list_open_questions

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

Annotations provide readOnlyHint=true, and the description adds context by detailing the types of open questions (ALIAS, MERGE_CONFLICT, SOFT_CONTRADICTION) and their firing conditions, which helps the agent understand what to expect.

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 overly long with implementation-specific bullet points about question types and firing conditions. It lacks conciseness and does not front-load the most critical information for the agent.

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?

The description lacks explanation of the output format, does not clarify the role of the 'branch' parameter, and omits behavioral details like pagination or ordering. The tool's context is insufficient for effective use.

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%, and the description does not mention the 'branch' or 'status' parameters at all. The agent has no guidance on how these parameters affect the output.

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 explicitly states 'list unresolved questions' at the beginning, clearly indicating the tool's verb and resource. It distinguishes from sibling tools like answer_question by directing resolution to that 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 by stating that LLM should present questions to the author and pass to answer_question. However, it does not provide explicit guidance on when to use this tool vs alternatives or when to avoid it.

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