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LinkedIn Profile by Name + Domain

run_linkedin_domain
Read-onlyIdempotent

Finds a LinkedIn profile matching a company name or domain plus a full name.

Input Schema

NameRequiredDescriptionDefault
full_nameYesFull name of the person to find.
company_or_domainYesCompany name or domain, e.g. openai.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds nothing beyond that — no note on match ambiguity, multiple results, confidence, or failure when no profile matches, which is the main behavioral risk for a fuzzy people lookup.

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?

A single front-loaded sentence with no filler, and the matching keys are named immediately. It is efficient, though it trades away any structure that could have carried caveats or usage routing.

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?

With no output schema and no annotations covering result semantics, the description should say what a caller gets back (a profile URL? a list?) and what happens on zero or multiple matches. For a fuzzy identity-resolution tool, that gap leaves the agent unable to interpret or trust the call.

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 both parameters are fully documented in the schema, and the description merely restates the same two inputs. Baseline 3 is appropriate since the schema does the work and the description contributes no additional format or example detail.

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?

States a specific verb and resource ('Finds a LinkedIn profile') and enumerates the two matching keys (company name/domain + full name). It does not distinguish itself from close siblings like run_linkedin_people_search or run_linkedin_company_employee, which an agent must open separately to tell apart.

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?

No guidance on when this lookup is preferable to run_linkedin_people_search, run_linkedin_company_employee, or run_linkedin_email, all of which occupy the same LinkedIn-people neighborhood. The only implied condition is that both a name and a company/domain must be known.

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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