In-memory vector store with TF-IDF vectorization and cosine similarity search. Pay-per-call via x402 (USDC on Base L2) -- no API key, no signup, no rate-limit wall.

Part of the klymax402 marketplace -- 100 x402 micropayment APIs for AI agents, one wallet, USDC on Base.

Quickstart -- MCP

Add to your MCP client config (Claude Desktop, Cursor, ElizaOS, etc.):

json

{
  "mcpServers": {
    "vector-search": {
      "url": "https://vector-search.api.klymax402.com/mcp"
    }
  }
}

Quickstart -- HTTP (x402)

bash

curl -X POST "https://vector-search.api.klymax402.com/api/search" \
  -H "Content-Type: application/json" \
  -d '{"query":"..."}'
# -> 402 Payment Required, with an x402 payment challenge in the response body

Any x402-aware client (@x402/fetch, x402-agent-tools, ATXP) handles the 402 -> sign -> retry cycle automatically.

Tools

Tool	Method	Path	Price	Description
`data_vector_search`	POST	`/api/search`	$0.012	Store text and search by semantic similarity

`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 similarity to find the most relevant matches. Returns top-k results with similarity scores. Do NOT use for web search — use web_search_query instead. Do NOT use for keyword research — use keyword_research instead. Do NOT use for text classification — use text_classify instead.

Parameters

Name	Type	Required	Description
`documents`	array	no	Array of text documents to index (optional if already stored in this session)
`query`	string	yes	The text query to search for
`topK`	number	no	Number of top results to return (default: 3, max: 10)
`namespace`	string	no	Namespace to isolate document sets (default: 'default')