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@ntlgnc

About Agentery

Free MCP for live and historical pricing for 13,000+ agents and MCPs. Never knowingly pay too much... or charge too little. Know the going rate. https://agentery.com.

Connection details

https://agentery.com/api/mcp

Setup

claude mcp add agentery --transport http https://agentery.com/api/mcp

Tools

19

START HERE for provider procurement. ONE call turns a task into: (1) its live MARKET — the semantic neighbourhood of the closest-matching providers, found purely by text-embedding nearness (NO fixed niche or category), with the relevance floor and how many providers cleared it; (2) current pricing context — comparable price range and median with mean, stdev and n, plus provider/priced counts; and (3) a ready-to-compare provider shortlist — each with observed price, market_position (below/in-line/above market), integration status, match score, and handles collected in `compare_ready`. Retrieval is 100% nearest-neighbour by text embedding: providers are matched on what they actually DO, never on an assigned label. REUSES the canonical pricing/search engines (no new pricing logic). Also returns `suggested_alternatives` (cheaper or stronger options) and a `result_fingerprint` (+ `cached`) so repeat calls are cheap. It does NOT run the comparison — pass `compare_ready` to compare_providers once you have finalists. Use the lower-level tools (find_market, market_report, price_benchmark, search_providers) for finer control. Aliases: `task` also accepts `query` / `q`.

Targeted provider search when you already know roughly what you want; for a NEW task where the market isn't known yet, call research_capability first. Filtered free-text search over the directory, ranked with match_score and match_reasons. Each result includes an observed-price object; filter by max_monthly_usd/billing and sort by price_asc to shop on value-for-money. Results include how_to_connect (website, docs, mcp.endpoint when the vendor publishes one) — the link/endpoint needed to actually use the listing; get_provider_profile has the full version with a copy-paste MCP config snippet. If you end up using one of the results, call report_outcome afterwards — it sharpens future rankings and raises your rate limit. Accepts `query` (aliases: q, text) — an unknown query key is never silently ignored. For a market + pricing + shortlist in ONE call, use research_capability first.

Map a natural-language task, capability or service to its live MARKET — the semantic neighbourhood of the closest-matching providers, found by text-embedding nearness (NO fixed niche or category). For buyers ('a provider that monitors competitor pricing'), sellers ('what should I charge for lead-generation automation') or sizing a space. Pricing-intent boilerplate is stripped before matching. Returns the market label, how many providers are in the neighbourhood and how many are priced, `nearest` (the closest providers with observed price and relevance/cosine), and `pricing_by_tier` — median, mean, stdev, p25/p75, min–max range and n per buyer tier (individual/pro/team_sme/enterprise), computed by the canonical pricing engine over the priced neighbourhood. match_certainty is 'confident' when real neighbours exist and 'uncertain' when nothing is close (pricing WITHHELD). Accepts `task` (aliases: query, q). For the full market read + shortlist in ONE call, use research_capability instead. Read-only.

Call this to decide between shortlisted providers. Inputs are resolved to REAL providers — exact handle, then exact display name — and are NEVER silently swapped for a fuzzy match: unknown inputs come back in `unresolved_inputs` with `suggested_matches` and a ready-to-retry `corrected_call`, and if EXACTLY ONE input is real (the other was invented/mistyped) it does NOT dead-end — it returns `comparison_status: compared_with_market_peers`, comparing the real provider against its actual in-market competitors — its nearest providers by text-embedding — (listed in `compared_against_peers`, with a `recovery_note`); only when ZERO inputs resolve does it return `comparison_status: insufficient_valid_providers`. When the compared providers are different delivery types it sets `mixed_provider_types` + a `comparability_warning` (a hosted agent and an MCP server are not directly equivalent). Full evidence-scored cards for 2-6 handles side by side, each with observed price, all-time community upvotes and provider type. Each card carries the full how_to_connect object (website, docs, MCP endpoint + config_snippet, A2A card, API) so you can act on the winner directly. Each card also carries `reported_success` — the machine-reported outcome rate from report_outcome (null until 5+ distinct correlated reporters in 90 days). Report your own outcome after using the winner. Accepts `provider_ids` (aliases: handles, ids; a comma-separated string is also accepted). Use after search_providers or research_capability; when a compared provider is over budget or weakly matched, inline `suggested_alternatives` are returned.

