eval_acceptance_report

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

With no annotations, the description carries the entire burden and meets it: it explains determinism ('without model calls'), result semantics (accept/reject/indeterminate plus digest, coverage, per-slice results), failure rules ('Missing required checks... cannot pass', 'known quality failure can reject even with incomplete evidence'), and the important exit_code vs MCP process status distinction. No behavioral trait is hidden.

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?

Six sentences, each adding a distinct fact: mode, inputs/outputs, hard failure rules, and the exit_code caveat. There is no filler, and the core purpose is front-loaded.

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 annotations and two non-trivial inputs, the description covers input semantics, decision logic, and process-vs-policy exit code behavior. It also has an output schema to further specify return values. Only minor gaps remain, such as exact accepted file path forms or explicit routing relative to sibling eval tools, but nothing that blocks correct invocation.

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 0%, so the description must add meaning, and it does: report_json_path is a full EvalReport JSON and policy_json_path is a 'multivon.policy/v1 JSON policy.' This is enough for an agent to know what each argument must reference, though it stops short of detailing path constraints or policy syntax.

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 first sentence names a specific verb ('Apply'), a specific resource ('versioned acceptance policy to saved evidence'), and a distinctive constraint ('without model calls'). It then states the outcome values and artifacts, clearly differentiating this offline policy gate from sibling model-based eval tools like eval_toxicity or eval_custom_rubric.

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 phrase 'to saved evidence' and 'without model calls' indicates when this tool is appropriate: offline acceptance on an existing EvalReport JSON rather than generating a new evaluation. It does not explicitly name sibling alternatives or give negative routing guidance, so it misses full when-not-to-use clarity.

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