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Rafid Property Intelligence

find_companies

Read-onlyIdempotent

Discover companies from public web sources matching an industry, location, size and/or keyword criteria, returning cited candidate companies (never fabricated) with a stated confidence score. Use when an agent needs to discover a list of candidate companies matching criteria (industry, location, size, keywords) rather than analyze one already-known company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNo
limitNo
queryNo
countryNo
industryNo
keywordsNo
employeeMaxNo
employeeMinNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cachedYes
sourcesYes
dataModeYes
companiesYes
confidenceYes
limitationsYes
resultCountYes
appliedLimitYes
requestedLimitYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds valuable behavioral context: results are cited, never fabricated, and come with a confidence score. It also says sources are public web sources. This goes beyond annotations, though it could mention rate limits or sorting behavior.

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 a single, front-loaded sentence with no wasted words. It states the core action first, then criteria, output guarantees, and usage context. Every clause earns its place.

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

Completeness3/5

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

Given 8 parameters, zero schema descriptions, and an output schema, the description covers purpose, usage, and high-level output guarantees. However, it does not explain all parameters (query, limit) or how filters interact. While the output schema defines return format, parameter semantics are incomplete, leaving an agent to infer details.

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?

With 0% schema description coverage, the description must explain parameters. It mentions industry, location, size, and keywords, which map to industry, city/country, employeeMin/Max, and keywords. However, it omits 'query' (free-text search) and 'limit' (result cap), and does not clarify how criteria combine (AND/OR). It partially compensates but leaves gaps.

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 action ('Discover companies'), the resource ('companies'), and the filtering criteria (industry, location, size, keywords). It differentiates from siblings by explicitly contrasting with analyzing an already-known company, making it distinct from tools like research_company or get_oman_company_profile.

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?

It provides explicit when-to-use guidance ('Use when an agent needs to discover a list of candidate companies') and a when-not ('rather than analyze one already-known company'), but does not name specific alternative sibling tools. The contrast is clear enough, though referencing a specific alternative would strengthen it.

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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