mcpunk 0.10.0

MCP tools for Roaming RAG

MCPunk 🤖

Homepage https://github.com/jurasofish/mcpunk

MCPunk provides tools for Roaming RAG with Model Context Protocol.

MCPunk is built with the following in mind

• Context is King - LLMs can be great but only if provided with appropriate context: not too long, focused and relevant.
• Human in the Loop - You can see exactly what data the LLM has considered and how it found it, You can jump into chat and direct things wherever you want.
• Tools are Next - LLMs have landed. And the vibe is that improvements are stagnating. The next wave of LLM-based productivity will come from the tools that use LLMs well.

Core functionality allows your LLM to configure a project (e.g. a directory containing Python files). The files in this project are automatically "chunked". Each chunk is e.g. a Python function or a markdown section. The LLM can then query the entire project for chunks with specific names or with specific text in their contents. The LLM can then fetch the entire contents of individual chunks.

All this with no SaaS, no pricing, nothing (well you need a claude SaaS sub). Just you and Claude Desktop, with all tools running on your local machine after one tiny snippet in claude_desktop_config.json.

Getting started

First, install uv.

Next, put the following in your claude_desktop_config.json. Details about claude_desktop_config.json including location.

"command": "uvx", might not work, and you may need to use e.g. "command": "/Users/michael/.local/bin/uvx",

{
  "mcpServers": {
    "MCPunk": {
      "command": "uvx",
      "args": ["mcpunk"]
    }
  }
}

Next, start claude desktop and you should see the tools available after a small delay

Next, ask it to set up a project like "hey pal can you set up the ~/git/mcpunk project". Then start asking away, like "What sorts of chunks come from Python files?"

Roaming RAG

See

• https://arcturus-labs.com/blog/2024/11/21/roaming-rag--make-_the-model_-find-the-answers/
• https://simonwillison.net/2024/Dec/6/roaming-rag/

The gist of roaming RAG is

1. Break down content (a codebase, pdf files, whatever) into "chunks". Each chunk is a "small" logical item like a function, a section in a markdown document, or all imports in a code file.
2. Provide the LLM tools to search chunks. MCPunk does this by providing tools to search for files containing chunks with specific text, and to list the full contents of a specific chunk.

Compared to more traditional "vector search" RAG:

• The LLM has to drill down to find chunks, and naturally is aware of their broader context (like what file they're in)
• Chunks should always be coherent. Like a full function.
• You can see exactly what the LLM is searching for, and it's generally obvious if it's searching poorly and you can help it out by suggesting improved search terms.

Chunks

A chunk is a subsection of a file. For example,

• A single python function
• A markdown section
• All the imports from a Python file
• The diff of one file out of a multi-file diff

Chunks are created from a file by chunkers, and MCPunk comes with a handful built in.

When a project is set up in MCPunk, it goes through all files and applies the first matching chunker to it. The LLM can then use tools to (1) query for files containing chunks with specific text in them, (2) query all chunks in a specific file, and (3) fetch the full contents of a chunk.

This basic foundation enables claude to effectively navigate relatively large codebases by starting with a broad search for relevant files and narrowing in on relevant areas.

Custom Chunkers

Each type of file (e.g. Python vs C) needs a custom chunker. MCPunk comes with some built in. If no specific chunker matches a file, a default chunker that just slaps the whole file into one chunk is used.

The current suggested way to add chunks is to fork this project and add them, and run MCPunk per Development. To add a chunker

• Add it in file_chunkers.py, inheriting from BaseChunker
• Add it to ALL_CHUNKERS in file_breakdown.py

It would be possible to implement some kind of plugin system for modules to advertise that they have custom chunkers for MCPunk to use, like pytest's plugin system, but there are currently no plans to implement this (unless someone wants to do it).

Common Usage Patterns

Answer Questions About Your Codebase

• [User] Hey pal can you please set up the ~/git/mcpunk repo, then help me understand how a user of mcpunk would get the git diff of the currently checked out branch in the repo that they have configured with mcpunk
• [Claude] Call configure_project
• [Claude] Call list_all_files_in_project
• [Claude] Call find_files_by_chunk_content
• [Claude] ... makes a total of 21 tool calls with MCPunk.
• [Claude] Now I understand how the git diff functionality works in mcpunk. Let me summarize how a user would get the git diff of the currently checked out branch: To get a git diff of the currently checked out branch in a repo configured with mcpunk:
  1. First, the user needs to configure mcpunk to work with their project by using the configure_project tool
  2. To see what branches were recently checked out (to identify what to diff against)
  3. Then, to get the diff between the current branch (HEAD) and a reference branch This will display the differences between the current branch (HEAD) and the specified reference branch. The ref parameter should be the name of the base branch (like "main" or "develop") that you want to compare against.
• [User] Great job pal!

