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 does well by disclosing key behavioral traits: it requires a specific API product enabled, explains pagination defaults and limits (start: 0, count: max 100), and details the return structure. It also mentions using the returned URN for replies, adding practical context. However, it doesn't cover error conditions or rate limits.

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 front-loaded with the core purpose, followed by prerequisites, args, returns, and usage note. Every sentence earns its place by providing essential information without fluff, making it efficient and easy to parse.

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 complexity (API-specific tool with prerequisites), 0% schema coverage, and no annotations, the description is highly complete. It covers purpose, prerequisites, all parameters with semantics, return values in detail, and a practical usage tip. With an output schema present, it appropriately explains return values to aid understanding, leaving no significant gaps.

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 fully, which it does excellently. It explains all three parameters: 'post_urn' with examples of URN formats, 'start' as pagination index with default, and 'count' with default and max value. This adds crucial meaning beyond the bare schema, making parameters fully understandable.

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 ('Get comments') and resource ('on a LinkedIn post') using the Official API. It distinguishes itself from sibling tools like 'get_post_comments' by specifying the API source and having a different name, though the sibling list shows both exist, indicating potential redundancy that the description doesn't address.

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 clear context for when to use this tool: for LinkedIn posts via the Official API with the 'Community Management API' product enabled. It explicitly links to 'create_comment' for replying, offering a clear next-step alternative. However, it doesn't explain when to use this versus the sibling 'get_post_comments' tool, which is a notable gap.

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