consult_problem

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains that the tool only returns structured suggestions, does not execute commands or modify files, and includes security measures such as sensitive information desensitization, input length control, and consultant whitelist validation. It also details mode options and fallback logic, offering comprehensive transparency.

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 moderately concise. It starts with a clear purpose statement, then lists what it does/doesn't do, then explains options. Some redundancy exists, but overall it is well-structured and essential information is front-loaded. A slight improvement could be grouping parameters more explicitly.

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 no output schema and 17 parameters, the description explains input handling (desensitization, length limits) and fallback behavior, but lacks details on output format (only 'structured suggestions' without specifics). The behavior for required parameter 'problem' is assumed but not elaborated. Completeness is adequate but not exhaustive.

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 0%, so the description must compensate. It explains the 'mode' parameter with explicit options (debugging/architecture/performance/etc.) and describes fallback behavior for 'panel' and 'consultants'. However, 17 parameters exist; many (goal, logs, files, effort, context, attempts, question, why_stuck, constraints, max_cost_usd, desired_output, current_attempt, max_input_chars) are not explained, leaving a significant gap.

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's purpose: '外援会诊', meaning external consultation, when the main model is stuck or needs a third-party perspective. It distinguishes from siblings by explicitly stating it only returns structured suggestions and does not execute commands or modify files.

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 explicit when-to-use scenarios: when the main model is stuck, not confident, continuous debugging failure, or needs a third-party perspective. It also states what the tool does not do (no command execution, no file modification) and describes fallback behavior for panel and consultants. This provides clear guidance for an AI agent.

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