list_project_learning_reviews

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

Annotations already mark this operation read-only and non-destructive, and the description goes well beyond them by clarifying that saved lessons are 'human-graded receipt evidence only' and enumerating what the data does not do. This prevents the agent from over-attributing causality or using the data for routing/policy actions.

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?

Three sentences with no filler: function, intended use, and hard limitations are each given one clear sentence. The lead sentence is front-loaded with the verb and the object, and the subsequent sentences add distinct value.

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 two-parameter read tool with rich annotations and an output schema, the description gives strong context on content and interpretation. The only real gap is the absence of explicit parameter guidance, but schema names make project_id and limit discoverable enough that the description is not incomplete for selection and basic invocation.

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%, and the description does not compensate: it never mentions project_id or limit, nor does it explain how results are scoped or paginated. The parameter names are self-explanatory to a degree, but the description adds no meaning beyond the raw schema.

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 ('Read human-reviewed mission lessons and shadow-training admissions'), clearly identifying this as a read/list operation over a particular ledger. It distinguishes this from sibling write/list tools in spirit by defining the content as human-reviewed learning reviews, though it never names an alternative sibling.

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 states when to use it: 'Agents may use this ledger to choose future shadow experiments or training inputs.' It also provides explicit when-not guidance by listing effects the data does not have (e.g., does not update live confidence/routing, activate a policy), but it does not name fallback sibling tools for those purposes.

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