knowledge

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. The description adds useful behavioral context by promising a 'belegbare' (verifiable) profile and listing 'kanonische Belegseiten' as part of the output, which is beyond the annotations but not extensive.

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 two sentences: the first is a dense, structured summary of content, the second provides concrete usage examples. Every phrase earns its place, no fluff, and the key information is front-loaded.

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?

The description fully covers the tool's purpose, output content (listing categories like Anbieter, Fachbereiche, Zielgruppen), and usage examples. With no parameters, strong annotations, and an output schema present, the description is complete for the agent to select and invoke it correctly.

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 the baseline is 4. The description does not need to explain parameter semantics, and the schema confirms no parameters exist.

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 the verb 'Liefert' (delivers) and the resource 'das belegbare Leistungsprofil von Michael Kanda / designare.at', outlining specific content categories. It distinguishes itself from the sibling search_knowledge by specifying exact use case questions like 'Was macht designare.at?', making its purpose unambiguous.

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 explicitly provides usage context with 'Nutze dieses Tool für Fragen wie...' and lists three example questions. However, it does not explicitly state when not to use the tool or mention the sibling as an alternative, so it lacks full exclusion guidance.

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

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

No annotations are provided, so the description carries the full burden. It describes the search functionality and scope but does not disclose behavioral details such as output format, result limits, or the fact that it is a read-only operation. This is acceptable for a simple search tool, but there is room for more transparency.

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 concise, consisting of two sentences that immediately convey the purpose and usage. It is front-loaded with the action and resource, and every sentence adds value without waste.

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 one-parameter search tool, the description provides adequate context: the knowledge domain, the entity, and example use cases. Since there is no output schema, it could optionally describe what kinds of results to expect, but this is not critical for a well-understood search operation.

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 schema already provides 100% coverage for the single parameter 'query' with examples. The description adds context about what kinds of questions to ask, which slightly enhances understanding, but essentially the description adds little beyond the schema's own parameter description.

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 the tool performs a semantic search in the designare.at Knowledge Base and specifies the topics it covers (Michael Kanda, web and AI services, projects, blog articles, expertise). It uses a specific verb ('Findet' / finds) and resource, making the purpose distinct from the sibling tool get_services.

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 explicitly tells the agent when to use this tool: 'Nutze dieses Tool wenn du Fragen über designare.at, Webdesign in Wien, SEO, GEO, KI-Sichtbarkeit oder Michael Kandas Arbeit beantworten willst.' It provides clear usage context, though it does not explicitly mention when not to use it or contrast it with the sibling tool.

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