{
"mcpServers": {
"chroma": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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MCP server for Chroma
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
MIT. View license →
Is it maintained?
Last commit 462 days ago. 41 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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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.
The server provides document storage and retrieval through Chroma's vector database:
src/chroma/data directoryThe server implements CRUD operations and search functionality:
create_document: Create a new document
document_id, contentmetadata (key-value pairs)read_document: Retrieve a document by ID
document_idupdate_document: Update an existing document
document_id, contentmetadatadelete_document: Remove a document
document_idlist_documents: List all documents
limit, offsetsearch_similar: Find semantically similar documents
querynum_results, metadata_filter, content_filteruv venv
uv sync --dev --all-extras
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"
]
}
}
}
The server stores data in:
src/chroma/datasrc/chroma/datauv run chroma
# 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"
}
})
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]npx @modelcontextprotocol/inspector uv --directory C:/MCP/server/community/chroma run chroma