team-docs-mcp

An MCP (Model Context Protocol) server for team documentation and knowledge bases. It provides semantic search over your team's documentation, guidelines, best practices, and frequently-used information - all accessible as MCP tools through Claude and other MCP clients.

Features

• search-documentation - Semantic similarity search over your team's documentation in the docs/ folder
• list-indexed-files - Get a list of all indexed documentation files
• get-document-content - Read the full content of any documentation file
• get-documentation-guidelines - Get guidelines for creating well-structured documentation
• save-documentation - Save new documentation files and automatically index them
• Local embeddings - Uses @xenova/transformers for local semantic search - no external API calls required
• Intelligent caching - Embeddings are cached and only recomputed when documents change
• Flexible configuration - Customize chunk sizes, file types, and embedding models
• Clean TypeScript architecture - Easy to understand and extend

Perfect for making your team's knowledge searchable through AI assistants!

Installation

git clone <repo-url> team-docs-mcp
cd team-docs-mcp
npm install

Quick Start

1. Add your documentation files to the docs/ folder (Markdown, text, JSON, HTML, CSV, etc.)
2. Run the server: npm start
3. Configure your MCP client to use this server
4. Start asking questions about your team's documentation!

Configuration

All settings have sensible defaults and work out of the box. To customise, copy the example file and edit as needed:

cp .env.example .env

Every variable uses the TEAM_DOCS_MCP_ prefix to avoid collisions:

Variable	Default	Description
TEAM_DOCS_MCP_DOCS_DIR	./docs	Directory containing documents to index
TEAM_DOCS_MCP_INDEXED_DIR	./docs/.cache	Cache directory for computed embeddings
TEAM_DOCS_MCP_EMBEDDING_MODEL	Xenova/all-MiniLM-L6-v2	Sentence-transformer model (ONNX)
TEAM_DOCS_MCP_ALLOWED_EXTENSIONS	.md,.txt,.json,.html,.htm,.csv	Comma-separated file extensions to include
TEAM_DOCS_MCP_MAX_FILE_SIZE_BYTES	20971520 (20 MB)	Files larger than this are skipped
TEAM_DOCS_MCP_CHUNK_SIZE_WORDS	220	Number of words per text chunk
TEAM_DOCS_MCP_CHUNK_OVERLAP_WORDS	40	Word overlap between consecutive chunks

Adding Documentation

Place your documentation files in the docs/ directory:

docs/
  coding-standards.md
  deployment-guide.md
  api-conventions.md
  troubleshooting-tips.md
  team-processes.md

The server indexes all documents at startup. Embeddings are cached in docs/.cache/ and only recomputed when the source file changes. Restart the server after adding or updating documents.

Running

npm run start

MCP Inspector

To test the tool interactively with the MCP Inspector:

npm run inspect

MCP Tools

search-documentation

Semantic similarity search over indexed documents from the docs/ folder.

Parameter	Type	Required	Default	Description
query	string	✅	-	Natural language search query
topK	number	-	5	Maximum number of results to return
minScore	number	-	0.2	Minimum cosine similarity threshold (0–1)

Example queries:

• "How do we handle database migrations?"
• "What are our coding standards for error handling?"
• "How to deploy to production?"
• "API authentication best practices"

list-indexed-files

Get a list of all documentation files that have been indexed and are available for semantic search.

No parameters required.

Returns:

• List of indexed file paths
• Total file count
• Total chunk count
• Last indexing timestamp

get-document-content

Read the complete content of a specific documentation file.

Parameter	Type	Required	Description
filePath	string	✅	Relative path to the file (e.g., "guides/observer-pattern.md")

Returns: Full content of the specified documentation file.

get-documentation-guidelines

Get comprehensive guidelines for creating and formatting team documentation.

No parameters required.

Returns: Detailed guidelines covering file naming, structure, formatting, and best practices.

save-documentation

Save a new documentation file to the docs folder. The file is automatically indexed for semantic search.

Parameter	Type	Required	Description
filename	string	✅	Name of the markdown file (must end with .md)
content	string	✅	Markdown content to save
subfolder	string	-	Optional subfolder within docs/ (e.g., "guides")

Returns: Confirmation of saved file path and indexing status.

Project Structure

server.ts                              # Entry point - wiring & startup
src/
  config.ts                            # Centralised configuration (env overrides)
  tool.ts                              # Abstract Tool<TInput> base class
  file-indexer.ts                      # Chunking, embedding, caching & search
  document-loader.ts                   # File discovery & reading
  logger.ts                            # Logging utility
  tools/
    search-documentation.ts            # Semantic search tool
    list-indexed-files.ts              # List all indexed files
    get-document-content.ts            # Read full file content
    get-documentation-guidelines.ts    # Get documentation guidelines
    save-documentation.ts              # Save and index new documentation
assets/
  documentation-guidelines.md          # Guidelines for creating documentation
docs/                                  # Your team's documentation (add files here!)
  .cache/                              # Cached embeddings (auto-generated)

How It Works

1. Startup: DocumentLoader scans docs/ for supported files
2. Chunking: FileIndexer splits each document into overlapping word chunks
3. Embedding: Each chunk is embedded using a local sentence-transformer model (runs via ONNX)
4. Caching: Embeddings are saved to docs/.cache/ - unchanged files are skipped on restart
5. Search: When search-documentation is called, the query is embedded and ranked against all chunks by cosine similarity

Use Cases

• Onboarding: Help new team members quickly find answers to common questions
• Best Practices: Make coding standards, conventions, and guidelines easily searchable
• Troubleshooting: Quick access to debugging guides and known issues
• Process Documentation: Find information about deployment, testing, and development workflows
• API Documentation: Search through API specs, usage examples, and integration guides
• Institutional Knowledge: Preserve and make searchable team decisions, architecture notes, and meeting summaries

Configuring with Claude Desktop

Add to your Claude Desktop configuration file:

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

{
  "mcpServers": {
    "team-docs": {
      "command": "npx",
      "args": ["-y", "tsx", "/absolute/path/to/team-docs-mcp/server.ts"]
    }
  }
}

Restart Claude Desktop after updating the configuration.

Sample Instructions for Copilot

To get the best results when using this MCP server with GitHub Copilot, create an instructions file:

.github/copilot-instructions.md

# Team Docs MCP Server - Copilot Instructions

MCP server for team documentation and knowledge base. Provides semantic search over local docs.

## Tool Usage

Use the `search-documentation` tool to answer questions about:

- Team processes and workflows
- Coding standards and conventions
- Deployment and infrastructure
- API documentation and examples
- Troubleshooting guides
- Best practices and guidelines

Always search the documentation before making assumptions about team practices or standards.

Extending the Server

The codebase is designed to be simple and extensible:

• Add new tools: Create new tool classes in src/tools/ extending the Tool base class
• Customize chunking: Modify FileIndexer to change how documents are split
• Different embedding models: Change EMBEDDING_MODEL in config to use other sentence-transformers
• Custom file types: Add parsers in DocumentLoader for additional file formats

License

MIT