agenstry.com

Turn a natural-language task into a structured execution plan.

Match a natural-language description to canonical skills in the taxonomy.

Search A2A agents by freeform query (hybrid BM25 + semantic + quality).

Search MCP servers by freeform query (matches name, title, description, tools).

(Alias of find_agent.) Hybrid search across A2A agents.

Get full details for a specific agent — card, capabilities, conformance signals.

Aggregate statistics about the Agenstry index — A2A + MCP funnel breakdowns.

Like compose, but Agenstry also fans out the plan and calls each step's selected agent for you.

Per-canonical-skill market shape: number of agents serving it, price distribution (min/median/max), quality distribution, growth 30d, top providers ranked by quality.

Per-provider portfolio + composite trust signal: every agent under one LEI / provider_org, avg quality, verified-card count, signed-card count, jurisdictional metadata, aggregate on-chain revenue across the portfolio.

Time-series of the agent-economy revenue stream: daily gross + tx + earning-agent count for the requested window (7-180d), weekly cohort table by first-seen date, and per-protocol split (x402 vs ap2 vs stripe_acp vs l402).

Per-wallet portfolio of every indexed agent that settles into this address.

Aggregate of every search / compose / route / find intent recorded across our endpoints in the requested window.

Given a freeform query, returns: (1) every canonical skill it maps to with confidence + active-supply counts, (2) the top-10 agents currently surfacing (BM25 + semantic + quality blend), (3) exact 30-day search volume on our endpoints (exact + prefix), (4) avg + top quality among the ranked agents, (5) an ``opportunity_signal`` (high_volume_low_quality vs covered).

Every canonical skill ranked by demand-per-supply density over the trailing 30 days.

For one agent domain, return the full 30-day caller-attribution breakdown captured by our universal call log: which AI hosts called it (Claude Desktop / Cursor / OpenAI Responses / ChatGPT / n8n / curl / browser), which A2A peer agents called it, caller geography (ISO-2 country), surface mix (a2a / mcp / rest / web / managed_runtime), and the top search intents that surfaced it.

Compact top-line numbers for the agent-economy money flow: total gross USDC across all indexed agents (30d), tx count, count of earning agents, and a 3-name teaser of the leaderboard.

Comprehensive on-chain x402 USDC flow snapshot — the data behind the public /flows dashboard.

Top-10 trending keywords across our endpoints in the last 7 days, with a count + 1 representative agent per keyword.

Rich detail snapshot for one agent: card, drift history (last 20 hashes), full verification timeline, current LEI status, capability vector, on-chain payment metadata.

Per-agent observability: uptime over the last 30 days, p50/p95 response time, on-chain x402 USDC revenue across 7/30/90 day windows, drift snapshot count, verification timeline.

Acquisition surface for the paid ``keyword_search``.

Acquisition surface for the paid ``supply_gaps``.

Given an agent domain, returns the top-N agents nearest to it in the embedding space (cosine over unit-norm vectors).

Given an MCP qualified name, returns the top-N MCP servers nearest to it in the embedding space, with a `via` flag so the caller can tell whether the result came from vec0 cosine or the token-overlap fallback.

Given a canonical-skill id, returns the top-N related canonical skills with cosine similarity scores.