grade_step

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

With no annotations provided, the description adds behavioral context via the sieve metaphor, indicating the grading is not absolute. However, it does not disclose whether the tool is read-only, requires authentication, or has rate limits, leaving some gaps.

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 very concise: two sentences plus a metaphor, front-loading the purpose and adding nuance without unnecessary words.

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 simple tool with 3 parameters and no output schema, the description covers purpose and return type but does not specify the output structure or error handling. It is adequate but not fully complete.

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 baseline is 3. The description does not elaborate on parameter meanings beyond what the schema already provides, adding minimal value.

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 performs LLM automatic scoring verification on task session output, returning pass/fail and detailed feedback. This is a specific verb-resource pair, though it does not explicitly distinguish from sibling tools like approve_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 implies usage for grading session outputs and provides cautionary interpretation (pass != perfect, fail != real problem). However, it lacks explicit guidance on when to use this tool versus alternatives such as approve_tool or deny_tool.

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