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Find buyers of a kind of AI training data

find_buyers
Read-onlyIdempotent

Organisations with a public way to sell them data, with their own published terms: what they want, what they pay, when they pay, who removes personal data, eligibility, and what they pay an introducer. Largest published figure first.

Input Schema

NameRequiredDescriptionDefault
kindNoThe kind of data: company-records (Company records), source-code (Source code), video (Video and footage), audio-speech (Audio and speech), images (Images), text-archives (Text and archives), datasets-environments (Datasets, tasks and environments), physical-world (Physical-world data), expert-work (Expert and human work).
limitNoHow many rows to return (default 10, at most 50).
queryNoWords that must all appear in the buyer's name or terms.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, covering the safety profile. The description adds one genuine behavioral trait beyond that: results are ordered 'largest published figure first', which is not derivable from annotations.

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

Conciseness3/5

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

It is a single sentence with no filler, but it is a dense, slightly awkward run-on that crams the returned field list into one clause rather than front-loading the core action. Informative but not well structured.

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 carries the burden of describing return content and does so by enumerating the term fields returned plus sort order. It is largely complete for a read-only finder, though it omits any notion of result shape or pagination behavior.

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 description coverage is 100%, so kind, limit and query are fully documented in the schema. The description adds nothing about parameter semantics, so the baseline 3 applies.

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?

The description states a specific resource and scope: organisations that publicly buy data, along with the term fields returned (what they pay, eligibility, introducer fee). Combined with the title it is clear what the tool returns, but it does not differentiate from siblings like search_deals or get_market_rates.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no named alternative among the siblings. The usage is only implied by the resource described (buyers), leaving the agent to infer when this beats get_market_rates or search_deals.

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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