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NeuralVerge Business Intelligence

LinkedIn Profile by Name + Domain

run_linkedin_domain

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

Input Schema

TableJSON 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool 'Finds' a profile, without mentioning output shape, failure behavior when no match exists, session or authentication needs, or any other runtime characteristics. This leaves significant behavioral uncertainty.

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 a single, efficient sentence with no filler and starts with the key action. It is appropriately sized for the tool's complexity, though it omits helpful routing or behavioral details that would make it more valuable.

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?

The tool has no output schema and no annotations, and it sits among many similarly named LinkedIn tools. The description explains what the tool searches for but not what it returns, when to choose it over alternatives, or how failures are handled, so an agent is not fully equipped to invoke it correctly.

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 the baseline is 3. The description adds the notion that the two parameters are combined ('plus a full name'), but otherwise it essentially paraphrases the parameter names and descriptions without adding meaningful format, constraints, or usage nuance.

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 a specific verb ('Finds'), a specific resource ('LinkedIn profile'), and the exact matching criteria (company/domain plus full name). It does not explicitly differentiate itself from overlapping siblings like run_linkedin_people_search, so it stops short of full sibling differentiation.

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 when-to-use guidance, exclusions, or mention of alternative tools is provided. With siblings such as run_linkedin_people_search, run_linkedin_company_employee, and run_linkedin_company_search, an agent cannot tell when this tool is preferable over those alternatives.

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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