Can my agent perform semantic search across my collections?

Yes. Provide the vector embedding array in JSON format, and your agent will return the closest document matches along with their distance metrics. It is the perfect way to test your RAG (Retrieval-Augmented Generation) logic without complex scripts.

How can I verify the health of my self-hosted Chroma instance?

Simply ask your agent to check the heartbeat. The agent performs a nanosecond-level responsiveness test against your API nodes, confirming the physical database is active and reachable from the gateway.

I manage multiple tenants — how do I switch between them?

You can define the tenant and database names during the setup phase. If you need to switch often, you can update the credentials in the dashboard. The agent uses these values for all collection and document operations to ensure strict isolation.

Chroma (Vector DB) MCP Connector for Claude

Manage vector embeddings via Chroma — list collections, query embeddings, and audit document counts directly from any AI agent.

7 tools Official Updated Jun 28, 2026 Official Vinkius Partner

More Details Connect to Claude

Connect your Chroma vector database to any AI agent and take full control of your semantic data through natural conversation.

What you can do

Vector Collections — List all available collections and inspect their deep configuration and metadata
Semantic Search — Perform high-dimensional vector similarity searches to find relevant context for your LLM applications
Document Auditing — Count documents, peek at unstructured data segments, and retrieve specific records by ID
Instance Health — Monitor heartbeats and connectivity across Chroma Cloud or self-hosted instances
Tenant & Database Management — Switch between different tenants and databases to isolate your production and staging environments

How it works

Subscribe to this server
Enter your Chroma URL (Cloud or self-hosted) and your API Key
Start querying your embeddings from Claude, Cursor, or any MCP-compatible client

Who is this for?

AI Developers — test and debug vector search logic using natural language without writing Python scripts
Data Engineers — audit collection volumes and metadata consistency across different environments
Product Managers — inspect what context is being fed to AI agents by peeking at stored embeddings
DevOps Teams — monitor instance connectivity and health through automated heartbeats

embeddingssemantic-searchllm-infrastructurevector-searchdata-retrievalmachine-learning

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