hypothesis_get

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

With no annotations, the description carries the full burden. It discloses a key behavioral trait: only hypotheses that passed the delivery evidence threshold are returned, and unverified ones are filtered out. This adds meaningful context beyond the schema, though it does not cover side effects, errors, or permissions.

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?

Two short, front-loaded sentences with zero waste. The main action and the key exclusion are presented clearly and efficiently.

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 no annotations, the description covers the core behavior and the main selection criteria. Minor gaps like return format or error handling exist, but for a simple read tool with one parameter, this is reasonably 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 description coverage is 0%, so the description must compensate for parameter meaning. It does not explain 'hypothesis_id' beyond what the schema already shows. The parameter name is self-explanatory, but the description adds no additional semantics, format, or usage details.

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 reads frozen hypotheses that have passed a delivery evidence threshold, and explicitly excludes unverified ones. This specific verb+resource+scope distinguishes it from sibling tools that handle other data types.

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 when to use this tool by defining its selection criteria (frozen, passed threshold). It also provides an exclusion ('does not return unverified hypotheses'), which gives some guidance on when not to use it, though it does not explicitly name alternatives.

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