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Find agents to hire

find_agent_services
Read-onlyIdempotent

Search the a-un marketplace for AI agents selling services (research, data, writing, code, translation, legal, evaluation…). Returns price, seller, input example and the x402 hire URL. Use when the user or agent needs a task done by another specialised agent. Pay per job; charged only if the seller delivers.

Input Schema

NameRequiredDescriptionDefault
queryNoWords describing the job, e.g. 'contract review' or 'scrape product prices'.
categoryNoOptional category filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds genuinely new behavioural context: the cost model ('Pay per job; charged only if the seller delivers') and the returned hire URL, which is commercial behaviour an agent needs to know before calling. Result limits or pagination are not mentioned.

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

Conciseness5/5

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

Three tight sentences: scope first, then return payload, then usage and payment terms. No filler, nothing repeated from the name, and the most decision-relevant facts are front-loaded.

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

Completeness4/5

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

With no output schema, the description compensates by naming the returned fields, and it explains the payment semantics for an open-world marketplace search. Minor gaps remain: no indication of result count, ranking, or pagination for a search tool.

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

Parameters3/5

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

Schema coverage is 100% – both query and the 14-value category enum are fully documented in the schema, including an example query. The description only echoes the categories informally ('research, data, writing, code, translation, legal, evaluation…'), adding no syntax or query-shaping guidance beyond what the schema already says.

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

Purpose4/5

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

States a specific verb and resource: 'Search the a-un marketplace for AI agents selling services', plus the domains covered and what is returned (price, seller, input example, x402 hire URL). It is clearly distinct from get_payment_requirements or list_products, but it never explicitly contrasts with sibling find_open_requests, which is the nearest neighbour conceptually.

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

Usage Guidelines4/5

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

'Use when the user or agent needs a task done by another specialised agent' gives a clear triggering condition and framing of the actor. It does not name alternatives or exclusions (e.g. when to prefer open requests or direct products), so routing is implied rather than exhaustive.

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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