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Find Lookalike Companies

find_lookalike_companies
Read-only

Find companies similar to one or more seed domains (for audience expansion).

Company-level only. Use to expand a target list from known-good accounts.

RETURNS: {companies: [{domain, name, employee_range, industry, city, state}, ...]}. Returns {companies: []} when none are found or targeting is not configured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax lookalikes to return (default 10).
domainsYesSeed company domains, e.g. ['metadata.io'].

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=true and destructiveHint=false. The description adds useful behavioral detail: it returns a specific shape, returns an empty array when nothing is found, and notes the 'targeting is not configured' edge case. This goes beyond the annotation-provided safety profile.

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 compact and front-loaded with the core purpose, followed by the usage context and return format. Every sentence adds information; there is no filler or repetition of the title.

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?

For a two-parameter read-only tool with fully described schema fields, the description is complete: it explains purpose, scope, return shape, and failure behavior. Since there is no output schema, the explicit RETURNS block is especially valuable and sufficient.

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 coverage is 100%, so the baseline is 3. The schema already documents both 'domains' and 'limit'. The description adds contextual meaning like 'seed domains' and 'known-good accounts', but does not significantly extend parameter-level semantics beyond the schema.

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?

States a specific verb ('Find') and resource ('companies similar to seed domains') with a clear use case ('audience expansion'). The 'Company-level only' qualifier helps distinguish it from person-level or audience-creation tools among the many sibling tools.

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?

Provides clear context: 'Use to expand a target list from known-good accounts.' It does not explicitly name alternative tools or state when not to use it, but the purpose is specific enough that an agent can select it appropriately.

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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