get_v1_mantis_forecast_scorecard

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

With no annotations, the description discloses important traits: rolling nature, verifiability, use of validator-identical metrics, disclosure of random baselines, and disclaimer about not being investment advice. It does not mention update frequency or limitations.

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 concise (4 sentences), front-loaded with key purpose, and each sentence adds value: function, metrics, use case, disclaimer, and metadata (price, category).

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?

Despite no output schema, the description adequately covers the output (balanced accuracy, AUC, random baselines) and context (informational, not advice). It does not describe the exact response structure but is sufficient for understanding the tool's purpose.

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 input schema has no parameters, so the description adds meaning by explaining what the tool returns (metrics and purpose). This is sufficient for a parameterless tool.

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 provides a rolling verifiable AI-model quality scorecard with specific metrics (balanced accuracy, AUC) and purpose (audit AI track record). It is distinct from sibling tools which focus on cryptocurrency derivatives 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 phrase 'Audit an AI track record instead of trusting claims' implies when to use, but no explicit exclusions or alternatives are given. However, the differentiation from siblings is clear.

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