agent_learning_session

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

No annotations are provided, so the description must carry the burden. It discloses that the tool performs multiple steps (scoring, diffing, planning) and references 'testing effect + knowledge tracing', but it does not explain side effects, whether it modifies state, what 'snapshot' means, or what is returned. For a complex session tool, transparency is insufficient.

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 a single sentence that packs several actions, but it is not overly verbose. It front-loads the primary action ('Run one end-to-end learning session') and tacks on specifics. While slightly dense, it avoids redundancies and earns its place, though splitting into bullets could improve readability.

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 tool lacks both annotations and an output schema, and the description omits crucial context such as the meaning of 'second snapshot', the format of answers, and the expected result. For a tool with two undocumented parameters and a multi-step operation, this description is too thin for an agent to invoke correctly without guessing.

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?

The input schema has zero descriptions for the two parameters (count, answers), and the tool description does not explain them. 'count' could be the number of attempts, and 'answers' likely holds practice data, but nothing clarifies their meaning or relationship. With 0% schema coverage and no compensating description, parameter semantics are not established.

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 runs an end-to-end learning session and enumerates specific sub-actions (score practice attempts, diff a second snapshot against baseline, plan next loop). This provides a specific verb and resource, and distinguishes it from related siblings like practice_session or learning_loop by describing its multi-step process.

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 this tool is for a comprehensive learning session that combines scoring, diffing, and planning. However, it does not explicitly state when to use this tool versus alternatives like practice_session or learning_loop, nor does it mention exclusions or prerequisites. The usage context is implied but not spelled out.

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