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FlatNineOrg

LeadBrew MCP Server

by FlatNineOrg

leadbrew_search_companies

Search for B2B companies by name, industry, country, and employee size. Retrieve company details including website, industry, and size from LeadBrew's database.

Instructions

Search for companies in the LeadBrew database. Returns company info including name, website, industry, and size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoSearch query for company name
pageNoPage number (default: 1)
limitNoNumber of results (1-25, default: 10)
countryNoFilter by country code
industryNoFilter by industry
max_sizeNoMaximum company size (employee count)
min_sizeNoMinimum company size (employee count)
Behavior2/5

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

No annotations are present, and the description does not disclose behavioral traits such as read-only nature, pagination behavior, rate limits, or authentication requirements. It only states the output fields, omitting details on how results are returned or any 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, front-loading the purpose. It wastes no words, but it could be slightly more structured by explicitly listing the return fields or parameter usage.

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

Completeness2/5

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

Given 7 parameters and no output schema, the description is incomplete. It does not explain parameter behavior, pagination (though page/limit are in schema), or the structure of returned data beyond a vague list of fields. The agent lacks sufficient context to use the tool effectively without inferring from parameter names.

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?

All 7 parameters have descriptions in the input schema (100% coverage), so the schema does the heavy lifting. The description adds no additional meaning beyond listing the return fields, which are already implied by the parameter names and schema.

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?

The description clearly states it searches for companies and returns specific fields (name, website, industry, size). However, it does not differentiate from the sibling tool 'leadbrew_get_company', which likely retrieves a single company, leaving potential ambiguity about when to use each.

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?

No when-to-use or when-not-to-use guidance is provided. The description does not mention alternatives or context for use, leaving the agent to infer from sibling names alone.

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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