Pinecone MCP. Query embeddings & manage vector indexes from chat.

Listen up. This Pinecone MCP Server gives your agent full control over your vector databases. You're talking about querying embeddings, checking index health, listing collections, or deleting vectors—all done via natural language chat. It lets you manage complex knowledge graphs and run deep semantic searches without needing to write boilerplate code or jumping out of your IDE.

When you connect this server, you gain a suite of tools that let your AI client interact directly with Pinecone's operational layer. You don't just ask questions; you make database changes. Here’s what it lets you do:

Discovery & Mapping:
You can start by listing every single active vector index in your account using the list_indexes tool. This shows you a quick rundown of all the knowledge bases you've set up. If you need to see historical groupings, you run list_collections, which returns names of snapshot collections holding grouped versions of your data over time.

For any specific index you find, you can check its exact configuration—its schema, dimensions, and metadata requirements—by calling describe_index. This means you know precisely what structure the data in that index expects before sending anything to it.

Retrieval Operations:
Want to search for something? You use query_vectors. By passing an array of query embeddings, this tool finds the most semantically similar vectors and pulls back their associated metadata. It's not just a keyword match; it’s finding concepts that mean the same thing. If you already know the unique IDs of the records you need—maybe they came from another process—you skip the similarity search and use fetch_vectors to grab those specific vectors directly from an index.

Monitoring & Auditing:
You gotta keep track of your resources, right? To check how much capacity you're using or if you're running low on space, call get_index_stats. This pulls real-time usage metrics for any given index, giving you the vector count and pod capacity limits. This is crucial for knowing when you need to scale up your environment before things break.

Maintenance & Cleanup:
Sometimes you gotta clean house. If you find old or irrelevant data records cluttering an index, delete_vectors lets you perform surgical cleanups. You must confirm the vector ID and the collection name first; it’s a controlled deletion process so you don't mess up anything important.

Essentially, your agent doesn't just read from Pinecone; it operates within it. It maps out the structure with list_indexes and list_collections. It validates the schema using describe_index. It executes complex searches with query_vectors or retrieves known data points via fetch_vectors. It tracks usage limits by pulling stats with get_index_stats. And it keeps things tidy by running cleanup jobs with delete_vectors.

You get full, structured access to your entire vector database environment.