Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that it 'Returns a structured, audited deliverable' and mentions server-side validation. However, it does not clarify the async parameter behavior, rate limits, or what 'audited' entails. With annotations covering safety, the description adds moderate value but not rich behavioral context.

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 somewhat verbose and includes a marketing-like opening ('Gapup agent-payable C-suite expertise (CMO)') and a case reference. While the structure lists questions, it could be more concise and focused. The key functional statement ('Returns a structured, audited deliverable') is present but buried.

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 7 parameters, 5 required, no output schema, and low schema coverage, the description provides use cases and example output (score +5) but does not explain the full output structure or mention the async parameter behavior. The tool is moderately complex; the description fills some gaps but leaves important details (e.g., return format, async polling) unaddressed.

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 only 14% (only 'async' described). The description provides implied context for parameters like date_range_days (7 days), entity_name (company, brand), sentiment_lens (crisis-detection, launch-monitoring), and include_competitors (comparison). However, it does not explicitly map parameters to their schema definitions, leaving ambiguity. It adds meaning but falls short of systematic documentation.

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 states the tool provides media sentiment analysis for entities like companies, brands, and products, with specific use cases (e.g., crisis detection, launch tracking). It clearly identifies the resource (sentiment of news media) and the action (pulse/analysis). However, it does not explicitly differentiate from sibling tools like 'reputation_engine' or 'press_influencer', so it's slightly less than a 5.

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 lists multiple example questions that imply when to use the tool (e.g., 'What is current PR sentiment?', 'Is there a crisis building?'). It provides context for typical use cases but no explicit guidance on when not to use it or which sibling tools to prefer for related tasks. The lack of exclusions or alternatives prevents a higher score.

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