Chroma MCP
Local and Chroma Cloud vector database access for retrieval tasks and RAG workflows.
Delv Safety Grade: A
Score 84/100 · assessed 2026-04-28
Chroma MCP is the official server from Chroma's core team, giving it strong maintainer credentials and alignment with the database's roadmap. The server provides both local and cloud vector database access, which means it handles network connections and database writes. Permissions are reasonably scoped to database operations rather than arbitrary filesystem or shell access. Distribution via uvx and PyPI follows standard Python packaging practices, though the relatively new MCP ecosystem means less battle-testing than mature packages. Documentation is clear and the repository is active with responsive maintainers. No known security incidents. The main risk surface is the dual local/cloud model requiring network access and the write capabilities inherent to database operations, but these are appropriate for the stated use case of RAG workflows.
Lethal Trifecta (prompt-injection exposure)
ONE OF THREELocal vector DB. Private content.
Green flags
- Official vendor implementation from Chroma core team
- Active repository with responsive maintainers
- Clear documentation and usage examples
- Standard PyPI distribution via uvx
- Scoped to database operations, no shell or filesystem access
Red flags
- Network access required for Chroma Cloud connections
- Database write permissions allow data modification and deletion
- Relatively new MCP server with limited production track record
Permissions requested
Install
uvx chroma-mcp
Review
Install this if you're building RAG systems with Chroma and want Claude to query your vector stores directly. Skip it if you're just testing embeddings concepts or don't need a persistent database. It's a solid, official connector that does exactly what it claims.
Good at
- Official server from the Chroma team, so it stays current with database features and API changes.
- Works with both local Chroma instances and Chroma Cloud, giving you flexibility in deployment.
- Exposes collection management and query tools cleanly, no need to write HTTP client code in prompts.
- Lightweight and fast when paired with a well-configured Chroma instance.
- Supports metadata filtering and multiple distance functions, so you can tune retrieval behaviour.
Watch out
- Doesn't handle document chunking, embedding, or ingestion. You must populate Chroma yourself before querying.
- Requires a running Chroma instance, which adds a dependency if you're not already using it.
- Learning curve if you're unfamiliar with Chroma's collection model and query syntax.
- No built-in tools for managing embeddings lifecycle (updating, deleting stale docs) from Claude's interface.
Use cases
- vector search
- knowledge base Q&A
- RAG experiments
- persistent embeddings store
Getting started
Works with
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