# MCP Server Project Generator

> Generate executable FastMCP server/connector scaffolding from one or more mcp_init.md specs (per-MCP folders or file lists). Apply envelope/env-var conventions, required directory layout, and leave TODOs+Assumptions when specs are incomplete. Never emit pytest/tests.

- Skill: `leemg1476/mcp-server-project-generator` (Agent Skill)
- Install (CLI): `npx skillmds@latest add leemg1476/mcp-server-project-generator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/leemg1476/mcp-server-project-generator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: leemg1476 (https://skillmd.com/u/leemg1476)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/leemg1476/mcp-server-project-generator

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# MCP Server Project Generator

## Trigger
- Use when given MCP folders or file lists containing `mcp_init.md` and asked to scaffold runnable MCP servers/connectors from those specs.

## Inputs
- One or more MCP units; each must include `mcp_init.md`.
- Parse each spec for: MCP type (connector|server), name (external_api_name|domain_name), tools (name, purpose, request/response fields, data_source upstream|db|mixed, error cases), external integration (API base/auth/headers/query/body, DB intent), model requirements (DAO/DTO/service/repository/connection).
- If data is missing, do **not** ask questions: choose reasonable defaults and mark “Assumption:” in README + TODO comments.

## Outputs
- For every MCP, emit full project tree and full code per file (path + content). Project must be runnable (scaffold level). Unimplemented logic stays as TODO with interfaces shaped.
- Absolutely no pytest/테스트 files.

## Global Conventions
- FastMCP boilerplate (must keep order/shape):  
  `from fastmcp import FastMCP`  
  `mcp = FastMCP("<mcp_name>")`  
  `@mcp.tool` (or `@mcp.tools` only if spec forces; note it in README)  
  tool def/async def …  
  `if __name__ == "__main__": mcp.run()`
- Env vars (NAME = upper snake of external_api_name/domain_name):  
  `LOG_LEVEL` (default INFO); `DB_DSN` (server only); `MCP_<NAME>_BASE_URL`; `MCP_<NAME>_API_KEY`; `MCP_<NAME>_TIMEOUT_SEC` (default 20); `MCP_<NAME>_RETRY` (default 2).
- Types mapping: string→str, integer/int→int, float/double/number→float, boolean→bool, datetime→datetime.datetime (ISO8601 parse), date→datetime.date, decimal→decimal.Decimal, object/dict→dict[str, Any], array/list→list[T or Any]. Optional/nullable/? → Optional[T]=None. Enums → Literal[...] or Enum (Literal preferred). Unknown → Any with “Assumption:” notes.

## Required Structure (unless spec overrides)
```
<root>/
  mcp/
    common/
      settings.py      # Pydantic Settings loading .env
      logging.py       # std logging with request_id helper
      errors.py        # ValidationError/UpstreamError/DatabaseError/InternalError + payload mapper
      http_client.py   # httpx client factory with timeout/retry
      db.py            # SQLAlchemy engine + SessionLocal (tool-scope)
      types.py         # Meta, ErrorPayload, EnvelopeResponse
    connectors/<external_api_name>/
      mcp_connector_<external_api_name>.py
      tools/__init__.py, <tool_group>.py
      connection/__init__.py, client.py, schemas.py
      models/__init__.py, dto.py
    servers/<domain_name>/
      mcp_server_<domain_name>.py
      tools/__init__.py, <tool_group>.py
      service/__init__.py, interface.py (interface only, TODO impl)
      repository/__init__.py, repo.py (SQLAlchemy)
      models/__init__.py, dao.py (ORM), dto.py (Pydantic)
  README.md
  pyproject.toml or requirements.txt
```

## Behavior Rules
- **Connector**: No service/repository. tools → connection.client. All upstream→internal DTO transforms live only in `connection/client.py`; tools never touch raw upstream. Entry file named `mcp_connector_<external_api_name>.py`.
- **Server**: tools → service (interface only) → repository → db. Entry file `mcp_server_<domain_name>.py`. DAO uses declarative base. Repository gets session injected. DB session scope is per tool:  
  `with SessionLocal() as session: repo = ...; ...`
- Tool design: exactly one Request DTO and one Response DTO. Docstring must include purpose, input fields, output fields, error codes, brief example. meta.source = upstream (connector), db (server DB-only), mixed (server API+DB).
- Envelope response (never raise to caller):  
  ```
  {"data": <ResponseDTO|dict>,
   "meta": {"request_id": "...", "source": "upstream|db|mixed", "generated_at": "ISO8601", "version": "v1"},
   "error": null | {"code": "VALIDATION_ERROR|UPSTREAM_ERROR|DATABASE_ERROR|INTERNAL_ERROR", "message": "...", "detail": {...}}}
  ```

## Workflow
1) Discover all `mcp_init.md` from provided folders/files. If a referenced path is missing, note as TODO and proceed with available ones.  
2) For each spec, extract fields (type/name/tools/etc). Infer missing data with defaults; log “Assumption:” in README + code TODO.  
3) Decide MCP type → choose connector/server layout and env var NAME.  
4) Generate common layer once per project.  
5) Per MCP, create entrypoint, tools, DTOs, connection/client (connector) or service/repository/models (server). Keep interfaces complete, logic TODO where spec is silent.  
6) Ensure every tool returns Envelope and handles Validation/Upstream/Database/Internal errors into payload.  
7) Write README.md covering MCP overview, tools, env vars, run instructions, Assumptions, TODOs.  
8) Output: list every created file with path + full content. No pytest files.

## Notes
- Keep instructions concise in outputs; avoid extra assets unless required.  
- Respect any spec-enforced import paths/decorators, but preserve FastMCP boilerplate shape.  
- Never prompt the user for clarifications. Use explicit TODO + Assumption markers instead.