ml_similar_incidents

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

The description adds meaningful behavioral details beyond the readOnlyHint and openWorldHint annotations: it uses keyword-based matching, returns only resolved incidents, and ranks them by keyword match count. This gives the agent a clearer picture of expected results.

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—two sentences—and front-loaded with the main action, followed by input specifics and output behavior. Every sentence earns its place with no 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?

For a tool with no output schema, the description adequately covers what the tool does, how inputs relate, and what the output looks like. It does not mention edge cases like both inputs provided, but overall it is complete enough for an agent to use it effectively.

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 baseline is 3. The description adds important semantic insight by clarifying that incident_sys_id and short_description are alternative inputs (one or the other should be provided), which the schema alone does not convey.

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's purpose: finding similar past incidents using keyword-based matching. It specifies the input types (sys_id or short_description) and the output (resolved incidents ranked by keyword match count), which distinguishes it from other ML tool siblings.

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 clear usage context by explaining the two mutually exclusive input modes: incident_sys_id for finding similar incidents, and short_description for free-text matching. It does not explicitly exclude alternatives or mention when not to use, but the guidance is sufficient for most cases.

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