Skip to main content
Glama

Estimate scrape cost

estimate_scrap
Read-only

Estimate the cost (EF) and volume of a Google Maps extraction WITHOUT launching it. 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
scrap_modeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / stop_at / description
      Previous value: -"Stop after N unique places (recommended when the user gives a number)"New value: +"Stop after N unique places"
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

The readOnlyHint annotation already establishes that nothing is mutated, and 'WITHOUT launching it' reinforces this. The description adds genuinely useful context by defining the EF unit (one full-France scrape per query type), which helps interpret the result, but it says nothing about auth, rate limits, or how the estimate is derived.

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 tight sentences, front-loaded with the core action and the key non-side-effect guarantee, followed by the unit definition. No filler.

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 partially compensates by stating what is returned (cost in EF and volume). Combined with 80% schema coverage this is nearly complete, though it could say more about what drives the cost figure.

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 80%, so the schema already carries most parameter meaning, including the zones, queries, country, and stop_at semantics. The description adds no parameter-level detail such as which parameters most affect the estimate or how scrap_mode changes cost, so the baseline 3 applies.

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 (estimate) and resource (cost and volume of a Google Maps extraction), and explicitly distinguishes itself from an actual run with 'WITHOUT launching it'. This separates it cleanly from the sibling create_scrap_job, which does the real extraction.

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?

'WITHOUT launching it' clearly frames this as a pre-flight/dry-run check, implying it should be called before committing to create_scrap_job. It gives clear context but never names the alternative tool or states an explicit exclusion, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources