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 mentions analyzing engagement patterns but doesn't specify whether this requires authentication, involves data fetching (e.g., from LinkedIn API), has rate limits, or returns real-time vs. historical data. For a 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 appropriately sized, with a clear purpose statement followed by parameter and return value explanations in separate sections. Every sentence adds value, but the 'Args' and 'Returns' sections could be integrated more seamlessly, and some redundancy exists (e.g., repeating 'engagement' in the purpose and returns).

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is adequate but has gaps. It covers the purpose and parameters well, and the output schema likely handles return values, so it doesn't need to detail those. However, it lacks behavioral context (e.g., data sources, execution time) and usage guidelines, making it minimally viable but incomplete for optimal agent decision-making.

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 beyond the input schema, which has 0% description coverage. It explains that 'profile_id' is a 'LinkedIn public ID' and 'post_limit' specifies the 'Number of posts to analyze' with default and max values, clarifying usage. However, it doesn't detail format constraints (e.g., URL format for profile_id) or edge cases, slightly limiting completeness.

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: 'Analyze optimal posting times based on engagement patterns.' It specifies the verb ('analyze'), resource ('optimal posting times'), and basis ('engagement patterns'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_engagement' or 'get_my_posting_recommendations', which might have overlapping functionality, preventing 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. It doesn't mention prerequisites, such as needing prior engagement data or a valid profile, nor does it compare to siblings like 'analyze_engagement' or 'get_my_posting_recommendations'. This lack of context leaves the agent to guess 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.