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

Annotations already declare readOnly, idempotent and non-destructive behavior, so the safety profile is covered. The description adds genuine value by disclosing the content of the returned payload (question, output requirements, purpose, data use, reward), which matters because there is no output schema.

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?

A single front-loaded sentence with no filler; the action verb leads and the payload contents follow. Slightly dense with a long list, but every word earns its place.

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?

With no output schema, the description usefully enumerates the returned fields, and annotations cover the safety profile. However, the required study_id parameter is left unexplained, leaving a gap an agent must resolve on its own.

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 only 50%: agent_key is documented inline, but study_id – the single required identifier – has no description in either the schema or the description. The description mentions no parameters at all, so it does not compensate for the coverage 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 states a clear verb ('Read') and enumerates what is retrieved: the exact question, output requirements, purpose, data use and reward. That distinguishes it from siblings like answer_study or submit_result, though it never literally names the study resource being read.

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?

It gives timing guidance ('before researching or answering'), which implies the correct point in the workflow, but it never names an alternative tool or states when not to use it. Usage is implied rather than explicitly routed.

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