Yellhorn MCP Server

A Model Context Protocol (MCP) server that provides functionality to create detailed workplans to implement a task or feature. These workplans are generated with a large, powerful model (such as gemini 2.5 pro or even the o3 deep research. The yellhorn mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 5 defined tools rather than through you.

What it actually does

• Create Workplans — Creates detailed implementation plans based on a prompt and taking into consideration your entire codebase, posting them as GitHub issues and exposing them as MCP resources for your coding agent
• Seamless GitHub Integration — Automatically creates labeled issues, posts judgement sub-issues with references to original workplan issues
• Context Control — Use .yellhornignore files to exclude specific files and directories from the AI context, similar to .gitignore
• MCP Resources — Exposes workplans as standard MCP resources for easy listing and retrieval
• Google Search Grounding — Enabled by default for Gemini models, providing search capabilities with automatically formatted citations in Markdown
• Automatic Chunking — Handles large codebases that exceed model context limits by intelligently splitting prompts

Its toolset

Everything the assistant can do here goes through one of these:

• curate_context — Analyzes the codebase and creates a .yellhorncontext file listing directories to be included in AI context. This tool helps optimize AI context by
• create_workplan — Creates a GitHub issue with a detailed workplan based on the title and detailed description
• get_workplan — Retrieves the workplan content (GitHub issue body) associated with a workplan
• revise_workplan — Updates an existing workplan based on revision instructions. The tool fetches the current workplan from the specified GitHub issue and uses AI to
• judge_workplan — Triggers an asynchronous code judgement comparing two git refs (branches or commits) against a workplan described in a GitHub issue. Creates a

Adding it to your client

The server ships on PyPI as yellhorn-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

Configuration

You will need 6 environment variables: GEMINI_API_KEY, REPO_PATH, YELLHORN_MCP_SEARCH, OPENAI_API_KEY, XAI_API_KEY, PYPI_API_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

When to reach for it

Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Yellhorn MCP's toolset — curate_context, create_workplan, get_workplan and 2 more — is a fair guide to whether it matches your workflow. It is maintained by msnidal; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Yellhorn MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Caveats

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the yellhorn mcp mcp server does with a few real requests.