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estimate_scrap

Estimate the cost (EF) and volume of a Google Maps extraction WITHOUT launching it. Always estimate before creating a large job. 1 EF = a full-France scrape for one query type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zonesYesCities, departments, regions or 'France'
countryNoISO3 country code, default FRA
queriesYesBusiness types, e.g. ['plombier', 'electricien']
stop_atNoStop after N unique places (recommended when the user gives a number)
scrap_modeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description must carry behavioral weight. It clarifies this is a non-executing estimation tool, which is crucial for safety, but doesn't mention rate limits, auth requirements, or whether estimates are cached. Adds key context (no launch) but leaves other operational details unstated.

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?

Two sentences, front-loaded with the action and result, then the critical caveat (WITHOUT launching) and a usage rule. No wasted words.

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?

For a 5-parameter estimation tool with no output schema, the description covers the essential: what it does, when to use it, and the unit of cost. It could have explained how query×zone combinations scale cost, but the core is complete enough for an agent to invoke 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 80%, so most parameters are well-described. The description adds the unit definition ('1 EF = full-France scrape for one query type'), which is valuable conceptual context not in the schema, but doesn't clarify how zones/queries interact with cost scaling. Baseline 3 is appropriate given high schema coverage.

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 precise verb+resource: 'Estimate the cost (EF) and volume of a Google Maps extraction'. It also clarifies it does not launch the job, instantly distinguishing it from create_scrap_job. No ambiguity about what the tool returns (cost and volume).

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

Usage Guidelines5/5

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

Explicitly says 'Always estimate before creating a large job', giving clear when-to-use guidance and implicitly routing to create_scrap_job as the alternative. This is actionable and unambiguous.

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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