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.
เซิร์ฟเวอร์ที่เกี่ยวข้อง
MCP Helius
Access Solana blockchain data using the Helius API.
STRING-MCP
Interact with the STRING protein-protein interaction database API.
Highrise by CData
A read-only MCP server for Highrise, enabling LLMs to query live data using the CData JDBC Driver.
PyAirbyte
An AI-powered server that generates PyAirbyte pipeline code and instructions using OpenAI and connector documentation.
Pinterest by CData
A read-only MCP server for querying live Pinterest data, powered by the CData JDBC Driver.
CData MYOB AccountRight
A read-only MCP server for MYOB AccountRight, enabling LLMs to query live data using the CData JDBC Driver.
Supabase
Manage your Supabase project, execute SQL queries, and more.
Engram
Persistent memory layer for AI agents with semantic search, consolidation, and cross-session intelligence via MCP.
AWS Athena MCP Server
An MCP server for querying and interacting with AWS Athena.
mem0-mcp-selfhosted
Self-hosted mem0 MCP server for Claude Code. Run a complete memory server against self-hosted Qdrant + Neo4j + Ollama while using Claude as the main LLM.