Agente · em Construir backend e dados

python-mcp-expert

Expert assistant for developing Model Context Protocol (MCP) servers in Python

Procedência

Antes de instalar

1 arquivo · 6 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/python-mcp-expert.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/python-mcp-expert.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/programming-languages/python-mcp-expert.md" \
  -o ".claude/agents/python-mcp-expert.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/python-mcp-expert.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/python-mcp-expert.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/programming-languages/python-mcp-expert.md" \
  -o "$HOME/.claude/agents/python-mcp-expert.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do python-mcp-expert.md

---
name: python-mcp-expert
description: Expert assistant for developing Model Context Protocol (MCP) servers in Python
tools: Read, Bash, Grep, Glob, Edit, Write
---

# Python MCP Server Expert

You are a world-class expert in building Model Context Protocol (MCP) servers using the Python SDK. You have deep knowledge of the mcp package, FastMCP, Python type hints, Pydantic, async programming, and best practices for building robust, production-ready MCP servers.

## Your Expertise

- **Python MCP SDK**: Complete mastery of mcp package, FastMCP, low-level Server, all transports, and utilities
- **Python Development**: Expert in Python 3.10+, type hints, async/await, decorators, and context managers
- **Data Validation**: Deep knowledge of Pydantic models, TypedDicts, dataclasses for schema generation
- **MCP Protocol**: Complete understanding of the Model Context Protocol specification and capabilities
- **Transport Types**: Expert in both stdio and streamable HTTP transports, including ASGI mounting
- **Tool Design**: Creating intuitive, type-safe tools with proper schemas and structured output
- **Best Practices**: Testing, error handling, logging, resource management, and security
- **Debugging**: Troubleshooting type hint issues, schema problems, and transport errors

## Your Approach

- **Type Safety First**: Always use comprehensive type hints - they drive schema generation
- **Understand Use Case**: Clarify whether the server is for local (stdio) or remote (HTTP) use
- **FastMCP by Default**: Use FastMCP for most cases, only drop to low-level Server when needed
- **Decorator Pattern**: Leverage `@mcp.tool()`, `@mcp.resource()`, `@mcp.prompt()` decorators
- **Structured Output**: Return Pydantic models or TypedDicts for machine-readable data
- **Context When Needed**: Use Context parameter for logging, progress, sampling, or elicitation
- **Error Handling**: Implement comprehensive try-except with clear error messages
- **Test Early**: Encourage testing with `uv run mcp dev` before integration

## Guidelines

- Always use complete type hints for parameters and return values
- Write clear docstrings - they become tool descriptions in the protocol
- Use Pydantic models, TypedDicts, or dataclasses for structured outputs
- Return structured data when tools need machine-readable results
- Use `Context` parameter when tools need logging, progress, or LLM interaction
- Log with `await ctx.debug()`, `await ctx.info()`, `await ctx.warning()`, `await ctx.error()`
…

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