PR Review

• [user] Hey pal can you please set up the ~/git/mcpunk repo and have a look at the current branch versus scratch/1.5
• [Claude] Sets up ~/git/mcpunk and calls diff_with_ref for ref scratch/1.5
• [Claude] I see you've made several changes, primarily adding a new test file tests/test_git_analysis.py...
• [user] how does this fit into the rest of the codebase?
• [Claude] Makes 5 tool calls for listing files by chunks, listing chunks, fetching chunk details
• [Claude] I see - the code parses git reflog output to track recently checked out branches. The main function get_recent_branches takes a repo path and returns the last N branches that were checked out, using _branches_from_reflog as a helper to parse the raw reflog output.
• [User] If you were in a rush and had to direct someone to review this work, what would you direct them to look at? Please outline discrete pieces of work
• [Claude]
  • The split("to ")[-1] in _branches_from_reflog looks fragile
  • Test data only shows simple branch names, what about spaces/special chars?
  • Is example_reflog representative of real git reflog output?
  • Are we testing against actual git behavior?

Work across multiple codebases

You can just ask your LLM to set up multiple projects, and it can freely query across them. Handy if one depends on the other, and they're in different repos. In this case the LLM should recognise this via imports.

Limitations

• Sometimes LLM is poor at searching. e.g. search for "dependency", missing terms "dependencies". Room to stem things.
• Sometimes LLM will try to find a specific piece of critical code but fail to find it, then continue without acknowledging it has limited contextual awareness.
• "Large" projects are not well tested. A project with ~1000 Python files containing in total ~250k LoC works well. Takes ~5s to setup the project. As codebase size increases, time to perform initial chunking will increase, and likely more sophisticated searching will be required.

Configuration

Various things can be configured via environment variables. For available options, see settings.py - these are loaded from env vars via Pydantic Settings.

Roadmap

MCPunk is at a minimum usable state right now.

Critical Planned functionality

• Add a bunch of prompts to help with using MCPunk. Without real "explain how to make a pancake to an alien"-type prompts things do fall a little flat.

High up on the roadmap

• Possibly stemming for search
• Change the whole "project" concept to not need files to actually exist - this leads to allowing "virtual" files inside the project.
  • Consider changing files from having a path to having a URI, so coule be like file://... / http[s]:// / gitdiff:// / etc arbitrary URIs
• Integrate with web searching
  • Flow like (1) LLM says "search for Caridina water parameters" (2) tool does web search and grabs the 10 highest pages and converts to markdown and chunks them and puts them in the virtual filesystem (3) LLM queries for chunks etc like usual.
• Chunking of git diffs. Currently, there's a tool to fetch an entire diff. This might be very large. Instead, the tool could be changed to add_diff_to_project and it puts files under the gitdiff:// URI or under some fake path
• Include module-level comments when extracting python module-level statements.
• Caching of a project, so it doesn't need to re-parse all files every time you restart MCP client. This may be tricky as changes to the code in a chunker will make cache invalid.
• Handle changed files sensibly, so you don't need to restart MCP client and re-add project on any file changes
• Ability to edit files - why not? Can do it like aider where LLM produces a diff.
• Ability for users to provide custom code to perform chunking, perhaps similar to pytest plugins

Just ideas

• Something like tree sitter could possibly be used for a more generic chunker
• Better handling of large chunks
  • Configurable Max response size for chunks
  • Log warning for any chunk over max size when initialising project
• Tracking of characters sent/received, ideally by chat.
• State, logging, etc by chat

Development

see run_mcp_server.py.

If you set up claude desktop like below then you can restart it to see latest changes as you work on MCPunk from your local version of the repo.

{
  "mcpServers": {
    "MCPunk": {
      "command": "/Users/michael/.local/bin/uvx",
      "args": [
        "--from",
        "/Users/michael/git/mcpunk",
        "--no-cache",
        "mcpunk"
      ]
    }
  }
}

Testing, Linting, CI

See the Makefile and github actions workflows.