Connect Typesense Vector Search MCP for AI Agents
Semantic Document Retrieval and Knowledge Graph Building
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What AI agents can do with 6 Tools for Advanced Vector Search and Indexing
Use these tools to list collections, create schemas, index documents, and run complex similarity searches against your knowledge base.
Search vectors
Runs a combined vector similarity search, allowing you to filter results using text criteria alongside the semantic query.
Create collection
Creates an entirely new search collection by specifying its required schema details in a JSON object.
Delete document
Permanently removes a specific document from any collection using only its unique ID.
Get collection details
Retrieves the full schema and metadata for a specified collection, helping developers map fields correctly.
Index document
Adds new document data or updates existing records in a search collection using a JSON payload.
List vector collections
Generates a list of every available semantic collection within the Typesense instance.
Frequently Asked Questions
How do I use the Typesense Vector Search MCP to find specific documents? +
You tell your agent what you're looking for. It runs a combined search that uses both keywords and semantic meaning against your entire knowledge base, returning highly relevant matches.
I need to add new data to my vector database, how does the Typesense Vector Search MCP help? +
You just ask the agent. It handles the complex process of taking your JSON payload and ensuring it's correctly indexed into the target collection, saving you API work.
What if I need to change the structure or fields in my knowledge base? +
The MCP lets you manage schemas. You can ask the agent to provision a new collection with specific data types, or check existing structures using get_collection_details before making changes.
Can I clean up old records from my semantic search system? +
Absolutely. You instruct your agent to delete documents by ID, permanently removing them from the collection without needing manual database access or scripts.
Can the agent perform vector plus text-filtering search combined natively? +
Yes. Provide the agent with the collection name alongside the text payload and tell it the exact vector structure. It leverages internal filters querying natively and returns the nearest neighbors with exact accuracy scores.
How do I make the AI create a semantic collection ready for embeddings (OpenAI 1536 dims)? +
Ask the agent to use 'create_collection'. Provide standard JSON declaring the name, the field structure, and explicitly define the float[] field tracking the 1536 dims length. The cluster will spin the framework up instantly.
Can it delete problematic vectors holding bad geometry data manually? +
Absolutely. Supplying the explicit collection target and the item 'id' to the delete_document prompt securely wipes out all traces from the dataset. Use this sparingly as it can't be undone easily.
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