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Jing-yilin

LinkedIn MCP Server

by Jing-yilin

search_companies

Find LinkedIn companies by keywords, location, size, and other filters to retrieve structured company data for research or analysis.

Instructions

Search LinkedIn companies. Returns cleaned data in TOON format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchYesKeywords to search
locationNoFilter by location
geoIdNoFilter by LinkedIn Geo ID
companySizeNoFilter by size: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+
pageNoPage number
save_dirNoDirectory to save cleaned JSON data
max_itemsNoMaximum results (default: 10)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'Returns cleaned data in TOON format' which adds some value about output format, but doesn't cover important behavioral aspects like whether this is a read-only operation, rate limits, authentication requirements, pagination behavior beyond the 'page' parameter, or what 'cleaned' specifically entails.

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 extremely concise at just two sentences, with zero wasted words. It's front-loaded with the core purpose and follows with output format information. However, the brevity comes at the cost of missing important contextual information.

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 search tool with 7 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the relationship between parameters, what 'TOON format' means, how results are structured, or provide any examples. The lack of output schema means the description should do more to explain what the tool returns.

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?

The description provides no parameter-specific information beyond what's already in the schema. However, with 100% schema description coverage and all 7 parameters well-documented in the schema (including defaults for 'page' and 'max_items'), the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 action ('Search LinkedIn companies') and the resource ('companies'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling search tools like search_groups, search_jobs, or search_profiles, which all search different LinkedIn entities.

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?

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling search tools available (search_groups, search_jobs, search_profiles, etc.), there's no indication that this tool is specifically for company searches rather than other LinkedIn entity types.

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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