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Yellhorn MCP Server

@msnidal

基本信息

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版本控制

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stdio

作者

msnidal

关于 Yellhorn MCP Server

Yellhorn offers MCP tools to publish detailed workplans as GitHub issues with entire-codebase reasoning and to review diffs against them

Yellhorn MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "yellhorn-mcp": {
      "command": "uv",
      "args": [
        "sync",
        "--group",
        "dev"
      ]
    }
  }
}

Yellhorn MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Yellhorn MCP?

Yellhorn MCP is a Model Context Protocol server that creates detailed workplans for implementing tasks or features. It uses large AI models (e.g., Gemini 2.5 Pro, GPT‑5, o3, Grok‑4) with full codebase context, URL access, and optional web search. The server is designed for coding agents like Claude Code to define work, and later to review the output against the original requirements.

How to use Yellhorn MCP?

Install via uv sync --group dev or uv pip install yellhorn-mcp. Set environment variables GEMINI_API_KEY, OPENAI_API_KEY, XAI_API_KEY, REPO_PATH, and optionally YELLHORN_MCP_MODEL and YELLHORN_MCP_SEARCH. The GitHub CLI (gh) must be installed and authenticated. Configure the server in your MCP client (Codex CLI, VSCode/Cursor, or Claude Code) using a config.toml or mcp.json file with command uv run yellhorn-mcp.

Key features of Yellhorn MCP

  • Creates detailed workplans posted as GitHub issues
  • Judges code diffs against the original workplan
  • Seamless GitHub integration with labeled issues and sub‑issues
  • .yellhornignore file to exclude files from AI context
  • MCP resources for listing and retrieving workplans
  • Google Search Grounding enabled by default for Gemini models

Use cases of Yellhorn MCP

  • Defining implementation tasks for coding agents (e.g., Claude Code)
  • Reviewing code agent output against specified requirements
  • Generating workplans with full codebase awareness
  • Iteratively revising workplans based on feedback

常见问题

1

What models does Yellhorn MCP support?

It supports Gemini (2.5‑pro, 2.5‑flash, 2.5‑flash‑lite), OpenAI (GPT‑4o, GPT‑4o‑mini, o4‑mini, o3, GPT‑4.1, GPT‑5 series), and xAI Grok (Grok‑4, Grok‑4 Fast) models. Deep Research models (o3‑deep‑research, o4‑mini‑deep‑research, GPT‑5) enable web search and code interpreter tools.

2

How do I exclude files from AI context?

Create a `.yellhornignore` file in your repository, similar to `.gitignore`. The server also provides the `curate_context` tool to generate a `.yellhorncontext` whitelist that further reduces token usage.

3

What are the prerequisites for using Yellhorn MCP?

You need API keys for the models you plan to use (Gemini, OpenAI, or xAI), the GitHub CLI (`gh`) installed and authenticated, and a Python environment managed by `uv`. The repository to analyze must be accessible via `REPO_PATH`.

4

Can I disable web search in workplans?

Yes. Set the environment variable `YELLHORN_MCP_SEARCH` to `"off"` (default is `"on"` for Gemini models). You can also pass `disable_search_grounding: true` to individual tool calls.

5

How does workplan judgment work?

The `judge_workplan` tool compares two git refs (base and head) against a workplan stored in a GitHub issue. It creates a sub‑issue, then asynchronously evaluates the diff with full codebase context and posts detailed feedback.

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