Python MCP Instructions

Instructions for building Model Context Protocol (MCP) servers with Python. MCP is a standard way for AI assistants to use tools and access information from other programs.

In plain words
What is it for?
Use them when creating Python MCP tools, resources, or prompts that run locally or over HTTP.
Why use it?
They provide the expected project setup, registration patterns, data validation, communication options, logging, progress reporting, and user-input handling in the Python MCP SDK.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/timothywarner/copilot-dev/python-mcp-instructions
Clone the repo
git clone --depth 1 https://github.com/timothywarner/copilot-dev

Made for: GitHub Copilot.

Per session 1,600 This file is loaded in full into every session.
When invoked 1,600 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01600 $0.01600
Opus 5 $0.00800 $0.00800
Sonnet 5 $0.00320 $0.00320
Haiku 4.5 $0.00160 $0.00160

Measured 2d ago against content hash 85b5d2d40f7c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Python MCP Instructions 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 2d 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.

.github/instructions/Python MCP Instructions.instructions.md · 214 lines

How it starts

The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python MCP Server Development

Instructions

  • Use uv for project management: uv init mcp-server-demo and uv add "mcp[cli]"
  • Import FastMCP from mcp.server.fastmcp: from mcp.server.fastmcp import FastMCP
  • Use @mcp.tool(), @mcp.resource(), and @mcp.prompt() decorators for registration
  • Type hints are mandatory - they're used for schema generation and validation
  • Use Pydantic models, TypedDicts, or dataclasses for structured output
  • Tools automatically return structured output when return types are compatible
  • For stdio transport, use mcp.run() or mcp.run(transport="stdio")
  • For HTTP servers, use mcp.run(transport="streamable-http") or mount to Starlette/FastAPI
  • Use Context parameter in tools/resources to access MCP capabilities: ctx: Context
  • Send logs with await ctx.debug(), await ctx.info(), await ctx.warning(), await ctx.error()
  • Report progress with await ctx.report_progress(progress, total, message)
  • Request user input with await ctx.elicit(message, schema)
  • Use LLM sampling with await ctx.session.create_message(messages, max_tokens)
  • Configure icons with Icon(src="path", mimeType="image/png") for server, tools, resources, prompts
  • Use Image class for automatic image handling: return Image(data=bytes, format="png")
  • Define resource templates with URI patterns: @mcp.resource("greeting://{name}")
  • Implement completion support by accepting partial values and returning suggestions
  • Use lifespan context managers for startup/shutdown with shared resources
  • Access lifespan context in tools via ctx.request_context.lifespan_context
  • For stateless HTTP servers, set stateless_http=True in FastMCP initialization
  • Enable JSON responses for modern clients: json_response=True
  • Test servers with: uv run mcp dev server.py (Inspector) or uv run mcp install server.py (Claude Desktop)
  • Mount multiple servers in Starlette with different paths: Mount("/path", mcp.streamable_http_app())
  • Configure CORS for browser clients: expose Mcp-Session-Id header
  • Use low-level Server class for maximum control when FastMCP isn't sufficient

Read the full file on GitHub · 214 lines

Changes

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.

  1. 2d ago First seen · 214 lines · 1,600 tokens per session scan A 85b5d2d40f7c

Subscribe to this mod's changes

Python MCP Instructions is an instructions file published in the GitHub repository timothywarner/copilot-dev (46 stars, last pushed 29d ago), licensed MIT. It adds 1,600 tokens to every session, about $0.0080 per session on Opus 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 2026-08-30.

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