knowledge_modell

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

Annotations are absent, so the description carries the full burden. It explicitly states 'Read-only' and 'Never withdraws or deletes anything itself,' which is critical behavioral disclosure. It also reveals the returned columns (actor/session/model) and the evaluation intent, adding context beyond the 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?

The description is front-loaded with the most important facts: read-only, what is listed, and the filtering criterion. The dash-separated rationale ('isolates one model's entries to judge its quality') is useful but slightly dense with the parenthetical ticket reference. No wasted words overall.

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 list tool with one required parameter and no output schema, the description conveys the operation, the purpose, and the safety guarantee. It does not mention pagination or output format, but the low complexity and self-contained scope make the description sufficient for correct 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 100% and the single parameter 'model' is already described with examples. The tool description reinforces that the model filters the entries, but adds no new semantic detail beyond what the schema provides, so baseline 3 is appropriate.

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?

States a specific verb and resource: 'list every knowledge node and lesson written by one model.' It also includes the key filter (model) and explicitly declares read-only behavior, which distinguishes it from mutation siblings like knowledge_update or knowledge_zurueckziehen. The parenthetical columns further clarify the output scope.

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?

Provides clear context: the tool is used to isolate one model's entries and judge its quality by outcome. This implies a use case (evaluation) without naming alternative tools explicitly, but the purpose is evident enough that an agent can decide when to invoke it rather than siblings like knowledge_search or knowledge_stats.

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