lookup_companyTurn a LinkedIn company URL into firmographic data: industry, size, location and website. Spends 1 credit, refunded if the company cannot be retrieved.
| Name | Required | Description | Default |
|---|---|---|---|
| linkedin_company_url | Yes | A LinkedIn company URL, e.g. https://linkedin.com/company/… |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the 1-credit cost and the refund-on-failure behavior, which is useful operational context beyond the annotations. The wording implies a non-destructive lookup-style call, and nothing contradicts the supplied annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: the first covers purpose and output, the second covers cost and failure behavior. No redundant phrasing or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description fully covers input, output fields, and credit/refund behavior. An agent has enough information to call it correctly and interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully documents the single required parameter with a format and example, so the description adds little beyond what the schema provides. This matches the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Uses a specific verb ('turn') and a clear resource ('LinkedIn company URL'), then names the exact output fields: industry, size, location, and website. The company-focused phrasing distinguishes it from profile/email siblings like lookup_linkedin_profile and fetch_email.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The trigger condition is clear: use this when you have a LinkedIn company URL and need firmographic data. It does not explicitly compare against company_search or state when not to use it, but the intended usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.