Typesense Vector Search MCP. Run semantic searches across indexed knowledge bases.
Typesense Vector Search lets your AI agent perform complex semantic searches and manage vector data entirely through conversation. Index documents, create new collections with specific schemas, and run combined text-filtering queries without writing a single line of API code.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Run vector similarity searches combined with text filters using search_vectors.
List all existing collections or retrieve the detailed schema for a specific collection using list_vector_collections and get_collection_details.
Instantly provision new vector search datasets with custom schemas via create_collection.
Add or update JSON documents in a collection using index_document, bypassing manual REST calls.
Permanently delete specific records from a collection by ID using delete_document.
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What AI agents can do with Typesense Vector Search: 6 Tools Available
These tools give your agent the power to read, write, and structure data within your vector database using conversational commands.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Typesense Vector Search MCPSearch Vectors
Runs a combined search that uses both text filters and numerical vector similarity queries.
Create Collection
Builds a new search dataset by defining its specific required schema using JSON...
Delete Document
Removes a document from any collection, and this action cannot be reversed.
Get Collection Details
Retrieves the full schema definition and metadata for a specific data collection.
Index Document
Adds or updates an existing document in a collection by providing its name and JSON...
List Vector Collections
Fetches a list of every available vector collection within the current Typesense instance.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Typesense Vector Search integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Typesense Vector Search, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Typesense Vector Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
The pain of manual vector management
Today, managing a semantic database means jumping between documentation, writing API wrappers in Python, and manually constructing complex JSON payloads for every single operation. If you need to index 50 documents, that's 50 separate calls. If you change the schema geometry, it’s another code deployment cycle just to confirm the fields are correct.
With this MCP connection, those manual steps vanish. You simply tell your agent what needs doing—like 'Add these records and make sure they fit the existing structure.' The system handles the API calls for `index_document` and validates the process without you ever touching a terminal command or writing a single line of boilerplate code.
Get full control with Typesense Vector Search
You no longer need to manually run `list_vector_collections` just to see what datasets exist, nor do you have to worry about schema mismatches. The agent surfaces the available collections and their definitions for you.
What's different now is that your AI client treats the entire vector database like a single API endpoint accessed via natural language. It’s immediate, conversational control over complex data infrastructure.
What Typesense Vector Search MCP does for your AI
Connect this MCP to any compatible client to take autonomous control over your vector database. Instead of constructing CURL payloads or writing custom Python scripts for every query, you talk to your agent about what data you need. You can ask it to list all existing collections, create a new schema dataset with specific embedding structures, and immediately begin indexing documents by simply providing the JSON payload.
The system handles the complex API calls in the background. This ability to manage vector storage—from creating schemas via create_collection to running advanced queries using search_vectors—means your agent becomes a full-time data engineer for your knowledge base. When you connect this through Vinkius, you get access to a robust set of tools that lets your AI client do all the heavy lifting on indexing and retrieval.
019d7617-527a-71ad-a8a6-4ab8bb65c437 
How to set up Typesense Vector Search MCP
The bottom line is that you talk naturally to your agent, and it handles the entire back-end process of querying and managing your vector database for you.
Subscribe to this MCP and provide your Typesense Host URL along with an Admin API Key.
Instruct your AI agent to perform the desired data operation, like listing available collections or running a search query.
The agent executes the necessary tool calls through the connection, returning the requested data or confirmation status.
Who uses Typesense Vector Search MCP
This MCP is for people who build complex AI applications using proprietary data. It targets the Data Engineer who needs to manually ingest reference documents into a running collection, or the Backend Developer building RAG pipelines that require precise schema validation and multiple types of search.
Needs to manually add missing knowledge base records or validate schemas for new data sources before they are used by the AI.
Builds conversational agents that need to perform advanced, filtered semantic searches over proprietary document sets at runtime.
Writes and tests the core indexing logic for a service, needing an easy way to perform sanity checks on relevance scores or collection geometry.
Benefits of connecting Typesense Vector Search MCP
You skip complex REST calls. Instead of writing code to index_document, you just ask your agent to update the data, and it handles the payload transfer automatically.
Advanced querying is simple. With search_vectors, you can combine natural language text filters with vector similarity results in a single chat command.
Schema management becomes conversational. Use get_collection_details to verify field geometries or create_collection to deploy a new, structured knowledge base instantly.
You maintain full control over your data lifecycle. Easily run list_vector_collections to map out every dataset you have, and use delete_document when a record is stale.
The entire process runs through your agent's chat window. You don't need dedicated terminal access or manual script execution for basic document mutations.
Typesense Vector Search MCP use cases
Updating Product Catalogs
A product manager wants to update pricing and descriptions across 50 old records in the inventory collection. Instead of writing a batch script, they prompt their agent: 'Update all products matching category X with the new JSON payload.' The agent executes index_document for each record automatically.
Debugging Retrieval Failures
A developer notices some search results are poor. They use get_collection_details to check the schema geometry, ensuring that their new field is mapped correctly and doesn't break the existing vector structure.
Building a New Knowledge Domain
A team needs a separate index for legal documents. They use create_collection to build the specialized schema, then run list_vector_collections to confirm the new dataset is ready before they start indexing.
Complex Research Queries
A researcher needs results about 'AI ethics' that were written specifically in 2023. They use search_vectors, combining a text filter ('year: 2023') with the vector query, getting highly relevant and narrow answers immediately.
Typesense Vector Search MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manual API scripting
Writing multi-step Python code or CURL commands every time you need to index a document, check a schema, or run a query.
Let your agent handle it. Use index_document for updates, get_collection_details for validation, and search_vectors for the final results—all in conversation.
Assuming schema compatibility
Running a search query against a collection that hasn't been properly initialized or whose field definitions were changed manually.
Always start by calling list_vector_collections and then use get_collection_details to confirm the exact geometry before making any changes.
Overwriting data blindly
Executing an update without confirming that the document ID exists or if the new JSON payload actually contains valid embedding vectors.
Use get_collection_details first. Then, run a test search using search_vectors before committing to any updates via index_document.
When to use Typesense Vector Search MCP
Use this MCP if your core workflow involves semantic retrieval from complex, proprietary data and you need conversational control over the indexing process. This is essential for building advanced RAG systems where the search criteria must combine natural language filters with dense vector similarity searches. Don't use it if your needs are limited to simple key-value lookups (like finding a user by ID) or basic database operations that don't involve semantic understanding—a standard relational tool would be better. You need this if you constantly have to manage schemas (create_collection) and ensure data fidelity across multiple, distinct knowledge domains.
Frequently asked questions about Typesense Vector Search MCP
How do I check what collections are available using Typesense Vector Search MCP? +
You ask your agent to list all vector collections. The tool list_vector_collections will immediately provide a roster of every dataset configured on your instance.
Can I create a new schema with the Typesense Vector Search MCP? +
Yes, you can use create_collection. You just need to supply the desired field structure as a JSON object, and the tool provisions the entirely new semantic dataset.
What is the difference between using search_vectors and indexing documents? +
search_vectors reads data; it executes complex queries combining text filters with vector similarity. index_document, conversely, writes data by adding or updating a record in a collection.
Is the action of deleting documents permanent using Typesense Vector Search MCP? +
Yes, the delete_document function permanently removes records by their ID. Treat this tool with care, as there is no undo feature for this operation.
Does the Typesense Vector Search MCP support combined search queries? +
Absolutely. The search_vectors tool lets you combine traditional text filtering with vector similarity searches in one single request.
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.