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Hassan220022

harvestapi-mcp

by Hassan220022

harvest_search_companies

Search and filter LinkedIn companies by keywords, location, company size, and industry.

Instructions

Search LinkedIn companies by keywords, location, size, industry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
geoIdNoFilter by LinkedIn Geo ID (overrides location). Use harvest_search_geo_id to find it
searchNoKeywords to search for in company names
locationNoFilter by location (e.g. Australia)
industryIdNoIndustry ID(s) comma-separated. List: https://github.com/HarvestAPI/linkedin-industry-codes-v2
companySizeNoCompany size filter. Values: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+ (comma-separated)
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits itself. It only states the action and filters; it does not mention that the operation is read-only, how results are paginated, what fields are returned, or any authentication/rate-limit implications. This under-disclosure leaves significant uncertainty for an agent.

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, front-loaded sentence: 'Search LinkedIn companies by keywords, location, size, industry.' It is concise, with no filler, and clearly communicates the core purpose without 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?

For a tool with 6 parameters and no output schema, the description is minimally viable. The schema covers parameter semantics well, but the description lacks details on return format, pagination, or typical usage scenarios. It meets the basic bar but leaves measurable gaps.

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 input schema includes detailed descriptions for all 6 parameters (100% coverage), including geoId override and companySize value ranges. The description only lists a subset of filter dimensions without adding new meaning beyond the schema, so a baseline of 3 is appropriate.

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 by keywords, location, size, and industry, which are meaningful filter criteria. It distinguishes itself from sibling search tools (e.g., search_jobs, search_posts) by explicitly targeting companies.

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 this tool is for searching/finding companies via the verb 'search', but it does not explicitly contrast with alternatives like harvest_get_company for retrieving a specific company or other search tools. There is no when-to-use guidance or exclusionary language.

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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