doc-lib-mcp MCP server

A Model Context Protocol (MCP) server for document ingestion, chunking, semantic search, and note management.

Components

Resources

• Implements a simple note storage system with:
  • Custom note:// URI scheme for accessing individual notes
  • Each note resource has a name, description, and text/plain mimetype

Prompts

• Provides a prompt:
  • summarize-notes: Creates summaries of all stored notes
    • Optional "style" argument to control detail level (brief/detailed)
    • Generates prompt combining all current notes with style preference

Tools

The server implements a wide range of tools:

• add-note: Add a new note to the in-memory note store
  • Arguments: name (string), content (string)
• ingest-markdown: Ingest and chunk a markdown (.md) file
  • Arguments: path (string)
• ingest-python: Ingest and chunk a Python (.py) file
  • Arguments: path (string)
• ingest-openapi: Ingest and chunk an OpenAPI JSON file
  • Arguments: path (string)
• ingest-html: Ingest and chunk an HTML file
  • Arguments: path (string)
• ingest-html-url: Ingest and chunk HTML content from a URL (optionally using Playwright for dynamic content)
  • Arguments: url (string), dynamic (boolean, optional)
• search-chunks: Semantic search over ingested content
  • Arguments: query (string), top_k (integer, optional, default 3)
• delete-source: Delete all chunks from a given source
  • Arguments: source (string)
• ingest-batch: Ingest and chunk multiple documentation files (markdown, OpenAPI JSON, Python) in batch
  • Arguments: paths (list of strings)
• list-sources: List all unique sources (file paths) that have been ingested and stored in memory
• update-chunk-metadata: Update the metadata field for a chunk by id
  • Arguments: id (integer), metadata (object)

Chunking and Code Extraction

• Markdown, Python, OpenAPI, and HTML files are split into logical chunks for efficient retrieval and search.
• The HTML chunker extracts code snippets from <pre> and <code> tags as dedicated "code" chunks, enabling code-only search and retrieval.

Semantic Search

• The search-chunks tool performs vector-based semantic search over all ingested content, returning the most relevant chunks for a given query.

Metadata Management

• Chunks include a metadata field for categorization and tagging.
• The update-chunk-metadata tool allows updating metadata for any chunk by its id.

Configuration

[TODO: Add configuration details specific to your implementation]

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

2. Build package distributions:

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

Note: You'll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.