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Search disclosed AI data licensing deals

search_deals
Read-onlyIdempotent

Licences of data to AI companies disclosed since July 2023: licensor, licensee, content, use (training or display), term, value where disclosed, with a verbatim quote and the source. Newest first.

Input Schema

NameRequiredDescriptionDefault
useNoWhat the data is used for.
limitNoHow many rows to return (default 10, at most 50).
queryNoWords that must all appear, such as a licensor, a licensee ("OpenAI") or a kind of content ("news").
sinceNoOnly deals on or after this date (YYYY, YYYY-MM or YYYY-MM-DD).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: results are ordered newest-first and coverage is bounded to disclosures since July 2023, which the annotations do not convey.

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?

A single dense sentence that front-loads the resource scope, then lists the returned fields and ordering, with zero filler. Nothing could be cut without losing information.

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 usefully compensates by enumerating the returned fields (licensor, licensee, content, use, term, value, verbatim quote, source) and the sort order. The remaining gap is usage guidance and result-volume behavior, which is only partly covered by the limit parameter in the schema.

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%, including enum values for 'use' and the date pattern for 'since', so the schema carries all parameter meaning. The description adds no syntax or filter-combination detail beyond what the schema already states, making the baseline 3 correct.

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 names a specific resource (disclosed AI data licensing deals) and enumerates the returned fields, so an agent knows exactly what kind of record set this is. It is distinguishable from siblings like search_cases and search_prices by resource, though it never uses an explicit verb to say it performs a search/lookup.

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 when-to-use statement, no prerequisites, and no mention of alternative tools (e.g. value_data or get_market_rates) for related licensing questions. Usage must be inferred entirely from the resource description.

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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