knowledge_stats

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond a simple 'get statistics' by explaining the semantics of the anlass_by split: selbst/betreiber are self-reported and only as reliable as the caller, hook/skript are objective, and unbekannt is the default for older entries. It also explicitly instructs readers not to treat the categories as equally trustworthy. It does not explicitly state read-only behavior, but the word 'statistics' strongly implies a non-mutating operation.

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 consists of two sentences, with the core purpose front-loaded in the first sentence and the necessary trustworthiness caveat in the second. There is no filler. The second sentence is dense and somewhat convoluted with nested parentheses and slashes, but every part serves a purpose.

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 tool with no parameters, no output schema, and no annotations, the description covers the essential return semantics: the types of statistics and the meaning of the anlass_by categories. The main omission is an explicit statement that the operation is read-only, but this is reasonably inferable from 'statistics'. The description handles the most confusing part of the output (trust levels of categories) very well.

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?

This tool has zero parameters, so there is no parameter ambiguity to resolve. The description adds no parameter-level details, and none are needed. The baseline of 4 for zero-parameter tools applies.

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 identifies the tool as providing overview statistics of the knowledge database and enumerates concrete metrics: node counts, lesson counts, access patterns, and anlass distribution. This positions it as an aggregate/analytics tool, distinct in likely purpose from sibling tools like knowledge_search or lesson_query. However, it does not explicitly name alternatives or state what it is not, so sibling differentiation remains implicit.

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 offers no explicit guidance on when to choose this tool over siblings. It does provide a strong interpretive caveat about the anlass_by fields – warning that the four categories are not equally trustworthy – but that is guidance for reading output, not for selecting the tool. When-to-use vs. alternatives is only implied by the tool's name and aggregate framing.

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