LinkedIn Intelligence MCP Server

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

With no annotations provided, the description carries the full burden and adds valuable behavioral context: it discloses the data returned (basic info, LinkedIn member ID), mentions API reliability (Official LinkedIn API with OAuth 2.0), and fallback behavior (unofficial API if official client not configured). It does not cover aspects like rate limits or error handling, but provides substantial transparency beyond basic functionality.

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 front-loaded with the core purpose in the first sentence, followed by a bulleted list of return data and API details. Every sentence adds value without waste, making it efficient and well-structured for quick comprehension.

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 tool's low complexity (0 parameters), lack of annotations, and presence of an output schema (which handles return value details), the description is complete. It covers purpose, output examples, and API behavior, leaving no significant gaps for an agent to understand and invoke the tool correctly.

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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing on output and behavior instead. A baseline of 4 is applied as it effectively handles the zero-parameter case without redundancy.

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 verb ('Get') and resource ('the authenticated user's LinkedIn profile'), distinguishing it from sibling tools like 'get_profile' (which likely retrieves other users' profiles) and 'batch_get_profiles' (which handles multiple profiles). It precisely defines what the tool does without being vague or tautological.

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 implies usage context by specifying it retrieves 'the authenticated user's' profile, suggesting it should be used for self-profile access rather than others. However, it does not explicitly state when not to use it (e.g., vs. 'get_profile' for other users) or name alternatives, keeping it from a perfect score.

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