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

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint. The description adds value by specifying that returns include 12-month projections with confidence scores, skill growth rates, patent filing trends, and job posting volumes. It also describes inputs and outputs. However, it does not mention the async parameter behavior or the job_result polling mechanism, which are present in the schema.

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 a paragraph of 4 sentences, front-loading the main purpose. It efficiently covers inputs, outputs, and use case. While clear, it could be slightly more concise by removing the 'Keywords' line, which adds little. Overall, every sentence contributes and the structure is logical.

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 moderate complexity (4 parameters, annotations present, output schema exists), the description covers the core functionality: what it does, inputs, outputs, and target audience. It mentions the 12-month horizon and confidence scores. However, it does not mention the async option or how to retrieve results for slow queries (the job_result tool). This is a minor 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?

Schema coverage is 100% with descriptions for all 4 parameters. The description adds context beyond the schema by providing examples (e.g., 'machine learning', 'NLP') and explaining how parameters relate to the output (e.g., 'geographic focus (US state/country)'). This helps the agent understand parameter purpose and format, exceeding the baseline 3 for high schema coverage.

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 forecasts AI skill demand trends for CHROs using patent filings and job postings. The verb 'Forecasts' is specific, the resource is detailed (AI skill demand trends with inputs and outputs). Although the sibling list is long, this tool's focus on forward-looking demand projections distinguishes it from related tools like lnd_skill_taxonomy_builder, job_postings_intelligence, and patent_landscape.

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 implies usage for HR leaders prioritizing workforce upskilling but does not explicitly state when to use this tool versus alternatives. No when-not or alternative tool guidance is provided. For example, it could have mentioned that for current talent availability one should use talent_intelligence. The context is clear but lacks exclusionary guidance.

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