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. While it mentions that the tool 'Returns a full engagement report', it doesn't describe important behavioral aspects: whether this is a read-only operation, if it makes external API calls, what permissions are required, potential rate limits, or how long the generation might take. For a tool that likely involves data analysis and potentially significant processing, this is a substantial gap.

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 appropriately sized. The first sentence clearly states the purpose, followed by organized sections for Args and Returns. Every sentence adds value, though the 'Args' and 'Returns' labels could be more integrated with the natural flow. There's no unnecessary repetition or verbose explanation.

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 that there's an output schema (which handles return value documentation) and only 2 parameters with some semantic clarification in the description, the description is minimally adequate. However, for a tool that generates 'comprehensive engagement reports' with content analysis and recommendations—likely involving complex processing—the description should provide more context about what 'comprehensive' entails, any limitations, or prerequisites. The absence of annotations exacerbates this gap.

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?

With 0% schema description coverage, the description adds meaningful context for both parameters: it clarifies that 'profile_id' is a 'LinkedIn public ID' (not just any string) and that 'post_limit' specifies 'Number of posts to analyze' with a default of 20. This provides essential semantic information beyond the bare schema. However, it doesn't explain parameter constraints like valid profile_id formats or post_limit ranges.

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: 'Generate a comprehensive engagement report for a profile' with specific details about analyzing posts and returning content analysis, timing, and recommendations. It distinguishes from siblings like 'analyze_engagement' by specifying a full report generation rather than just analysis. However, it doesn't explicitly contrast with other report-related tools like 'generate_my_content_calendar'.

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. With many sibling tools like 'analyze_engagement', 'get_my_posting_recommendations', and 'get_post_analytics', there's no indication of when this comprehensive report is preferred over more focused analysis tools. The description mentions what it returns but not when it's the appropriate choice.

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