run_cn_model

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

With no annotations, the description carries full disclosure burden. It mentions delegation to a Chinese LLM provider and the need for review, but lacks details on behavioral traits such as timeouts, error handling, or data sovereignty. The transparency is adequate but minimal for a tool delegating to an external provider.

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 consists of two concise sentences that front-load the purpose and usage guidelines. Every sentence adds value: one for delegation and use cases, one for the review requirement. No unnecessary information.

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 6 parameters, no output schema, and no annotations, the description is brief. It addresses the task's nature and usage but omits crucial context like output format hints, error scenarios, or cost/speed trade-offs. Adequate but leaves gaps given the complexity.

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 67%, with 4 of 6 parameters explained in the schema itself. The tool description adds no new parameter insights beyond the schema. Given high coverage, baseline is 3; the description does not compensate for uncovered parameters (max_tokens, temperature).

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 delegates tasks to a Chinese LLM provider and lists specific use cases (drafts, summaries, simple code generation, mechanical edits). While it distinguishes from the sibling 'draft_code_patch' by emphasizing simplicity and delegation, the differentiation could be more explicit, hence a 4 rather than 5.

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 provides clear guidance on when to use the tool ('Best for drafts, summaries, simple code generation, and mechanical edits') and explicitly requires review of results. However, it does not specify when not to use it or mention alternative tools, so it falls short of a 5.

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