Typesense Vector Search Connector for AI agents.
6 live capabilities
Manage vector embeddings and semantic search indexes directly.
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Why people use Typesense Vector Search
Typesense Vector Search for Faster RAG Indexing
With this Connector, that cycle stops. You just tell your agent what needs to change, and it handles the indexing, schema updates, and deletions. You get a functional database that stays in sync with your requirements without the overhead.
What Vinkius changes
You get a direct line from your chat interface to your vector database.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Fixing a broken RAG index
A developer asks the agent to delete a faulty document and re-index a corrected JSON payload using `delete_document` and `index_document`.
- Real-world use case 02
Rapid prototyping of new categories
An app builder creates three new collections for different product categories in minutes using `create_collection`.
- Real-world use case 03
Data auditing and verification
A data engineer asks the agent to list all collections and check the schema of the main knowledge base using `list_vector_collections` and `get_collection_details`.
Complete set · 6capabilities
The complete Typesense Vector Search capability set.
These are the exact actions your AI can choose when you ask it to work with Typesense Vector Search.
01—03
3 capabilities in this set.
Part of 6 available through Typesense Vector Search.
- 01 Capability
Search vectors
Run a similarity search using a vector query and optional text filters. This helps you find relevant data quickly.
- 02 Capability
Create collection
Build a new search collection with a custom JSON schema. Use this to set up new datasets for your AI agent.
- 03 Capability
Delete document
Permanently remove a document from a collection using its unique ID. This is useful for cleaning up old data.
04—06
3 capabilities in this set.
Part of 6 available through Typesense Vector Search.
- 04 Capability
List vector collections
See a list of all collections currently in your Typesense instance. This gives you a quick overview of your setup.
- 05 Capability
Get collection details
View the schema and metadata for a specific collection. Use this to verify your data structure is correct.
- 06 Capability
Index document
Add or update a JSON document within your search collection. This lets you push new data without writing code.
Set up in minutes
One URL. Then ask Typesense Vector Search to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Typesense Vector Search from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Typesense Vector Search, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Typesense Vector Search for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Typesense Vector Search URL.
- Step 03
Save and start
Save the connection and enable Typesense Vector Search in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"typesense-vector-search": {
"url": "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Typesense Vector Search
Open Agent mode in chat and ask: "Using Typesense Vector Search, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"typesense-vector-search": {
"url": "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Typesense Vector Search
Ask Copilot: "Using Typesense Vector Search, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"typesense-vector-search": {
"url": "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Typesense Vector Search
Open Cascade and ask: "Using Typesense Vector Search, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"typesense-vector-search": {
"url": "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Typesense Vector Search
Ask Cline: "Using Typesense Vector Search, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add typesense-vector-search --transport http "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Typesense Vector Search
Ask Claude: "Using Typesense Vector Search, show me...". 6 tools are ready
Where the request belongs
Work Typesense Vector Search can move forward.
This is for the developer who's tired of manually syncing embeddings or the data engineer who needs to fix a broken RAG index without opening a terminal.
AI Application Builder
Creates and manages semantic collections for production RAG apps during development.
Data Engineer
Manages large-scale document ingestion and vector indexing for knowledge bases.
Backend Developer
Performs sanity checks on relevance scores and schema mappings to ensure data integrity.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsOpenSearch Vector
Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.
Qdrant
Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.
Elasticsearch Vector
Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.
Chroma (Vector DB)
Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.
Marqo AI (Vector Search & Embeddings)
Manage semantic search via Marqo. execute tensor queries, index JSON documents, and audit vector indices.
Zilliz Cloud
Manage vector collections and perform similarity searches via Zilliz Cloud.
Bring your own AI
Change the model, client or framework. Keep Typesense Vector Search connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Typesense Vector Search.
The practical details behind the request, access and result.
Can the Typesense Vector Search MCP create new collections?
Yes, it can provision new collections with specific schemas. You just describe the fields and embedding dimensions to your agent, and it handles the creation for you.
How do I update a document in my vector index?
You can just tell your agent to update a document using its ID and provide the new JSON data. The Connector will handle the update automatically.
Can I run hybrid searches with this?
Yes, it supports combined text-filtering and vector similarity queries. This allows you to narrow down results by category or name while still using semantic search.
Is this for managing my embeddings?
This Connector manages the indexing, storage, and retrieval of your embeddings. It connects your AI agent directly to your existing Typesense vector database.
Can I delete specific records using the Typesense Vector Search MCP?
Yes, you can ask your agent to permanently remove a specific document from any collection by providing its unique ID.
Does this work for RAG systems?
It's a perfect fit for RAG. It allows you to manage your knowledge base documents and perform the actual semantic searches that power RAG applications.
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.
One connection away
Give your agent a direct line to Typesense Vector Search.
Connect Typesense Vector Search once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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