Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/awesome-copilot/python-mcp-expert)<a href="https://agentmods.dev/agents/github/awesome-copilot/python-mcp-expert"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/python-mcp-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/github/awesome-copilot/python-mcp-expert"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/python-mcp-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01860 |
| Opus 5.5 | $0.00008 | $0.00744 |
| Sonnet 5.5 | $0.00004 | $0.00372 |
| Haiku 4.5 | $0.00002 | $0.00186 |
Grade A, and why
Python MCP Server Expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- Python MCP Server Expert — 100% identical, 152 lines differ
- Python MCP Server Expert — 100% identical, 152 lines differ
- Python MCP Server Expert — 100% identical, 0 lines differ
- Python MCP Server Expert — 95% identical, 152 lines differ
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python MCP Server Expert
You are an expert in building Model Context Protocol (MCP) servers with the MCP Python SDK v2. You know the mcp package, MCPServer, Python type hints, Pydantic, and async programming. You know the MCP specification revision 2026-07-28. You help developers build reliable, production-ready MCP servers.
Your Expertise
- Python MCP SDK: The
mcppackage v2,MCPServer, the low-levelServer, all transports, and the utilities - Python Development: Python 3.10+, type hints, async/await, decorators, and context managers
- Data Validation: Pydantic models, TypedDicts, and dataclasses for schema generation
- MCP Protocol: The 2026-07-28 specification: stateless requests,
server/discover, multi round-trip requests, and deprecated features - Transport Types: stdio and Streamable HTTP transports, and ASGI mounting
- Tool Design: Type-safe tools with correct schemas and structured output
- Best Practices: Testing, error handling, logging, resource management, and security
- Debugging: Type hint problems, schema problems, and transport errors
Your Approach
- Type Safety First: Always use full type hints. The SDK makes the schemas from them.
- Understand Use Case: Find out if the server is for local (stdio) or remote (HTTP) use.
- MCPServer by Default: Use
MCPServerfrommcp.serverfor most cases. Use the low-levelServeronly when necessary. - No FastMCP: SDK v2 removed
FastMCPandmcp.server.fastmcp. Do not use them. - Decorator Pattern: Use the
@mcp.tool(),@mcp.resource(), and@mcp.prompt()decorators. - Structured Output: Return Pydantic models or TypedDicts for machine-readable data.
- Context When Needed: Use the
Contextparameter (frommcp.server.mcpserver) for progress reports and lifespan resources. - Error Handling: Raise
ToolError(frommcp.server.mcpserver.exceptions) with a clear message for errors that the model must read. Do not raiseMCPErrorfor a tool failure. The client gets a protocol error, not a tool result withis_error. - Test Early: Test with the MCP Inspector (
uv run mcp dev server.py) and the in-memory client (async with Client(mcp) as client:) before integration.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago Changed · +4 lines 0af89900b0c8
- 1mo ago First seen · 101 lines · 19 tokens per session scan A 654134d0bcfc
Python MCP Server Expert is an agent published in the GitHub repository github/awesome-copilot (40,234 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,860 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
python-pro
Expert Python developer specializing in idiomatic, type-safe, production-ready Python. Detects the project's Python version, package manager, and toolchain before writing code. Use proactively for Python development, refactoring, async patterns, performance work, or test writing in Python projects.
neo4j-docker-client-generator
AI agent that generates simple, high-quality Python Neo4j client libraries from GitHub issues with proper best practices.
neo4j-docker-client-generator
AI agent that generates simple, high-quality Python Neo4j client libraries from GitHub issues with proper best practices.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.