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AIData.Broker

by broker.aidata
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Search published AI training data prices

search_prices
Read-onlyIdempotent

The individual price observations behind the market rates: what a lab, vendor, marketplace or court said data was paid, offered, listed or sold for, with the figure as published, the figure per unit, the date, the source's own words and a link. Newest first.

Input Schema

NameRequiredDescriptionDefault
kindNoThe kind of data: company (Company records), code (Source code), video (Video and footage), audio (Audio and speech), images (Images), text (Text and archives), environment (Datasets, tasks and environments), physical (Physical-world data), people (Work by people), products (Data products).
sideNoThe side of the market: pay (Paid to the people who make it), offer (Offered to holders), licence (Sales that happened), listing (Asking prices off the shelf), collection (Made to order), cost (Cost to produce).
limitNoHow many rows to return (default 50, at most 50).
queryNoWords that must all appear, such as a company ("Shutterstock"), a language ("hindi") or a kind of record ("egocentric").

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / kind / description
      Previous value: -"The kind of data: company (Company records), code (Source code), video (Video and footage), audio (Audio and speech), images (Images), text (Text and archives), environment (Datasets, tasks and environments), physical (Physical-world data), people (Work by people)."New value: +"The kind of data: company (Company records), code (Source code), video (Video and footage), audio (Audio and speech), images (Images), text (Text and archives), environment (Datasets, tasks and environments), physical (Physical-world data), people (Work by people), products (Data products)."
    • changedInput schema / properties / kind / enum
      Previous value: -[
-  "company",
-  "code",
-  "video",
-  "audio",
-  "images",
-  "text",
-  "environment",
-  "physical",
-  "people"
-]New value: +[
+  "company",
+  "code",
+  "video",
+  "audio",
+  "images",
+  "text",
+  "environment",
+  "physical",
+  "people",
+  "products"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"How many rows to return (default 10, at most 50)."New value: +"How many rows to return (default 50, at most 50)."
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish that this is a safe, read-only, idempotent lookup. The description adds useful behavioral context beyond annotations: results are newest first and include the published figure, per-unit figure, date, source wording, and link.

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?

The description is a single dense sentence that front-loads the core purpose and return contents. It is efficient, though the sentence is long enough that slight structural trimming would improve scanability.

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?

With no output schema, the description carries the burden of explaining returned values, and it does so by naming the published figure, per-unit figure, date, source quote, and link. Ordering is also disclosed, so an agent has enough context to call and interpret the tool correctly.

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%, and the enum descriptions fully explain kind and side. The description does not add parameter-level meaning beyond the schema, so the baseline score of 3 is appropriate.

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?

The description states a specific resource: individual price observations behind market rates, including what was paid, offered, listed, or sold. It clearly distinguishes this from aggregate market-rate data, which is the relevant sibling contrast.

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

Usage Guidelines3/5

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

The phrase 'behind the market rates' implies this tool is for underlying evidence rather than aggregates, but it does not explicitly say when to use it instead of get_market_rates or other price-related siblings. Usage is inferable but not well guided.

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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