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scrape_leads

Start a background job to scrape business leads from Google Places by specifying a search query and location.

Instructions

Start a background job to scrape leads from Google Places based on a query and location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxLeadsNoMaximum leads to collect (default 50)
minRatingNoMinimum business rating (0-5)
textQueryYesThe search query (e.g., 'Digital Marketing Agencies')
radiusMetersNoSearch radius in meters (max 50000)
centerLatitudeNoOptional center latitude for precise search
centerLongitudeNoOptional center longitude for precise search
targetLocationNameYesHuman-readable location name (e.g., 'Mumbai, India')
includedPrimaryTypesNoFilter by Google Place types (e.g., ['restaurant', 'gym'])
Behavior2/5

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

The description mentions 'background job' but does not disclose what that entails (e.g., async behavior, job ID returned, how to check status). With no annotations, the description carries full burden for behavioral traits like rate limits, authorization needs, or potential impact. This is insufficient, especially for a mutation tool.

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 one concise sentence that immediately communicates the core action and outcome. Every word serves a purpose, and the most critical information ('background job') is front-loaded.

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

Completeness1/5

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

With 8 parameters, no output schema, and no annotations, the description is severely lacking. It fails to explain what the background job returns (e.g., job ID), how to monitor progress, or how the optional parameters affect the search. The agent has insufficient context to use this tool correctly without external documentation.

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 schema already documents each parameter. The description does not add meaning beyond that (e.g., how parameters interact, examples, or constraints). Baseline 3 is appropriate given high coverage, but no extra value is provided.

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 clearly states the tool's purpose: scraping leads from Google Places based on a query and location. It uses a specific verb ('scrape') and resource ('leads from Google Places'), and it distinguishes from sibling tools like get_leads and export_leads by indicating it initiates a background job.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as get_leads (retrieving existing leads) or export_leads (exporting). It does not mention prerequisites, context, or exclusions, leaving the agent to infer usage based solely on the tool's name and purpose.

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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