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Agent Data API

run_data_tool

Run one of our 70 data tools (Google Maps places, YouTube transcripts/videos/comments, Indeed / LinkedIn / 13-board jobs, Google Ads library, Google News and Trends, TikTok comments, Pinterest, Airbnb, Redfin, AliExpress, app stores, website contacts, SEO and domain checks, sanctions, clinical trials...) with its JSON input. list_tools_and_prices (free) shows each tool's input example. Price: $0.01 per call (x402, USDC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
inputYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It usefully discloses the $0.01 per-call price and x402/USDC payment mechanism, but does not state what the tool returns, how invalid tool/input combinations are handled, or whether calls are idempotent.

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?

Front-loads the action and resource, then adds discovery path and pricing. The long parenthetical category list is dense but informative; nothing is seriously wasted.

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

Completeness3/5

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

For a meta-dispatcher with a nested input object, no output schema, and no annotations, the description covers discovery and cost but leaves return values and failure behavior unstated. Adequate to start, but not fully complete for reliable invocation.

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 0%, so both parameters depend on the description. It explains that "input" is the selected tool's JSON input and directs agents to list_tools_and_prices for examples, which partially compensates, but it does not describe the tool enum or the nested input shape beyond that.

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?

States a specific verb ("Run") and resource ("one of our 70 data tools"), enumerates representative categories, and distinguishes itself from list_tools_and_prices by being the paid execution path rather than the free listing path.

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?

Clearly points agents to list_tools_and_prices to discover each tool's JSON input example, which is essential context before calling this dispatcher. It stops short of explicit when-not conditions or error-handling guidance, but the primary usage path is clear.

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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