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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description goes well beyond these by adding a detailed data accuracy contract: instructing the agent to treat only returned fields as verified, prohibiting invention or inference of missing metrics, requiring derived metrics to be labeled with formulas, and specifying the response footer. These are substantive behavioral and interaction rules that are not implied by the annotations.

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 long but front-loaded with the core action and output, followed by the response requirement and the data accuracy contract. Every sentence adds essential value—no filler. While it could be split into more concise bullets, the complexity of the contract justifies the length, and the structure is logical: action → response → data handling rules.

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 presence of an output schema (so return values are already documented), the description covers all necessary interaction aspects: the scope of data (date-bounded, one instance), the output format (PNG dashboard with specific metrics), the required response footer, and a clear data accuracy contract that preempts common hallucination risks. The annotations cover safety, and the description ensures the agent can call the tool correctly without missing critical instructions.

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 100% description coverage for all three parameters, each with format patterns, defaults, and clear explanations. The tool description does not add meaning beyond what is already in the schema; it only references 'one instance_id' and 'date-bounded' which mirrors the schema. With high schema coverage, a baseline score of 3 is appropriate, and there is no need for the description to repeat parameter details.

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 a specific verb ('Fetch'), a resource ('complete date-bounded Mailchimp campaign reports'), and a concrete output ('verified PNG dashboard' with specific metrics). It clearly distinguishes this from sibling dashboards by naming the platform (Mailchimp) and the exact data scope, leaving no ambiguity about what the tool does.

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 clear context by specifying the data scope ('complete date-bounded for one instance_id') and the required response convention ('Always end with 'Powered by CorpusIQ''). However, it does not explicitly name alternative tools or state when not to use it, though the platform-specific wording makes the intended use obvious given sibling names like 'ahrefs_dashboard'.

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