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Getlead

Start a lead scrape

scrape_leads_start

Queue a background scrape for fresh leads from the web and social platforms. Returns a job_id immediately; poll scrape_job_status and then read the results with scrape_job_leads. Scraped rows spend scrape credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat to look for, for example 'dental clinics in Berlin'
platformsNoSocial platforms to include
max_resultsNoTarget lead count
location_textNoLocation hint
email_requiredNoOnly keep leads that have an email

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false. The description adds valuable behavioral context beyond annotations: it explicitly states the operation is async ('Returns a job_id immediately') and that 'Scraped rows spend scrape credits,' which is a non-obvious cost. This goes beyond the annotation hints and helps the agent understand the side effects.

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 three sentences with no filler. It front-loads the core action and then gives the follow-up steps and cost implication. Every sentence earns its place—there is no redundancy or irrelevant detail.

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?

Despite having no output schema, the description clearly states what the tool returns (a job_id) and how to proceed (poll status, then fetch leads). It also mentions the credit cost, which is a critical operational detail. For a 5-parameter tool with a 100%-described schema, the description provides sufficient context for an agent to correctly invoke and handle the result without ambiguity.

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%, so the baseline is 3. The description does not add any additional meaning to the parameters beyond what the schema already provides (e.g., it doesn't clarify 'query' format beyond the example, or explain the 'platforms' enum semantics). It mentions 'social platforms' but that's already implied. No extra value is added.

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 verb ('Queue') and resource ('a background scrape for fresh leads from the web and social platforms'). It clearly distinguishes itself from sibling scraping tools by emphasizing the asynchronous nature and the immediate return of a job_id, which is a unique characteristic among the listed siblings.

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?

The description provides a clear workflow: returns a job_id immediately, then poll scrape_job_status and read results with scrape_job_leads. This gives context on how to use it, but it does not explicitly name alternatives or when-not-to-use (e.g., for immediate synchronous results). It's clear enough for an agent to infer the use case, but lacks explicit exclusions.

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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