Chroma (Vector DB) MCP Server
Manage vector embeddings via Chroma — list collections, query embeddings, and audit document counts directly from any AI agent.
Vinkius AI Gateway prend en charge le streamable HTTP et le SSE.
Fonctionne avec tous les agents IA que vous utilisez déjà
…et tout client compatible MCP
Chroma MCP Server : voyez votre AI Agent en action
Capacités intégrées (7)
check_heartbeat
Validate fundamental network availability against explicit Chroma API nodes
count_documents
Execute explicit structural tracking enumerating total document volumes
get_collection
Identify bounded logical settings configuring a specific Vector Collection block
get_documents
Retrieve exact physical documents and semantic context inside known arrays
list_collections
List all explicitly defined Vector Collections within a given tenant database
peek_documents
Extracts explicitly attached bounded preview of the Database limits
query_embeddings
Identify precise logical bounds matching high-dimensional semantic clustering
Ce que ce connecteur débloque
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
1. Subscribe to this server
2. Enter your Chroma URL (Cloud or self-hosted) and your API Key
3. 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
Questions fréquemment posées
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