Call this for the CURRENT level of the Agent Economy Price Index (AEPI) — a chained like-for-like index over observed provider/MCP pricing (base 100 = 29 Jun 2026). It is an INDEX LEVEL, not a market price or tradeable asset. Returns the whole-economy headline index level with change_1d/change_7d/change_30d, as_of, like_for_like_pair_count, status and the methodology version, PLUS the same fields for the four buyer tiers (Individual, Pro, Team/SME, Enterprise). `provider_type` returns the standalone index for one delivery type (provider or mcp, own base 100) — agents and MCPs price and move differently. `tier` filters to one buyer tier; response_mode 'full' adds exact sub-0.01% moves and repricing counts. Reads the SAME canonical series as the /aepi page, so the MCP and website agree for a given timestamp. (Also accepts a `benchmark_id` to read a private custom benchmark's current index.) The economy index is whole-market by design — for pricing on a specific capability use market_report or price_benchmark.

Fair-price benchmarks for a capability described in natural language. The MARKET is the semantic neighbourhood of your query — the nearest providers by text embedding, with NO fixed niche or category — SEPARATED BY provider type (provider / mcp) and buyer tier (individual / pro / team_sme / enterprise). Each cohort reports median, mean, stdev, p25/p75, min–max range and n; a single blended provider+MCP median is NEVER the default. Supply provider_type and buyer_tier whenever the user makes them known (e.g. 'an individual MCP', 'a professional provider', 'enterprise'); when neither is known the tool returns the populated per-type/per-tier cohort matrix. Prices come from the canonical pricing engine run over the priced members of the neighbourhood, so the figures match the rest of the site to the digit. Pass `query` (the natural-language capability); `niche` and `task` are accepted as aliases for the query text. When nothing priced is semantically close it returns resolved:false with a note, never a fabricated figure.

Call this for the canonical DATED index SERIES (to chart or analyse movement) of the AEPI — the same chained like-for-like series the /aepi page plots. Every point is an index level (base 100), never a price. Returns the whole-economy headline series, or a single buyer tier's series when `tier` is set. `provider_type` returns the standalone 'agent' / 'mcp' series (own base 100). `period` selects '30d' (default), '90d' or 'all'. response_mode 'summary' (default) returns date + index_level points plus the window change; 'full' adds gap flags. Returns an honest status (insufficient_history) rather than a fabricated series when data is too thin. (Also accepts a `benchmark_id` to read a private custom benchmark's history.) The economy index is whole-market by design — for pricing on a specific capability use market_report or price_benchmark.

Find where real demand is going unmet — computed demand-first in the raw text-embedding space (NO niches). A gap = a cluster of real user requests (mined from live MCP search telemetry) that sits FAR from any PAID provider. For each gap it returns: the demand phrasing, demand_mass (how many similar requests cluster with it), nearest_paid_similarity (cosine to the closest paid provider — low = under-served) and that closest paid provider. Also returns demand_queries and paid_supply counts. Honestly returns few or no gaps while query volume is still low — it sharpens as usage grows. No arguments needed ({}); `limit` caps the list.

Call this when a shortlisted provider is too expensive, unreachable or a poor fit: substitutes for one known provider, ranked by text-embedding nearness to that provider's OWN profile — the closest-matching providers first (NO niche lookup) — each with observed price, endpoint liveness, community upvotes and how_to_connect (website, docs, mcp endpoint) so a substitute is immediately usable. Set cheaper_only to shop down from the subject's price. Accepts `agent_id` (aliases: handle, id). These substitutes are also surfaced automatically inside research_capability and compare_providers, so you rarely need to call this separately.

Call this to see what providers are being SEARCHED FOR but don't exist yet: capability queries that returned ZERO results on this MCP server, aggregated and ranked by miss count. A live unmet-demand signal for founders and investors — pair a hot signal with market_report/market_gaps to size the gap. Empty args ({}) return the current ranked unmet-demand list.

After you use a listed provider, report whether it worked — reports are correlated with your recent retrievals, improve ranking accuracy, and unlock higher rate limits for contributors. Only reports we can match to one of YOUR retrievals (search_providers / get_provider_profile / compare_providers / suggest_alternatives naming that provider, last 48h) carry weight; unmatched reports are stored but unweighted. Aggregates surface as `reported_success` on profile/comparison cards once 5+ distinct reporters exist (90-day window). Callers with 5+ correlated reports in 30 days get a doubled per-minute rate limit. Send an x-agentery-key header to keep one reporter identity across IPs (it is stored only as a hash).

PARTNER-ONLY (Bearer key required). Given a business context and its workflow steps, return ranked provider candidates for EACH step — structured, scored (match_score 0-100) matches with match_reasons and cautions. Built for app builders (e.g. Builtery) assembling automations. Reads each provider's analysed site profile; never invents capabilities; returns 'unclear' where evidence is missing.

