jev_verify_decision

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

No annotations provided, so the description must carry behavioral disclosure. It does well: explains return values ('accepted, rejected or needs_review plus statements, coverage, models, usage and cache metadata'), clarifies that verdicts are successful results, notes low coverage triggers review, and states prerequisite credentials (gh, Jev, AI backend).

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 packs multiple distinct facts into a dense block: purpose, mechanism, return format, verdict semantics, coverage behavior, scope caveat, and credential requirements. It is front-loaded with purpose but the middle-and-later sentences are run-on and would benefit from restructuring. The final sentence about server config is somewhat tangential.

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 11 parameters, no output schema, and no annotations, the description does a good job covering the core behavior: what it returns, that verdicts are successful results, that low coverage means review, and that it checks fidelity not correctness. The credential requirements are noted. It could explicitly address the cache (refresh param) behavior but covers enough.

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 coverage is 100%, so the schema already documents all 11 parameters in detail. The description adds high-level context (e.g., 'latest selects the most recent comment with citations') but doesn't enumerate threshold or model override semantics beyond what the schema provides. Baseline 3 is appropriate.

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 ('Verify') and resource ('GitHub decision comment against its cited issues/PRs') with the mechanism ('using the configured AI splitter and Jev'). Distinguishes from siblings like jev_route and ai_chat, though it doesn't explicitly name them.

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?

Explains the specific use case (verifying a decision comment) and clarifies the scope ('Checks source fidelity, not whether the decision is correct'), which sets a clear boundary on when this tool is the right choice. No explicit sibling alternatives are named.

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