Latzerus Lernbereich

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

With no annotations provided, the description carries the full burden. It transparently discloses that the tool understands paraphrases, synonyms, singular/plural forms, and typos, and that exact title vocabulary is not required. It also specifies the returned fields (title, cluster, URL, short description). This goes beyond a simple 'search' claim and sets accurate expectations for a fuzzy-matching tool.

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, front-loaded with the primary purpose and immediately followed by the key behavioral differentiator (fuzzy matching). Every sentence earns its place—no filler, and the structure is efficient for an agent scanning for relevance.

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 search tool with fuzzy matching and an output schema present, the description covers the essential inputs, the flexibility of queries, the source scope, and the output fields. It does not mention sorting or pagination behavior, but these are minor given the output schema exists and the tool's purpose is clear. The description adequately equips an agent to call 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by providing concrete example queries ('Kunde will billiger', 'ChatGPT Follow-up') that illustrate the intended input semantics and the tool's tolerance for informal phrasing. This enriches the parameter understanding beyond the schema's generic 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 states the verb 'Durchsucht' (searches) and the specific resource 'alle kostenlosen Lernmodule von latzerus.ch' (all free learning modules from latzerus.ch). It clearly distinguishes this search tool from the sibling tools (overview, read, about) by its function of keyword-based discovery.

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 usage when a user has keywords or a question, but it does not explicitly state when to prefer this over lernmodule_uebersicht (for listing all) or lernmodul_lesen (for reading a specific module). No exclusions or alternative routing are given, leaving usage to inference.

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 discloses the grouping behavior, the output (Titel und URL), and the optional filter. It implies a read-only operation via 'Listet', but does not explicitly state that it has no side effects, nor does it mention any pagination, limits, or output volume beyond the token-saving hint. Adequate but not rich.

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 sentences, front-loaded with the core purpose and output, followed by the optional usage tip. No filler or redundant phrasing. Every sentence earns its place, and the structure is efficient for an agent to parse quickly.

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 listing tool with no output schema and no annotations, the description covers the essential information: what it returns (Titel und URL), how results are organized (by cluster), and the optional filter. Minor gaps exist (e.g., behavior on invalid cluster, whether results are paginated), but they are not critical for a straightforward overview tool. The token-saving hint implicitly warns about potentially large output.

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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining the rationale for using the optional cluster filter ('um Tokens zu sparen') and reinforces the meaning ('nur ein Thema listen'), which is not present in the schema description. This adds practical guidance on when and why to pass the parameter.

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 states a specific verb ('Listet'), a resource ('alle verfügbaren Lernmodule'), a scope ('alle', grouped by cluster), and the return fields (Titel und URL). This clearly differentiates from the sibling search tool (lernmodule_suchen) which implies targeted retrieval rather than a full listing. No ambiguity.

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 gives a clear usage hint for the optional parameter ('um Tokens zu sparen'), but it does not explicitly state when to use this overview tool versus the search or read tools. The context is implied (listing vs. searching vs. reading), but no direct alternative routing is provided.

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 discloses the return content (the listed sections) but does not mention any side effects, error handling, authentication requirements, or rate limits. As a read operation, it is implicitly non-destructive, but this is not stated explicitly. The description adds some context about what is returned but lacks behavioral depth.

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 a single, well-structured sentence that front-loads the main purpose and lists the content sections, followed by the parameter guidance. Every word is functional; there is no redundancy or filler.

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 tool with one parameter and no output schema, the description provides enough to call it correctly: it states what the tool does, what parameter to supply, and gives an example. It does not cover error scenarios or prerequisites, but for a read operation this is acceptable. The absence of an output schema is not a gap since the content is described.

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 coverage is 100%, so the baseline is 3. The description adds value by providing a concrete example slug ('preis-anker-ohne-zittern') and reiterating that a full URL is also acceptable, which reinforces the schema's parameter description. This goes slightly beyond the schema's minimal definition, earning a 4.

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's purpose: 'Liefert den kompletten Inhalt eines Lernmoduls' (delivers the complete content of a learning module), listing the specific sections (Kernpunkte, Hauptteil, Praxis-Schritte, typische Fehler, FAQ). The verb 'lesen' and the resource 'Lernmodul' are unambiguous, and the tool is distinct from its siblings (search, overview, about) by its action of reading a full module.

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 the tool is used when you have a slug or URL and want the full content, but it provides no explicit guidance on when to use this tool versus lernmodule_suchen or lernmodule_uebersicht. It does not mention any exclusions or alternatives, leaving the selection to the agent's inference.

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?

With no annotations, the description carries the full burden for behavioral disclosure. It does not state whether this is a read-only operation, whether it returns static content, or whether any permissions are needed. The description only lists content areas and provides no behavioral traits.

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 a single sentence that front-loads the core purpose ('Infos über Latzerus') and then lists the specific contents. It is concise with no filler or redundancy.

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 informational tool, the description is mostly complete. It covers what topics the information includes, but it does not indicate the output format (e.g., plain text, structured list) or any expected response shape, which would be helpful given there is no output schema.

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, and the schema description coverage is 100% (trivially). Per the rubric, a no-parameter tool earns a baseline of 4, and the description does not need to add parameter semantics.

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 subject (Latzerus, the knowledge project by Christoph Latzer) and lists the specific content areas (four topics, origin, AI tools used, contact). It is distinguishable from sibling tools about learning modules, though it lacks an explicit verb like 'get' or 'show'.

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?

No guidance is given about when to use this tool versus the sibling tools (lernmodule_suchen, lernmodule_uebersicht, lernmodul_lesen). The only signal is the implicit difference in subject matter, but the description does not state it or provide any alternatives or exclusions.

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