Vector Search — In-Memory TF-IDF Semantic Store

In-memory vector search API for AI agents.

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What it can do

  • Data Vector Search: Use this when you need to store text documents and search them by semantic similarity. Accepts documents to store and a query to search. Uses TF-IDF vectorization with cosine simil

What data it sees

Do you need an account

No: the server works without sign-in

In-memory vector search API for AI agents. Store documents and query by semantic meaning using TF-IDF vectorization with cosine similarity. Lightweight alternative to Pinecone/Weaviate for small datasets.

Tools: data_vector_search.

Use this for building simple RAG systems, document matching, or semantic search over small collections (< 10K docs). IMPORTANT: For web-wide search, use web_search_query instead.

Returns: {results[], scores[], matchCount}. No API key required — x402 micropayment $0.005/call on Base L2.

How to connect

How to connect Vector Search — In-Memory TF-IDF Semantic Store to Claude

The server address to paste into Claude settings:

https://vector-search--axel-belfort.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Vector Search — In-Memory TF-IDF Semantic Store to ChatGPT

The server address to paste into ChatGPT settings:

https://vector-search--axel-belfort.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Vector Search — In-Memory TF-IDF Semantic Store to Cursor

The server address to paste into Cursor settings:

https://vector-search--axel-belfort.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (1)

Raw names from tools/list. Only developers need these.

data_vector_searchUse this when you need to store text documents and search them by semantic similarity. Accepts documents to store and a query to search. Uses TF-IDF vectorization with cosine similarity to find the mo