Chroma MCP Server
A Model Context Protocol (MCP) server implementation that provides vector database capabilities through Chroma. This server enables semantic document search, metadata filtering, and document management with persistent storage.
Requirements
- Python 3.8+
- Chroma 0.4.0+
- MCP SDK 0.1.0+
Components
Resources
The server provides document storage and retrieval through Chroma's vector database:
- Stores documents with content and metadata
- Persists data in
src/chroma/data
directory - Supports semantic similarity search
Tools
The server implements CRUD operations and search functionality:
Document Management
create_document
: Create a new document- Required:
document_id
,content
- Optional:
metadata
(key-value pairs) - Returns: Success confirmation
- Error: Already exists, Invalid input
- Required:
read_document
: Retrieve a document by ID- Required:
document_id
- Returns: Document content and metadata
- Error: Not found
- Required:
update_document
: Update an existing document- Required:
document_id
,content
- Optional:
metadata
- Returns: Success confirmation
- Error: Not found, Invalid input
- Required:
delete_document
: Remove a document- Required:
document_id
- Returns: Success confirmation
- Error: Not found
- Required:
list_documents
: List all documents- Optional:
limit
,offset
- Returns: List of documents with content and metadata
- Optional:
Search Operations
search_similar
: Find semantically similar documents- Required:
query
- Optional:
num_results
,metadata_filter
,content_filter
- Returns: Ranked list of similar documents with distance scores
- Error: Invalid filter
- Required:
Features
- Semantic Search: Find documents based on meaning using Chroma's embeddings
- Metadata Filtering: Filter search results by metadata fields
- Content Filtering: Additional filtering based on document content
- Persistent Storage: Data persists in local directory between server restarts
- Error Handling: Comprehensive error handling with clear messages
- Retry Logic: Automatic retries for transient failures
Installation
- Install dependencies:
Configuration
Claude Desktop
Add the server configuration to your Claude Desktop config:
Windows: C:\Users\<username>\AppData\Roaming\Claude\claude_desktop_config.json
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Data Storage
The server stores data in:
- Windows:
src/chroma/data
- MacOS/Linux:
src/chroma/data
Usage
- Start the server:
- Use MCP tools to interact with the server:
Error Handling
The server provides clear error messages for common scenarios:
Document already exists [id=X]
Document not found [id=X]
Invalid input: Missing document_id or content
Invalid filter
Operation failed: [details]
Development
Testing
- Run the MCP Inspector for interactive testing:
- Use the inspector's web interface to:
- Test CRUD operations
- Verify search functionality
- Check error handling
- Monitor server logs
Building
- Update dependencies:
- Build package:
Contributing
Contributions are welcome! Please read our Contributing Guidelines for details on:
- Code style
- Testing requirements
- Pull request process
License
This project is licensed under the MIT License - see the LICENSE file for details.
local-only server
The server can only run on the client's local machine because it depends on local resources.
A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
- Requirements
- Components
- Features
- Installation
- Configuration
- Usage
- Error Handling
- Development
- Contributing
- License
Related Resources
Related MCP Servers
- AsecurityAlicenseAqualityA Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.Last updated -443TypeScriptMIT License
- -securityFlicense-qualityA Model Context Protocol server for ingesting, chunking and semantically searching documentation files, with support for markdown, Python, OpenAPI, HTML files and URLs.Last updated -Python
- -securityFlicense-qualityA Model Context Protocol server integration that creates a persistent, searchable working memory for AI-assisted development by enabling automated context recall and knowledge persistence in Chroma, the open-source embedding database.Last updated -16Python
- AsecurityFlicenseAqualityA Model Context Protocol server that intelligently fetches and processes web content, transforming websites and documentation into clean, structured markdown with nested URL crawling capabilities.Last updated -24053TypeScript