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ralf9090

Agent Kiosk

maps_leads

Read-onlyIdempotent

Find local businesses and places worldwide, then extract names, phone numbers, websites, addresses, opening hours, and GPS coordinates for lead generation and verification.

Instructions

Find local businesses, companies, and places in any city worldwide. Extracts business name, phone number, website URL, address, opening hours, and GPS coordinates for lead generation and verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of leads to return (1-25, default: 10)
queryYesBusiness type or name (e.g., 'cafe', 'dentist', 'lawyer')
locationNoCity, region, or country (e.g., 'San Francisco', 'London', 'Istanbul')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.5.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds value by disclosing the returned field set (name, phone, website, address, hours, GPS), but says nothing about pagination, rate limits, or how 'limit' interacts with result availability. With annotations carrying the safety burden, a 3 is appropriate.

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: the first establishes what the tool finds, the second what it returns. No filler, no redundancy, and the core capability is front-loaded.

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 correctly compensates by enumerating the returned fields, which is the most important missing piece. For a 3-parameter read tool with full annotation coverage, the description is nearly complete; only the usage-routing context is thin.

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 all three parameters (query, location, limit with its 1-25 range and default) are already fully documented in the schema. The description's 'any city worldwide' loosely implies location is broad-scoped, but adds no syntax, format, or constraint detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Find local businesses, companies, and places in any city worldwide,' and enumerates the extracted fields. An agent can immediately tell this is a business-directory/lead lookup. It does not, however, name or contrast itself with any sibling (e.g., web_extract or social_search), so it stops short of full sibling differentiation.

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

Usage Guidelines3/5

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

The phrase 'for lead generation and verification' implies the intended use case but gives no explicit when-to-use/when-not-to-use guidance and never mentions an alternative tool. The agent must infer that this is for prospecting rather than general web search, which is a reasonable but unstated inference.

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