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 but offers minimal behavioral insight. It mentions a default and max for 'post_limit' and hints at return content, but lacks details on permissions, rate limits, data freshness, or whether this is a read-only operation. For a tool with potential data access implications, this is insufficient.

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 appropriately sized and front-loaded with the core purpose, followed by parameter details and return summary. Every sentence adds value, though the structure could be slightly more polished (e.g., integrating Args/Returns into prose). No wasted words.

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 2 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is moderately complete. It explains parameters adequately and defers return details to the output schema, but lacks behavioral context (e.g., auth needs, side effects) which is a gap for a tool in a social media analytics context.

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%, but the description compensates well by explaining both parameters: 'profile_id' as a LinkedIn public ID and 'post_limit' with default and max values. This adds crucial context beyond the bare schema, though it doesn't cover validation rules or format specifics for 'profile_id'.

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 analyzes content performance patterns for a LinkedIn profile, specifying the resource (profile) and action (analyze patterns). It distinguishes from siblings like 'analyze_my_content_performance' by targeting external profiles, but doesn't explicitly contrast with 'analyze_engagement' or 'analyze_post_audience' which might overlap.

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?

No guidance on when to use this tool versus alternatives like 'analyze_engagement' or 'analyze_my_content_performance'. The description implies usage for performance analysis but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.

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