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AIData.Broker

by broker.aidata
OverviewDiscussionsAdmin

Market rates for AI training data

get_market_rates
Read-onlyIdempotent

Median, middle half and range per comparable unit (per hour of video or audio, per image, per task, per company, per title, and more) for each kind of AI training data and each side of the market, computed from published prices. Without kind, returns every rate the evidence supports a median for.

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).

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. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish that this is a read-only, idempotent, non-destructive, closed-world operation. Beyond that, the description adds meaningful behavioral context: it reports aggregate statistics computed from published prices, describes the comparable units, and explains what happens when `kind` is omitted. It does not contradict the annotations.

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?

The description is two sentences with no wasted text. It front-loads the core purpose and uses the second sentence to explain default behavior when `kind` is absent.

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 explaining return content, and it does so by naming the statistics and comparable units returned. It is nearly complete for a read-only rates query, though it does not specify response structure, pagination, or how sparse evidence is represented.

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%, with both `kind` and `side` fully enumerated and described in the input schema. The description reinforces that `kind` and `side` segment the rates and notes the omitted-`kind` behavior, but it adds little parameter detail beyond the already rich schema.

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 clearly states the tool returns market-rate statistics (median, middle half, range) for AI training data, broken down by kind and market side, computed from published prices. It is specific about the resource and output measures, but it does not explicitly distinguish this from sibling tools such as search_prices or value_data.

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 description explains the default behavior when `kind` is omitted, which is useful usage context. However, it does not state when to use this tool versus alternatives like search_prices, find_buyers, or value_data, so the agent must infer the selection criteria.

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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