Vector Search API

io.github.Br0ski777Published by GitHub account Br0ski777v1.1.0Updated Nov 2, 2026

In-memory vector search with TF-IDF and cosine similarity. x402 micropayment.

VerifiedSSEWeb executableFinanceKnowledge & MemoryAI & ML

Overview

AI-generated overview

Remote MCP endpoint that stores text documents and searches them by TF-IDF cosine similarity, paid per call in USDC via x402.

What you can do
  • Store text documents in a namespace and search them by similarity
  • Run a query against indexed documents and get the top-k matches with similarity scores
  • Isolate separate document sets using namespaces
  • Pay per call through an x402 challenge instead of an API key
When to use it
Useful when an assistant needs a lightweight, hosted vector store for storing a small set of texts and retrieving the closest matches, without signing up for an account or managing its own embedding stack.
Requirements
A remote MCP endpoint (SSE/HTTP) at the provider's host, so network access is required. No API key or signup, but calls are paid per request in USDC on Base L2 through the x402 protocol, so an x402-capable client and a funded wallet are needed.
Before you install
Every call costs money: the listed search tool is priced at $0.012 per request in USDC on Base, so automated loops can spend funds. Payment is handled by a wallet through x402, and the provider's facilitator (Coinbase CDP, with PayAI as fallback) processes the payment challenge. Documents sent for indexing go to a third-party service, and the store is described as in-memory, so persistence should not be assumed.

Installation

In SourceWeft

  1. Open Vector Search API in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via SSE. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.Br0ski777%2Fvector-search/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

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

README

Vector Search API

[MCP Server] [x402] [License: MIT]

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

ToolMethodPathPriceDescription
data_vector_searchPOST/api/search$0.012Store 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

NameTypeRequiredDescription
documentsarraynoArray of text documents to index (optional if already stored in this session)
querystringyesThe text query to search for
topKnumbernoNumber of top results to return (default: 3, max: 10)
namespacestringnoNamespace to isolate document sets (default: 'default')

Example agent prompts

  • "Store text documents and search them by semantic similarity"

Payment

  • Protocol: x402 -- HTTP-native pay-per-call, no signup, no API key
  • Network: Base L2 (eip155:8453)
  • Asset: USDC
  • Facilitator: Coinbase CDP (primary), PayAI (fallback)

Part of klymax402

100 x402 micropayment APIs for AI agents -- one wallet, USDC on Base, zero signup.

License

MIT

Source: README.md at commit 4d6e0a9

Tools

0
SourceWeft has not read this server's tools yet.

Version history

1
  1. v1.1.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declaredRequired10 pts

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  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts