Chroma
A vector database server powered by Chroma, enabling semantic document search, metadata filtering, and document management.
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/datadirectory - 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:
uv venv
uv sync --dev --all-extras
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
{
"mcpServers": {
"chroma": {
"command": "uv",
"args": [
"--directory",
"C:/MCP/server/community/chroma",
"run",
"chroma"
]
}
}
}
Data Storage
The server stores data in:
- Windows:
src/chroma/data - MacOS/Linux:
src/chroma/data
Usage
- Start the server:
uv run chroma
- Use MCP tools to interact with the server:
# Create a document
create_document({
"document_id": "ml_paper1",
"content": "Convolutional neural networks improve image recognition accuracy.",
"metadata": {
"year": 2020,
"field": "computer vision",
"complexity": "advanced"
}
})
# Search similar documents
search_similar({
"query": "machine learning models",
"num_results": 2,
"metadata_filter": {
"year": 2020,
"field": "computer vision"
}
})
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 contentInvalid filterOperation failed: [details]
Development
Testing
- Run the MCP Inspector for interactive testing:
npx @modelcontextprotocol/inspector uv --directory C:/MCP/server/community/chroma run chroma
- Use the inspector's web interface to:
- Test CRUD operations
- Verify search functionality
- Check error handling
- Monitor server logs
Building
- Update dependencies:
uv compile pyproject.toml
- Build package:
uv build
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.
Servidores relacionados
BioMCP
Connects AI assistants to authoritative biomedical data sources like PubMed and ClinicalTrials.gov, enabling natural language queries.
CongressMCP
Access comprehensive U.S. Congressional data, including bills, votes, and member information, via the Congress.gov API.
kintone
An MCP server for accessing and managing data on the kintone low-code platform.
AKShare One
Access Chinese stock market data, including historical prices, real-time quotes, news, and financial statements.
ChatQL MCP Server
Query SQL Server databases using natural language with OpenAI GPT models.
PyAirbyte
An AI-powered server that generates PyAirbyte pipeline code and instructions using OpenAI and connector documentation.
Google Analytics MCP Server by CData
A read-only MCP server for querying live Google Analytics data using LLMs. Powered by CData.
Turso Cloud
Integrate with Turso databases for LLMs, featuring a two-level authentication system for secure operations.
Supabase
Interact with Supabase databases, query tables, and generate TypeScript types.
CentralMind Gateway
Expose structured databases to AI agents via MCP or OpenAPI 3.1 protocols, with APIs optimized for AI workloads.