$ loading_
Connect AI assistants to ChromaDB for vector storage and semantic retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "py.chroma.mcp" yet — see the docs or source repo.
Use py.chroma.mcp to connect to my ChromaDB collection, store these product documents as paragraph-level vectors, and keep metadata like title, source URL, and updated time.
A summary of the ingestion result, including chunk count, collection details, and metadata fields.
Use py.chroma.mcp to search ChromaDB for the 5 most relevant items about 'best practices for enterprise knowledge base access control,' and include similarity scores and source details.
A ranked list of relevant matches with text snippets, similarity scores, and source references.
Using py.chroma.mcp, first retrieve context from ChromaDB related to 'how to configure a vector database backup strategy,' then organize it into reference material for answering a user question.
A well-structured set of supporting context materials ready for a downstream QA model.
Create a shared multilingual team memory with semantic search for MCP clients.
Index a knowledge base into Chroma and retrieve relevant document fragments.
Give LLMs persistent semantic memory and vector search for better context continuity.
Connect AI to MongoDB for querying, managing, and working with data.
Connect LLM apps to query and manage multiple databases through one tool.
Expose modular retrieval and reasoning tools to AI assistants through MCP.