generate_dashboard

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates JSON but doesn't describe what happens after generation (e.g., is it saved, displayed, or returned as output?), whether it requires specific permissions, or any side effects. This is inadequate for a tool that likely creates or outputs data.

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 single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 complexity (a generation tool with no annotations and no output schema), the description is incomplete. It doesn't explain what the output looks like (e.g., JSON structure), how the metrics are selected, or any behavioral traits. For a tool that likely produces significant output, this leaves critical gaps for an agent.

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 100%, so the schema already documents both parameters thoroughly (category with enum values and dashboard_name). The description adds no additional meaning beyond implying the parameters influence the dashboard generation, which the schema already covers. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('generate') and resource ('Grafana dashboard JSON'), making the purpose understandable. However, it doesn't distinguish this tool from its siblings (analyze_metrics, list_metrics, search_rust_code), which all deal with different operations on metrics/code rather than dashboard generation.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when not to use it, or how it relates to sibling tools like analyze_metrics or list_metrics. The agent must infer usage from the purpose alone.

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