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

Adds important behavioral details beyond annotations: in-flight model calls may finish and persist records, remaining work is skipped, cancellation does not roll back writes, and no new LLM call is started. These consequences are material and not derivable from the annotation hints alone.

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 dense, purposeful sentences: action and state scope first, then behavioral consequences, then follow-up guidance. There is no filler, repetition of schema fields, or unnecessary background.

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 a single well-documented parameter and an output schema, the description covers action, effects, persistence semantics, side effects, and recommended follow-through. Nothing needed for correct invocation or interpretation is missing.

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 only parameter, job_id, is already fully described in the schema as 'Job ID to cancel' with 100% coverage. The description references job_id in the follow-up example but adds no new semantic detail beyond the schema.

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?

States a specific verb and resource: 'Request cancellation of a pending, running or paused LLM job.' It also distinguishes itself from read/delete-like operations by explicitly noting cancellation is not a rollback. This leaves no ambiguity about what the tool does.

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?

Provides clear post-conditions and next steps: inspect outcome via get_job_status and list_records(job_id=...). It implies when to use the tool, though it does not explicitly enumerate alternatives or cases where cancellation is inappropriate. The context is strong enough for an agent to select it correctly.

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