list_hypotheses

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

No annotations are provided, so the description carries the transparency burden. It conveys that results are compact summaries and that status relates to verification packets, implying a read-only listing behavior. However, it does not describe output shape, ordering, or any limitations.

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 terse and well-organized: a one-sentence summary, a trigger list, and a parameter list. Every section earns its place with no filler or redundant restatement.

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 two-parameter list tool with no output schema, the description covers purpose, triggers, and parameter meaning. It still omits the set of valid status values and any detail about the returned summary fields, which an agent may need to interpret results effectively.

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 PARAMETERS section adds needed context: run_id is identified as a Co-Scientist run ID and status as an optional packet status filter. This surpasses the bare schema titles, but no allowed status values are given, leaving an important semantic 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 opens with a specific verb and resource: 'List compact hypothesis summaries for a Co-Scientist run.' It clearly identifies what the tool returns and the domain, and is distinct from sibling tools like start_hypothesis_verification or submit_hypothesis.

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?

An 'AUTOMATIC TRIGGERS' list explicitly states when to call this tool: inspecting generated hypotheses, selecting for verification, and checking which packets were sent. It does not mention when not to use it or name alternative tools, but the given triggers are concrete and actionable.

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