Get AI diagnosis

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

Annotations already indicate readOnlyHint=true; the description adds valuable behavioral context: returns null when none exists, and describes the content of the diagnosis. No contradictions.

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?

Two sentences, highly efficient. First sentence states purpose and output fields; second sentence adds null case and alternative. Front-loaded and no wasted words.

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?

For a simple read tool with one param, readOnly annotations, and no output schema, the description covers all necessary context: what it returns, when it returns null, and how to create a diagnosis. No gaps.

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 0%, so description must compensate. It does not explain the annotation_id parameter beyond its name; no format, constraints, or examples provided. The parameter is self-explanatory but description adds no new semantics.

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 reads the latest stored AI diagnosis, lists included fields (root causes, confidence, suggested fix, likely source files), and distinguishes it from the sibling diagnose_annotation which creates the diagnosis.

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?

Explicitly states when to use (read diagnosis) and when not (returns null), plus provides alternative: run diagnose_annotation to create one.

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