LinkedIn Intelligence MCP Server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It adds valuable context about implementation priorities and potential limitations (Pro plan requirement, bot detection risks), which helps the agent understand reliability and cost implications. However, it doesn't cover other important behavioral aspects like rate limits, authentication requirements, or error handling.

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 with clear sections (purpose, args, returns, implementation details). Every sentence adds value, though the technical implementation details might be more appropriate in a separate section. The front-loaded purpose statement is effective, but the two-paragraph structure could be slightly more streamlined.

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 has an output schema (so return values are documented elsewhere), the description provides good context. It covers purpose, parameters, implementation constraints, and limitations. For a search tool with 2 parameters and output schema, this is reasonably complete, though it could benefit from more usage guidance relative to sibling tools.

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?

Schema description coverage is 0%, so the description must compensate. It documents both parameters ('keywords' and 'limit') with clear explanations of their purpose and constraints (default: 10, max: 50). This adds meaningful semantics beyond the bare schema. However, it doesn't provide examples or format guidance for the 'keywords' parameter.

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: 'Search for companies on LinkedIn.' It specifies the verb ('search') and resource ('companies on LinkedIn'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_people' or 'search_jobs', which would be needed for a perfect score.

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. While it mentions two backend implementations (Fresh Data API and linkedin-api), this is technical detail rather than usage context. There's no mention of when to choose this over other search tools like 'search_people' or 'search_jobs' in the sibling list.

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