team-docs-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@team-docs-mcpsearch for deployment best practices"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 thedocs/folderlist-indexed-files- Get a list of all indexed documentation filesget-document-content- Read the full content of any documentation fileget-documentation-guidelines- Get guidelines for creating well-structured documentationsave-documentation- Save new documentation files and automatically index themLocal embeddings - Uses
@xenova/transformersfor local semantic search - no external API calls requiredIntelligent 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!
Related MCP server: MCP Local Context
Installation
git clone <repo-url> team-docs-mcp
cd team-docs-mcp
npm installQuick Start
Add your documentation files to the
docs/folder (Markdown, text, JSON, HTML, CSV, etc.)Run the server:
npm startConfigure your MCP client to use this server
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 .envEvery variable uses the TEAM_DOCS_MCP_ prefix to avoid collisions:
Variable | Default | Description |
|
| Directory containing documents to index |
|
| Cache directory for computed embeddings |
|
| Sentence-transformer model (ONNX) |
|
| Comma-separated file extensions to include |
|
| Files larger than this are skipped |
|
| Number of words per text chunk |
|
| 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.mdThe 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 startMCP Inspector
To test the tool interactively with the MCP Inspector:
npm run inspectMCP Tools
search-documentation
Semantic similarity search over indexed documents from the docs/ folder.
Parameter | Type | Required | Default | Description |
| string | ✅ | - | Natural language search query |
| number | - |
| Maximum number of results to return |
| number | - |
| 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 |
| 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 |
| string | ✅ | Name of the markdown file (must end with .md) |
| string | ✅ | Markdown content to save |
| 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
Startup:
DocumentLoaderscansdocs/for supported filesChunking:
FileIndexersplits each document into overlapping word chunksEmbedding: Each chunk is embedded using a local sentence-transformer model (runs via ONNX)
Caching: Embeddings are saved to
docs/.cache/- unchanged files are skipped on restartSearch: When
search-documentationis 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 theToolbase classCustomize chunking: Modify
FileIndexerto change how documents are splitDifferent embedding models: Change
EMBEDDING_MODELin config to use other sentence-transformersCustom file types: Add parsers in
DocumentLoaderfor additional file formats
License
MIT
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Alicense-qualityDmaintenanceAn MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation contextLast updated22265MIT
- Alicense-qualityDmaintenanceA simple MCP server for local documentation with RAG capabilities, enabling AI assistants to access and search local documents.Last updatedMIT
- AlicenseAqualityCmaintenanceA local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.Last updated52MIT
- Alicense-qualityCmaintenanceAn MCP server that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.Last updated20MIT
Related MCP Connectors
Local-first RAG engine with MCP server for AI agent integration.
An MCP server that gives your AI access to the source code and docs of all public github repos
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/jpnqs/team-docs-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server