scf_trigger_evidence_assessment

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

With no annotations, the description carries full burden. It discloses the write operation ('write'), async behavior, and the return of a pending record. It instructs polling for status. It does not detail failure modes or side effects, but the key behaviors are covered adequately.

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 with critical information front-loaded: purpose, write operation, role, async, return, and next steps. No filler. Highly efficient.

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 the lack of output schema, the description adequately explains the return (pending record) and the follow-up action. It covers the core workflow, async behavior, role requirement, and resource constraints. All necessary context for an agent to use the tool correctly is present.

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 no additional meaning beyond what the schema provides (e.g., parameter sources are already in schema descriptions). The optional assessment_source is not elaborated. Thus, it meets but does not exceed the baseline.

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 explicitly states 'Queue an AI assessment of a single evidence file', clearly identifying the verb, resource, and scope. It distinguishes from siblings like scf_bulk_assess_evidence by specifying 'single', and mentions the async nature and the polling tool.

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 advises when to use (queue assessment) and what to do next (poll scf_get_evidence_assessment). It implies a single-file use case, differentiating from bulk. It also notes the required role ('editor+'), providing context. However, it does not explicitly list when not to use or compare alternatives in detail.

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