analyze_engagement

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 'deep engagement analysis' and output types but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or error conditions. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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: it starts with the core purpose, lists parameters with brief explanations, and ends with the return value. Every sentence adds value, though the parameter explanations could be slightly more detailed given the lack of schema descriptions.

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 an output schema exists (though not provided here), the description doesn't need to detail return values. However, with no annotations, 0% schema description coverage, and multiple related sibling tools, the description is incomplete—it lacks behavioral context and differentiation guidance. It's minimally adequate but has clear gaps for a tool performing analysis.

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 schema provides no parameter descriptions. The description adds some value by explaining 'post_urn' as a 'LinkedIn post URN' and 'follower_count' as 'Author's follower count for rate calculation (optional).' However, it doesn't fully compensate for the coverage gap—e.g., it doesn't clarify URN format, what 'rate calculation' means, or how the optional parameter affects results.

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 performs 'deep engagement analysis on a specific post' with 'comprehensive engagement metrics, reaction distribution, and quality score.' This specifies the verb ('analyze'), resource ('post'), and output scope. However, it doesn't explicitly differentiate from sibling tools like 'analyze_content_performance' or 'get_post_analytics,' which appear related.

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 multiple sibling tools that seem related (e.g., 'analyze_content_performance,' 'get_post_analytics,' 'generate_engagement_report'), there's no indication of what makes this tool distinct or when it should be preferred over others.

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