Diagnose a knowledge node

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds meaningful context beyond those annotations by stating the operation is deterministic and does not call a model, which clarifies expectations about variability and external dependencies. There is no contradiction with annotations.

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 sentences with no filler. The first sentence front-loads the core behavior and outcome, and the second adds an important behavioral distinction. Every part 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?

For a simple deterministic read-only tool, the annotations carry much of the safety context. However, with no output schema, the description does not indicate what the tool returns or how to consume the recommendation, and it lacks explicit usage guidance relative to siblings. An agent can invoke it correctly but may not know what to do with the result.

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 never mentions node_id or how it should be used. The parameter name is reasonably self-explanatory, which prevents a 1, but the description contributes no additional semantic meaning or usage detail for the required parameter.

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 specific verb (identify) and resource (prerequisite, depth, and evidence gaps), and attaches a concrete outcome (recommend the next learning action). It also distinguishes itself from model-based operations with 'It does not call a model,' which helps separate it from siblings.

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 context is implied: an agent would call this when it needs to diagnose gaps and decide a next action for a node. However, the description never explicitly says when to use this tool versus alternatives such as get_node_context, search_knowledge, or update_node_learning_state, and it provides no exclusions.

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