Skip to main content
Glama

Get company by LinkedIn

get_company_by_linkedin
Read-only

Resolve a company from its LinkedIn URL and enrich it against our profile graph: funding, founders, sectors, score, domain, and technographics. Same result as POST /api/v1/companies/by-linkedin. Needs a free StartupHub.ai account.

Input Schema

NameRequiredDescriptionDefault
linkedin_urlYesThe company LinkedIn URL, e.g. https://www.linkedin.com/company/anthropic.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish readOnly, non-destructive, open-world behavior, so the description only needs to add context. It does: it discloses an authentication/account prerequisite and the equivalence to POST /api/v1/companies/by-linkedin, which is useful for agents that know the REST surface. It does not mention rate limits or caching, hence not a 5.

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?

Three short sentences, front-loaded with what the tool does and what it returns, followed by the endpoint equivalence and the account requirement. Every sentence carries information, though the REST-endpoint sentence is only useful to a subset of agents.

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 no output schema, the description compensates by naming the returned enrichment fields (funding, founders, sectors, score, domain, technographics) and flagging the auth requirement. What remains unstated is failure behavior for unknown or invalid LinkedIn URLs, which would round this out.

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?

There is one parameter with 100% schema description coverage and an example URL, so the schema carries the full burden. The description adds nothing about URL format, variants, or handling of non-canonical LinkedIn URLs, so baseline 3 applies.

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 uses a specific verb pair ("resolve" + "enrich") on a named resource (company) keyed by LinkedIn URL, and enumerates the enriched fields. The LinkedIn-URL input modality implicitly separates it from siblings like enrich_company or get_startup_by_domain, though no sibling is named outright.

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?

Usage is implied by the input type (you have a LinkedIn URL, so you use this tool), and it notes the prerequisite of a free StartupHub.ai account. However, it never states when to prefer this over enrich_company, match_companies_by_domain, or get_startup_by_domain, which is the real routing decision an agent faces.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources