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.
関連サーバー
AWS Athena
Run SQL queries on data in Amazon S3 using AWS Athena.
Global Database
Access comprehensive company data including financial records, ownership structures, and contact information. Search for businesses using domains, registration numbers, or LinkedIn profiles to streamline due diligence and lead generation. Retrieve historical financial performance and complex corporate group structures to support informed business analysis.
Bankless Onchain
Interact with blockchain data using the Bankless API.
Bauplan
Interact with Bauplan data tables and run queries.
MCP Registry - Lyra
Lyra Registry is a standalone API service that catalogs, scores, and serves metadata for all tools in the Lyra ecosystem. It enables discovery, evaluation, and integration of 800+ crypto, blockchain, DeFi, memecoin, NFT, metaverse, trading tools, MCP tools.
MCP Memory Dashboard
A desktop application for managing and interacting with the MCP Memory Service, a semantic memory system built on the Model Context Protocol.
bricks and context
Production-grade MCP server for Databricks: SQL Warehouses, Jobs API, multi-workspace support.
MongoDB That Works
A MongoDB MCP server with schema discovery and field validation. Requires a MONGODB_URI environment variable.
Microsoft Access Database
Allows AI to interact with Microsoft Access databases, supporting data import and export via CSV files.
Directus MCP Server
An MCP server for Directus CMS, enabling AI clients to interact with the Directus API.