LinkedIn Custom MCP Server

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

Annotations only provide a title, so the description carries the burden of behavioral disclosure. It describes a read-only fetch operation, which aligns with the tool name and purpose, but does not mention rate limits, authentication requirements, error handling, or what data is returned (though an output schema exists). It adds minimal context beyond the basic action, leaving gaps in behavioral understanding.

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?

The description is a single, efficient sentence that front-loads the key information ('Fetch a specific member's profile') and includes a helpful example. There is no wasted text, and it directly supports the tool's purpose without redundancy, making it highly concise and well-structured.

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?

Given the simple input schema (one required parameter) and the presence of an output schema, the description is mostly complete. It covers the parameter semantics well but lacks details on authentication, rate limits, or error scenarios. For a read-only fetch tool, this is adequate, though adding a bit more behavioral context would enhance completeness.

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 has 0% description coverage, so the description fully compensates by explaining the 'member_urn' parameter's purpose and providing a concrete example ('urn:li:person:123'). This adds essential meaning beyond the bare schema, clarifying the parameter's format and usage, which is critical for correct tool invocation.

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?

The description clearly states the specific action ('Fetch') and resource ('a specific member's profile'), distinguishing it from sibling tools like 'linkedin_get_my_profile' (which fetches the user's own profile) or 'linkedin_search_people' (which searches for multiple profiles). It provides a concrete example of the required parameter format ('urn:li:person:123'), making the purpose unambiguous.

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 description implicitly indicates when to use this tool—when you have a specific member's URN and need their profile. However, it does not explicitly state when not to use it (e.g., vs. 'linkedin_search_people' for broader searches) or mention prerequisites like authentication, though the context of sibling tools suggests it's part of a LinkedIn API suite. The guidance is clear but lacks explicit alternatives or exclusions.

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