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

No annotations are provided, so the description carries the full disclosure burden. It explains the exact 5-pillar weighting, the A-F percentile bands, the distinction between grade and absolute health_score, and the crucial missing-pillar behavior where weights are renormalized rather than scored as zero. This is rich, operationally relevant behavioral detail.

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 dense but every sentence carries substantive information: methodology, weighting, grading rules, missing-data handling, usage intent, and response formats. It is front-loaded with the purpose and avoids filler, though the long run-on sentence about pillar weights could be easier to parse if split into bullets.

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?

For a tool with one optional parameter and no output schema, the description is highly complete. It specifies the scoring model, grade distribution, absolute vs relative scores, renormalization for missing data, and the difference between detailed and concise responses. An agent can accurately decide whether and how to call this tool without further documentation.

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% and the single parameter already has an enum plus a short description. The description adds value by clarifying that 'detailed' is the default and includes per-pillar sub-scores, while 'concise' returns only symbol/grade/score/change and is roughly ～70% smaller. This helps an agent choose correctly based on payload size and output needs.

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 opens with a specific verb and resource: 'Get health grades (A-F) for all tracked fan tokens.' It further defines the output as a percentile-based health ranking with 5 pillars, making the tool's purpose unmistakable and distinct from sibling tools that cover prices, sentiment, or match data.

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 gives a clear use case: 'Use this to quickly filter which tokens deserve attention relative to their peers.' It does not explicitly name sibling alternatives or exclusion conditions, so it stops short of full routing guidance, but the intended context is well implied.

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