signals

companies

Read-only

Search and filter company records by keyword, exact domain, HQ country, industry group, NAICS code, employee-count bucket, or founded-year range. Costs one credit per returned record.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum records to return (1-100). Each returned record costs one credit.
`since`	No	Return companies created/updated on or after this date (YYYY-MM-DD) or ISO-8601 timestamp.
`cursor`	No	Opaque pagination cursor returned as pagination.nextCursor on the previous page.
`domain`	No	Exact company domain.
`search`	No	Keyword search over name, domain, and description.
`countries`	No	Headquarters country codes (ISO alpha-2, OR). Pass "unknown" to include companies with no country on file.
`naicsCode`	No	NAICS 2022 code. Matches the code and everything beneath it, so "51" also returns 5132 and 5173. Sector ranges work: "31-33" returns all manufacturing. Pass "unknown" for unclassified companies.
`foundedYearMax`	No	Maximum founded year.
`foundedYearMin`	No	Minimum founded year.
`industryGroups`	No	Company industry groups (OR). Matched literally against the LinkedIn labels. Pass "unknown" for companies with no industry on file.
`employeeCountBuckets`	No	LinkedIn employee count buckets (OR), e.g. 51-200. Pass "unknown" for no headcount.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a meaningful operational detail beyond the annotations: 'Costs one credit per returned record,' which is not otherwise visible and directly affects agent planning of result sizes.

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 dense sentence followed by one critical cost note. Every phrase carries information, and the core search/filter capability is front-loaded before the cost caveat. There is no filler, repetition, or schema duplication.

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 11 optional parameters, no output schema, and no required inputs, the description names all major filtering categories and flags credit usage, which is the most important non-obvious behavior. Pagination and detailed filter semantics are already in the schema, so nothing essential to invoking the tool correctly is missing.

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?

Parameter schema coverage is 100%, with each parameter already having a descriptive purpose and, where relevant, enums or defaults. The description adds only a high-level grouping of the available filters (keyword, domain, country, industry, NAICS, headcount, founded year), which is useful for comprehension but does not materially deepen the schema's parameter-level explanations.

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 uses a specific verb ('Search and filter') and names the exact resource ('company records'), then enumerates the full set of filtering dimensions. This makes it clearly distinct from the sibling tools, which cover acquisitions, funding events, exec moves, and watchlist management rather than company lookup.

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?

The description implicitly tells an agent when to use this tool: whenever company records need to be searched or filtered by domain, country, industry, NAICS, headcount, or founding year. It does not explicitly name alternatives, but the sibling list makes the comparison obvious, so an agent can route correctly without extra guidance.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a clearly distinct resource or action: event types (acquisitions, funding_events, exec_moves), entities (companies, investors), and watchlist operations. The watchlist CRUD commands are separated by function, and company_timeline is clearly scoped to a single company's full history.

Naming Consistency4/5

Watchlist tools consistently use verb_noun (create_watchlist, get_watchlist, add_to_watchlist), while data lookup tools use plural nouns describing the returned entity or event (companies, investors, funding_events). The mixed convention is readable and predictable, with only company_timeline deviating from both patterns.

Tool Count5/5

With 11 tools, the server is well-scoped for its domain: company/event data search plus watchlist management. Each tool has a defined role and the count feels neither sparse nor bloated.

Completeness4/5

The toolset covers the core domain well: company and investor search, major event types, company timelines, and full watchlist membership management. A notable minor gap is the absence of a delete_watchlist tool, since create/list/get/add/remove exist for watchlists.

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