knowledge_selbstauskunft

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

With no annotations, the description carries the full behavioral disclosure burden, and it does so well. It reveals that numbers are measured at call time and never maintained, that it counts what currently exists, and that it does not assess correctness. It also explains the historical motivation, which helps set expectations about freshness and accuracy.

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 core definition and includes a clear usage directive early. It is somewhat verbose due to the anecdotal backstory about the foreign client, but that narrative reinforces why live measurement beats memory and documentation. Minor redundancy keeps it from a perfect conciseness score.

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 zero-parameter tool with no output schema, the description covers what the tool reports, when to use it, and what it does not guarantee. It stops short of describing the exact return shape or format, which is a gap given that no output schema is provided. Overall it is still sufficiently complete for an agent to decide when and why to call it.

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 tool has zero parameters, so there is nothing for the description to explain about inputs. The description still adds context about what the call-time report covers, which is the relevant semantic content. Baseline 4 is appropriate for a parameterless tool.

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 that the tool reports what brainlehr currently is by measuring live sources (sqlite_master, tool registry, requirements.txt) at call time. It uses specific verbs like 'measures' and 'counts,' and it distinguishes itself from documentation- or memory-based answers. The purpose is unambiguous and distinct from the sibling knowledge tools.

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?

It explicitly instructs when to use the tool: 'Call this instead of relying on documentation or memory when asked what brainlehr is or can do.' It also adds a clear when-not-to-use boundary by stating it does not verify correctness and that validity, rank, and release live on individual entries. This gives an agent actionable selection criteria.

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