Borrowing it
Nothing to install: this file belongs to SecondLifes/code-intel. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SecondLifes/code-intel/main/.agents/skills/python-mcp-server-generator/SKILL.mdgit clone --depth 1 https://github.com/SecondLifes/code-intelWrote 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/skills/secondlifes/code-intel/python-mcp-server-generator)<a href="https://agentmods.dev/skills/secondlifes/code-intel/python-mcp-server-generator"><img src="https://agentmods.dev/badge/skills/secondlifes/code-intel/python-mcp-server-generator/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/skills/secondlifes/code-intel/python-mcp-server-generator"><img src="https://agentmods.dev/badge/skills/secondlifes/code-intel/python-mcp-server-generator.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 | $0.00022 | $0.01176 |
| Opus 5.5 | $0.00009 | $0.00470 |
| Sonnet 5.5 | $0.00004 | $0.00235 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
python-mcp-server-generator 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 1mo 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.
This is a copy
89% identical to python-mcp-server-generator — 8 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Python MCP Server
Create a complete Model Context Protocol (MCP) server in Python with the following specifications:
Requirements
- Project Structure: Create a new Python project with proper structure using uv
- Dependencies: Include mcp[cli] package with uv
- Transport Type: Choose between stdio (for local) or streamable-http (for remote)
- Tools: Create at least one useful tool with proper type hints
- Error Handling: Include comprehensive error handling and validation
Implementation Details
Project Setup
- Initialize with
uv init project-name - Add MCP SDK:
uv add "mcp[cli]" - Create main server file (e.g.,
server.py) - Add
.gitignorefor Python projects - Configure for direct execution with
if __name__ == "__main__"
Server Configuration
- Use
FastMCPclass frommcp.server.fastmcp - Set server name and optional instructions
- Choose transport: stdio (default) or streamable-http
- For HTTP: optionally configure host, port, and stateless mode
Tool Implementation
- Use
@mcp.tool()decorator on functions - Always include type hints - they generate schemas automatically
- Write clear docstrings - they become tool descriptions
- Use Pydantic models or TypedDicts for structured outputs
- Support async operations for I/O-bound tasks
- Include proper error handling
Resource/Prompt Setup (Optional)
- Add resources with
@mcp.resource()decorator - Use URI templates for dynamic resources:
"resource://{param}" - Add prompts with
@mcp.prompt()decorator - Return strings or Message lists from prompts
Code Quality
- Use type hints for all function parameters and returns
- Write docstrings for tools, resources, and prompts
- Follow PEP 8 style guidelines
- Use async/await for asynchronous operations
- Implement context managers for resource cleanup
- Add inline comments for complex logic
Example Tool Types to Consider
- Data processing and transformation
- File system operations (read, analyze, search)
- External API integrations
- Database queries
- Text analysis or generation (with sampling)
- System information retrieval
- Math or scientific calculations
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.
- 1mo ago First seen · 114 lines · 22 tokens per session scan A b84b315860d7
python-mcp-server-generator is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 1,176 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to python-mcp-server-generator, differing in 8 lines, and is treated as a copy.
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