Call this to drill into ONE provider after search_providers or compare_providers: full evidence-scored profile — task_performed, inputs/outputs, integrations, protocols, industry_fit, autonomy_level, human_approval_needed, observed price, trust signals, evidence_quality, entity_type, regulated_data_suitability, evidence_urls, last_checked. Includes the full how_to_connect object — website, docs, any vendor-published MCP endpoint (with a copy-paste client config_snippet), A2A agent card and API surface — the info needed to actually use the listing; fields are null when the vendor publishes no endpoint (never guessed). Also carries `reported_success` — machine-reported outcome rate from report_outcome (null until 5+ distinct correlated reporters in 90 days). If you use the listing, call report_outcome afterwards.

Call this for the public directory card of one provider by handle or registration number: bio, source URLs, X-verification status, entity type, community rating and structured profile when available.

Deep-dive ONE market before building or investing — the market is the semantic neighbourhood of your natural-language query (nearest providers by text embedding, NO fixed niche). Every field is MEASURED: the observed-pricing benchmark separated by provider type and buyer tier (median, mean, stdev, p25/p75, min–max range and n via the canonical pricing engine), how many providers are in the neighbourhood and how many are priced, and the top providers already competing there with their observed price and relevance. Pass `query` (a natural-language capability or market, e.g. 'customer support chatbot'); `niche` is accepted as an alias. For market + pricing + a ready shortlist in one call, use research_capability.

Create a PRIVATE custom benchmark (a saved, calculated peer cohort) over Agentery's data — no account needed. Two modes: (A) explicit members: pass `members` (a list of exact handles; product names/domains resolve where unambiguous). (B) fork a niche: pass `base_niche` plus optional `remove`/`add`. Returns a one-time secret `benchmark_id` (cb_… token) — store it; it's your only key. Use it later in get/update/delete and in market_report/get_price_index/get_price_index_history. Ambiguous names are returned as candidates, never silently resolved; unresolved inputs block creation unless allow_partial:true. All prices/history are computed from Agentery's immutable observations; canonical niches are never changed.

Get a private custom benchmark's current report: members, current stats (headline median/quartiles only when ≥3 comparable priced members — monthly, per-seat and per-call prices are never blended), buyer-tier / provider-type / pricing-unit cohorts, historical index, and data coverage. Pass `benchmark_id` (your cb_ token) as an ARGUMENT.

Add/remove members or rename a custom benchmark. Creates a NEW immutable version (the previous version stays fully reproducible) and returns the exact change-impact on the median/quartiles/index. Pass `benchmark_id`.

Disable access to a custom benchmark. Keeps only a minimal audit record; no underlying Agentery data is touched. Pass `benchmark_id`.

Overview

Agentery is a market-intelligence MCP that turns "what does this cost?" into structured, normalized data for commercial AI agents, MCP servers, and agent APIs. It tracks thousands of listings with observed pricing, so your assistant can research a market, compare providers on value, and monitor price movement — all through one public, no-auth endpoint.

What it does

  • Map a task to its nichefind_niche, research_capability (one-call procurement).
  • Compare normalized prices by buyer tier and billing unit (monthly / per-seat / per-call, FX-adjusted) — price_benchmark, compare_agents, search_agents.
  • Read the Agent Economy Price Index (AEPI) and its history — get_price_index, get_price_index_history.
  • Build private custom benchmarks — your own peer cohort with a secret token, historical reconstruction, and change-impact analysis.
  • Find gaps & demandmarket_gaps, demand_signals, niche_report.

19 tools, read-only except reporting your own outcomes. No API key required.

Connect

{ "mcpServers": { "agentery": { "type": "http", "url": "https://agentery.com/api/mcp" } } }

Frequently asked questions

What is the Agentery remote MCP server?

The Agentery remote MCP server is a hosted Model Context Protocol endpoint at https://agentery.com/api/mcp, so AI assistants can connect to it without installing or running anything locally.

How do I connect to the Agentery MCP server?

Add the endpoint https://agentery.com/api/mcp to any MCP-compatible client such as Claude Code, Cursor, or VS Code. The setup snippets on this page configure each client in one step.

Does the Agentery MCP server require authentication?

No. Agentery's MCP server does not require authentication — you can connect directly with the endpoint URL.

Which transport does the Agentery MCP server use?

Agentery exposes a Streamable HTTP endpoint, the transport used by remote MCP servers and supported by all major MCP clients.

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