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aimarket-mcp

aimarket-mcp

by alexar76
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

market_search

Read-onlyIdempotent

Find executable AI services on an AIMarket hub by describing a task in plain words, with results ranked by intent match, trust, success rate, latency, and price; use before invoking a service.

Instructions

Find services an agent can run on an AIMarket hub (default https://modelmarket.dev) for a task described in plain words, e.g. weather at a place, VAT check, verifiable random number. Use it before market_invoke; for facts on the open web use web_search. Results are ordered by how well they match the intent (multilingual), and close matches by quality: the publisher's trust (0–1 reputation from the LUMEN oracle), 30-day success rate, latency and price. Each line gives capability_id, price per call in USD, product_id, trust and, for federated listings, source_hub — pass those to market_invoke. Free and read-only: no wallet or key. The hub lists only what it can execute, so an empty result means it does not sell that.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "intent": "<intent>"
    },
    "name": "market_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many matches to return, 1 to 25 (default 10).
intentYesThe task in plain words, any of EN/RU/ES/FR/ZH, up to 500 characters, e.g. 'air quality in Paris now' or 'check an EU VAT number'.
categoryNoOptional exact category to narrow results, e.g. 'security'; empty to search all.
max_price_usdNoOptional budget in USD per call; listings priced above it are left out, e.g. 0.01. 0 means no budget.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.4

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, open-world, idempotent and non-destructive, and the description still adds real operational context: free, no wallet or key required, multilingual intent matching, and the exact ranking signals (publisher trust 0–1 from LUMEN, 30-day success rate, latency, price). It also previews the per-line fields needed to chain into market_invoke.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action and the sibling routing, then ranking and output shape. Some sentences are dense multi-clause runs, but no sentence is filler and each adds either routing or chaining information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, yet the description still supplies the one thing an agent needs for chaining — which fields to pass to market_invoke — plus the semantics of an empty result. For a four-parameter, open-world search tool this leaves nothing material missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description goes further by clarifying that intent is plain-language and multilingual and by naming the result fields (capability_id, price, product_id, trust, source_hub) that the agent must carry forward. Budget and category semantics remain solely in the schema, so it is not a full 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Find services an agent can run on an AIMarket hub') plus concrete example tasks (weather, VAT check, random number), which makes the search-vs-invoke distinction obvious without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly positions the tool relative to both siblings: 'Use it before market_invoke' and 'for facts on the open web use web_search'. It also tells the agent how to interpret a negative outcome ('an empty result means it does not sell that'), so there is no ambiguity about retrying.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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