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NeuralVerge Business Intelligence

LinkedIn Company Search

run_linkedin_company_search

Searches LinkedIn companies by a text query with optional filters (size, industry, location).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxItemsNoMaximum number of results to return.
locationsNoLocation filters.
startPageNoPage offset to start from.
companySizeNoCompany size buckets, e.g. ['51-200'].
industryIdsNoIndustry filters.
scraperModeNoScraper depth mode, e.g. 'short' or 'full'.
searchQueryYesFree-text company search query.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It only says 'searches' without noting that this likely scrapes LinkedIn, may require an active session, has pagination (startPage, maxItems), or has rate limits. The scraperMode parameter implies scraping behavior, but the description does not explain it.

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 sentence that is front-loaded with the core action ('Searches LinkedIn companies') and an efficient parenthetical listing optional filters. There is no wasted text, and every word contributes to understanding the tool's purpose.

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?

For a 7-parameter tool with no output schema and no annotations, this description is too thin. It does not explain pagination behavior, return format, defaults, session requirements, or the difference between scraperMode values. An agent would still need to infer many important details from the schema and sibling tools.

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 each parameter already has a meaning. The description adds a small summary by mentioning filters size, industry, and location, which map to parameters, but adds no extra semantics for parameters like scraperMode, startPage, or maxItems. This meets the baseline for full schema coverage.

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 the verb ('Searches'), resource ('LinkedIn companies'), and scope ('by a text query with optional filters'). It is distinguishable from sibling tools like run_linkedin_people_search or run_crunchbase_company, though it does not explicitly name alternatives.

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 guidance is provided on when to use this tool versus siblings such as run_crunchbase_company or run_search. There is no mention of session prerequisites (e.g., get_session_status) or conditions that would make this tool appropriate or inappropriate.

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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