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

Adds meaningful behavioral context: it operates against selection-rules.json and returns which good_fit/poor_fit criteria matched along with a score. However, with no annotations and no output schema, it doesn't state whether results are deterministic, how the score is scaled, or error behavior for non-matching input.

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 compact sentences, front-loaded with purpose and scope, followed by the parameter definition and return behavior. No filler, though the parameter line is somewhat redundant with the schema's type hint.

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?

Covers purpose, source of criteria, and return content, which is adequate for a single-parameter tool. But with no annotations and no output schema, it omits the shape of the returned criteria/score and any failure modes, leaving gaps for a matching tool.

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%, so the description must carry the load. It does define the single parameter ('requirement: Freitext-Anforderung/Situationsbeschreibung des Interessenten'), clarifying format and source, but adds no length constraints or examples.

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+resource: a free-text match (Abgleich) of a requirement against documented good_fit/poor_fit criteria (signals) and services/capabilities. Distinguishes the resource (fit criteria from selection-rules.json) from siblings like find_cases or recommend_services, though it does not explicitly name an alternative tool.

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 when to use it – when you have a free-text requirement and want to know fit – but does not state when NOT to use it or point to alternatives such as recommend_services for purely service-level matching versus this holistic fit check.

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