report_metric

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

Discloses behavioral traits like auto-resolving targets and returning deviation analysis, and supports multiple metrics. However, without annotations, it omits details on side effects (e.g., whether it persists data) and authentication requirements, making it adequate but not rich.

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?

Three sentences efficiently convey purpose, mechanism, and usage context with no redundancy. Front-loaded with the core action.

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?

Provides enough context for an AI agent to understand when to use this tool and what it returns (deviation analysis). Lacks details on idempotency or side effects, but given no output schema, the description is reasonably complete.

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%, so baseline is 3. Description adds no extra meaning beyond parameter descriptions in the schema (e.g., it does not elaborate on how metrics are structured or expected formats).

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?

Description clearly states the action ('report actual performance metrics') and resource ('blueprint'), distinguishes from sibling tools by focusing on post-implementation tracking, and explains the auto-resolution and deviation analysis.

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?

Directly advises to use 'after implementing agents to track whether they hit their success criteria', providing clear context. Does not explicitly name alternatives but implicitly distinguishes from other tools.

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