health Gecti
  • License — License: Apache-2.0
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 87 GitHub stars
Code Uyari
  • Code scan incomplete — No supported source files were scanned during light audit
Permissions Gecti
  • Permissions — No dangerous permissions requested

Bu listing icin henuz AI raporu yok.

SUMMARY

Toolmaker is a lightweight software development life cycle management platform

README.md

Toolmaker Agent

An AI-augmented requirements and system-design workbench: a Go backend with an embedded React UI for managing Products, Features, Requirements, and UML/4+1-view system-design diagrams, paired with an LLM-driven conversational agent and a Model Context Protocol (MCP) server so both humans and coding agents (e.g. Claude Code) can drive the same data model.

Toolmaker Agent overview

What this is

Toolmaker Agent is a single self-contained executable: a Go REST API with a layered architecture (adapter → service → infra/dao → domain/model), SQLite storage, and the compiled React frontend embedded directly into the binary via go:embed. Open one port and you get the full workbench — no separate frontend deployment, no external database to provision.

On top of the plain CRUD workbench, it adds two AI-native ways to manipulate the same data:

  • Conversational agent — a chat panel backed by trpc-agent-go that can create/update/delete Products, Features, Requirements, and UML diagrams through natural language, using a propose → confirm flow for any write (the model proposes the action, a human confirms it before it executes).
  • MCP Server — a Model Context Protocol endpoint (official modelcontextprotocol/go-sdk) exposing 20 tools for full CRUD over the same four entities, so a coding agent like Claude Code can manage requirements directly from the terminal. Unlike the chat's propose/confirm tools, MCP tools are direct tools — they execute immediately, with no confirmation step.

Key features

  • Product / Feature / Requirement / UML CRUD, each with optimistic concurrency (a revision field, checked on every update) and full-row responses on create/update (the backend RETURNINGs the complete post-write row so the client never has to guess at server-computed fields like updatedAt or revision).

    Product management

    Feature management

    Requirement management

  • 4+1 system-design views rendered as Mermaid diagrams (flowcharts, sequence, C4/architecture, state, ER, and more), editable per Feature. Any diagram can be exported client-side as a PNG or SVG image (PNG rasterized at 3x scale for sharp output) directly from its detail panel — no server round trip.

    System design view

  • LLM chat agent with:

    • A propose-confirm tool-calling flow for every write (propose_create_* / propose_update_* / propose_delete_*), so the model never mutates data without a human in the loop.
    • SSE-streamed responses.
    • Persisted, per-conversation history, automatically summarized once it grows past a threshold (rolling summary folds everything except the most recent turns, keeping long sessions within the model's context window).
    • Pluggable multi-provider configuration — OpenAI, Anthropic, Gemini, DeepSeek, Ollama, LM Studio, Hunyuan, Moonshot AI, Qwen, GLM, MiniMax. Managed either by hand-editing a local, git-ignored config/settings.json (never committed; a documented .example template is checked in instead), or entirely from the browser via the Settings page, which lists every configured provider and lets you add, edit, activate, or delete one without touching a file.

    LLM provider settings

    LLM chat agent

  • MCP Server — 20 tools (5 each for Product/Feature/Requirement/UML: create/query/query-list/update/delete), Streamable HTTP transport, so any MCP-aware client can query or edit the requirements model directly.

  • Embedded single-binary deployment — the compiled frontend is staged into web/dist and embedded into the Go binary; the result is one executable that serves both the API and the UI.

  • Free-edition auth bypass — out of the box, every request is authenticated as a fixed admin identity (no login flow), so the workbench is usable immediately in a local/demo environment. (Not intended as-is for a multi-tenant or internet-facing deployment.)

Tech stack

Layer Technology
Backend language/runtime Go 1.26
HTTP framework Gin
Database SQLite via dbsqlx (raw SQL, no ORM); schema applied from config/schema_sqlite.sql
Agent/LLM orchestration trpc-agent-go
MCP Official modelcontextprotocol/go-sdk, Streamable HTTP transport
Auth/policy Casbin (via common-library-golang/auth)
CLI Cobra
Frontend React 19, TypeScript, Vite
Frontend state TanStack Query v5
Frontend routing react-router-dom v7
Styling Tailwind CSS
Diagrams Mermaid (+ Cytoscape, KaTeX for advanced diagram types)

MCP Server

Register the server with an MCP-aware client (e.g. Claude Code):

claude mcp add --transport http toolmaker-agent http://127.0.0.1:8080/agtapi/v2/mcp

20 tools are exposed, 5 for each of Product / Feature / Requirement / UML:

Operation Product Feature Requirement UML
Create create_product create_feature create_requirement create_uml
Get one query_product query_feature query_requirement query_uml
List query_product_list query_feature_list query_requirement_list query_uml_list
Update update_product update_feature update_requirement update_uml
Delete delete_product delete_feature delete_requirement delete_uml

Entities are addressed by a stable, per-parent OID (not the internal database id), and Requirement/Feature/UML lookups take a productOid (Requirement additionally accepts an optional featureOid). MCP tools execute immediately against live data — there is no propose/confirm step here (that's specific to the web chat's tool-calling flow).

License

Apache License 2.0 — see LICENSE.

Yorumlar (0)

Sonuc bulunamadi