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.
npx agentmods add agents/archubbuck/workspace-architect/python-mcp-expertgit clone --depth 1 https://github.com/archubbuck/workspace-architectWrote 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/archubbuck/workspace-architect/python-mcp-expert)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/python-mcp-expert"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/python-mcp-expert.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01320 |
| Opus 5 | $0.00010 | $0.00660 |
| Sonnet 5 | $0.00004 | $0.00264 |
| Haiku 4.5 | $0.00002 | $0.00132 |
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 yesterday.
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.
This is a copy
100% identical to Python MCP Server Expert — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 devbefore 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
Contextparameter when tools need logging, progress, or LLM interaction - Log with
await ctx.debug(),await ctx.info(),await ctx.warning(),await ctx.error() - Report progress with
await ctx.report_progress(progress, total, message) - Use sampling for LLM-powered tools:
await ctx.session.create_message() - Request user input with
await ctx.elicit(message, schema) - Define dynamic resources with URI templates:
@mcp.resource("resource://{param}") - Use lifespan context managers for startup/shutdown resources
- Access lifespan context via
ctx.request_context.lifespan_context - For HTTP servers, use
mcp.run(transport="streamable-http") - Enable stateless mode for scalability:
stateless_http=True - Mount to Starlette/FastAPI with
mcp.streamable_http_app() - Configure CORS and expose
Mcp-Session-Idfor browser clients - Test with MCP Inspector:
uv run mcp dev server.py - Install to Claude Desktop:
uv run mcp install server.py - Use async functions for I/O-bound operations
- Clean up resources in finally blocks or context managers
- Validate inputs using Pydantic Field with descriptions
- Provide meaningful parameter names and descriptions
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.
- yesterday First seen · 101 lines · 19 tokens per session scan A 654134d0bcfc
Python MCP Server Expert is an agent published in the GitHub repository archubbuck/workspace-architect (18 stars, last pushed yesterday), licensed ISC. It adds 19 tokens to every session and 1,320 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Python MCP Server Expert, differing in 0 lines, and is treated as a copy.
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