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rcreech93

LinkedIn MCP

by rcreech93

profile_search

Search for LinkedIn profiles by name and retrieve key details such as profile URL and job title.

Instructions

Search for LinkedIn profiles by name.

Args: name: The name to search for

Returns: A list of LinkedIn profiles as dictionaries

Example output in case of success: [ { "person_name": "Gil Fernandes", "person_linkedin_url": "https://www.linkedin.com/in/gil-palma-fernandes/", "profile_id": "gil-palma-fernandes", "title": "Software Engineer at Onepoint Consulting" }, { "person_name": "Gil Fernandes", "person_linkedin_url": "https://www.linkedin.com/in/gil-fernandes-a7578334a/", "profile_id": "gil-fernandes-a7578334a", "title": "Passionate Holistic Therapist and Dynamic Team Leader with over 12 years of experience across diverse care settings. Skilled in crisis management and dedicated to fostering well-being in every aspect of care!" } ...

Example output in case of error: { "error": "Error searching profiles: Error searching profiles" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden of disclosure. It goes beyond a simple summary by describing the return type ('a list of LinkedIn profiles as dictionaries') and providing both a success example and an error example, giving agents a clear picture of expected behavior.

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 well-organized with an intro line, Args, Returns, and examples. The success example is somewhat lengthy due to a very long title string, but it still earns its place by illustrating the exact output structure. Overall, it is efficient and clear.

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?

For a simple one-parameter search tool, the description covers the core requirements: what it does, what parameter to pass, and what output to expect (both success and error formats). It lacks advanced details like pagination or empty-result behavior, but these are not critical for this low-complexity tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema only provides the parameter title 'Name' with no description. The tool description compensates fully by explaining 'name: The name to search for' in the Args section, adding the essential meaning that the schema lacks.

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 a specific verb ('Search') and resource ('LinkedIn profiles') with a scoping qualifier ('by name'). This distinguishes it from the sibling tool 'linkedin_profile', which likely targets a single profile rather than name-based search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly conveys when to use this tool: when you need to find LinkedIn profiles by a person's name. It provides clear context, though it does not explicitly contrast with alternatives or mention exclusions, so it stops short of a 5.

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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