diagnose_issue

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

Given no annotations, the description fully covers behavior: it returns root causes, diagnosis steps, fix code, prevention tips, and suggested MCP tools. It does not mention any modifications, auth needs, or rate limits, but as a diagnostic tool, this is acceptable.

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 concise, front-loaded with the main purpose, and structured with a bulleted list of categories and examples. Every sentence adds value without redundancy.

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?

The description covers all necessary aspects: purpose, input examples, categories, and detailed return values. Despite no output schema, the agent knows exactly what to expect. It is complete for a diagnostic tool with two parameters.

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 description adds value by providing examples for the 'symptom' parameter and listing all categories. However, it does not elaborate on the 'category' parameter beyond the enum values in the schema. Schema coverage is 50%, but the description compensates well.

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 diagnoses ServiceNow issues from symptom descriptions and returns guided fixes. The verb 'diagnose' and resource 'ServiceNow issue' are specific, and the listed categories and examples differentiate it from sibling diagnostic tools.

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 implies use when a symptom description is available and lists covered categories and examples. It does not explicitly state when not to use or compare to alternatives, but the context is clear enough for an AI agent.

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