AgentIndex x402 MCP Discovery

x402

AgentIndex x402 MCP Discovery is a paid API for AI agents from x402.agentindex.world, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-30).

Semantically searches a snapshot of 10,000+ MCP servers to find the best-matching server for a plain-language capability need

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

Find MCP servers matching a need, ranked by semantic similarity over a curated snapshot of 10101 MCP servers. Returns name, endpoint, description, source registry and a 0-1 relevance per match. Paid POST: up to 25 matches. Try GET /discover/sample.

Output

Returns a JSON object containing the matched query string, the number of servers above the similarity threshold, and an ordered list of the top matching MCP servers — each with name, description, registry source, endpoint URL (if known), and cosine relevance score — plus snapshot metadata (date taken, total rows in snapshot).

Example use cases

Find PDF extraction MCP server

“I need an MCP server that can extract text from a PDF and convert it to markdown — find me the top 5 matches from the registry, with a minimum similarity of 0.35.”

Discover knowledge graph tooling

“Search the MCP server registry for something that does persistent knowledge graphs or graph databases — return up to 3 results.”

Locate browser automation agent tool

“Find me the best MCP servers for browser automation or web scraping — top 4 results, minimum similarity 0.40.”

Technical details

Endpoint

POSThttps://x402.agentindex.world/discover?utm_source=zero.xyz

When to prefer

Choose this endpoint when an AI agent needs to programmatically discover which MCP server best fits a particular capability need, rather than maintaining a hardcoded list of tools. It is especially useful for meta-agent architectures that dynamically select tools at runtime, or for agent builders exploring what MCP tooling exists for a given domain. Prefer it over manual registry browsing or hardcoded tool lists when the capability requirement is expressed in natural language.

Side effects

Query string too vague — returns low-relevance or zero results below the min_similarity threshold
min_similarity set too high — filters out all candidates and returns an empty results array
Snapshot may be days old — newly published MCP servers may not appear
Truncated description in crawled schema may cause unexpected input validation errors
Payment failure via x402/USDC on Base blocks the call entirely

Input

Schema · Request body

Field	Type	Description
q	string	The need to match, in plain language - e.g. "read a PDF and give me markdown" or "persistent knowledge graph". Matched against a curated snapshot of MCP servers (official registry + carnet) by semantic similarity.
max_results	integer	Maximum number of servers to return (default 5).
min_similarity	number	Minimum cosine similarity to include a result (default 0.30). Raise it for fewer, closer matches.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "q": "read a PDF and give me markdown",
    "matches": 3,
    "results": [
      {
        "url": "https://example.org/mcp/",
        "name": "PDF Extract MCP",
        "registry": "registre-mcp",
        "relevance": 0.7211,
        "description": "Extract a public PDF into clean markdown text, plus metadata and a real token count.",
        "observed_at": "2026-09-02T19:07:49.374328Z"
      },
      {
        "url": null,
        "name": "Docling Server",
        "registry": "annuaire",
        "relevance": 0.6487,
        "description": "Document conversion to markdown and structured text for LLM agents.",
        "observed_at": "2026-09-10T12:00:00Z"
      },
      {
        "url": "https://example.com/mcp/",
        "name": "Srclight",
        "registry": "registre-mcp",
        "relevance": 0.4102,
        "description": "Deep code indexing for AI agents. FTS5 + embeddings + call graphs. Fully local.",
        "observed_at": "2026-09-15T08:30:00Z"
      }
    ],
    "snapshot_date": "2026-09-18",
    "snapshot_rows": 10101,
    "min_similarity": 0.3
  }
}

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