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neuralverge

NeuralVerge MCP Server

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by neuralverge

run_linkedin_company_search

Search LinkedIn companies by text query and filter results by company size, industry, and location. Get targeted company profiles for research and enrichment.

Instructions

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.
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states 'searches', implying a read-only operation, but does not disclose pagination behavior, scraper depth implications, authentication needs, or return format. This is minimal transparency for a tool with no annotation support.

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, concise sentence that captures the core function and key filter dimensions. It is front-loaded with the action and resource, with no filler or redundancy.

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?

The tool has 7 parameters, no output schema, and no annotations. The description is brief but covers the main purpose. However, it omits behavioral context such as pagination, result format, and how parameters like scraperMode affect behavior, making it only partially complete for an agent to invoke correctly.

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 the structured schema already explains all parameters. The description adds a high-level summary (size, industry, location) that maps to specific parameters but does not add new semantic detail beyond what the schema provides.

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 tool searches LinkedIn companies using a text query with optional filters (size, industry, location). It uses a specific verb and resource, and the mention of size/industry/location distinguishes it from sibling tools like people search and company employee search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a company search is needed, but it provides no explicit timing guidance or alternatives. It does not mention when not to use this tool or point to sibling tools, so usage is inferred rather than directly stated.

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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