Get metric data

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe reads. The description adds value by explaining that data is simulated by default and the need to switch to a real backend in production. However, it mentions a 'simulated' parameter not present in the input schema, which could cause confusion.

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 consists of two concise sentences, front-loading the purpose and then adding behavioral notes. No unnecessary information.

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 (not shown), the description need not detail return values. It covers purpose, funnel step, simulation behavior, and production considerations. The only gap is the mention of a 'simulated' parameter not in the schema, which slightly reduces completeness.

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?

Input schema has 100% coverage with descriptions for both parameters. The tool description does not add significant semantic meaning beyond what the schema provides. Baseline is 3 per guidelines.

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 action: fetch current values, rolling averages, and thresholds for given metrics at a location. It also identifies itself as 'Step 3 of the funnel', distinguishing it from sibling tools like find_metrics (likely step 2) and analyse/diagnostic_categorize (post-processing).

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 context on when to use this tool ('Step 3 of the funnel'), implying it follows find_metrics. It also mentions the simulation default and production swap, guiding usage scenarios. However, it does not explicitly state when not to use it or list direct alternatives.

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