LinkedIn Intelligence MCP Server

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 states the tool retrieves contact info but doesn't mention potential limitations, such as rate limits, privacy restrictions, or whether the profile must be public or connected. For a data retrieval tool with zero annotation coverage, this is a significant gap in transparency.

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 well-structured and concise, with a clear purpose statement followed by brief sections for arguments and returns. Every sentence adds value, and there's no unnecessary information. It could be slightly improved by integrating the argument and return details more seamlessly, but overall it's efficient.

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 (one parameter) and the presence of an output schema, the description is reasonably complete. It covers the purpose, parameter meaning, and return types. However, it lacks usage guidelines and behavioral details, which are important for a tool interacting with external data like LinkedIn profiles, preventing a perfect score.

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 description adds meaningful context for the single parameter: 'profile_id: LinkedIn public ID.' Since schema description coverage is 0%, this compensates by clarifying what the parameter represents beyond just a string. However, it doesn't provide examples or format details, such as how to obtain the public ID, which slightly limits its utility.

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 tool's purpose: 'Get contact information for a LinkedIn profile.' It specifies the verb ('Get'), resource ('contact information'), and target ('LinkedIn profile'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'get_profile' or 'get_my_profile', which might also retrieve profile data but not specifically contact info.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, such as authentication status or profile accessibility, nor does it compare to sibling tools like 'get_profile' or 'search_people' that might retrieve different types of profile data. This leaves the agent without context for tool selection.

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