Claimondo — Kfz-Gutachter finden & Termin buchen

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

Beyond readOnlyHint and openWorldHint annotations, the description discloses typical phrases recognized and that the tool always suggests an expert appointment. This adds valuable context, though the mention of 'IMMER den nächsten Schritt' could be interpreted as triggering actions, which slightly conflicts with readOnlyHint.

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 main purpose and well-structured, but includes a list of example phrases that could be more concise. Every sentence adds value, but slightly longer than necessary.

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?

Given a single parameter and an output schema (implied), the description covers input requirements, processing details, and output contents (decoded meaning and next step). It also mentions limitations and sibling tools, providing complete context for an AI agent.

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?

With 100% schema description coverage, baseline is 3. The description adds meaning by specifying the text can be the whole letter or an excerpt and that it must be from the opposing insurer, providing context beyond the schema's 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 decodes insurance letters, identifies specific phrases, explains their meaning, and suggests next steps (offering an expert). It uses specific verbs and distinct resource, distinguishing it from sibling tools like claimondo_finde_gutachter_termine.

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 states the tool is for decoding letters from the opposing auto liability insurance and mentions it does not provide individual legal advice. It implies when to use (letter text as input) and that a consultation without offering an expert is incomplete, but does not explicitly state when not to use or compare to siblings.

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?

Annotations already include readOnlyHint=true and openWorldHint=true, and the description reinforces these while adding valuable behavioral details: the token itself is the authorization, only a coarse status label is returned, no personal data is exposed, and unknown/invalid references return 'kein Fall gefunden'. This goes well beyond the annotation baseline.

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 well structured and front-loaded, with the main purpose stated first and useful warnings afterward. It is slightly redundant in places—'Read-only' repeats the annotation, and the Args section repeats schema fields—but the length is still appropriate for a privacy-sensitive tool.

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?

With one parameter, a full schema, an output schema, and read-only/open-world annotations, the description covers all essential context: purpose, authorization semantics, data-privacy behavior, and invalid-input handling. Nothing important is missing for an agent to select and invoke this tool 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 schema already fully documents the token parameter. The description's Args section largely repeats the schema, though it adds the semantic context that the reference is the customer's authorization and must not be guessed. This is helpful but does not substantially raise the baseline for a fully documented single 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 opens with a specific verb and resource: 'Gibt den groben Bearbeitungsstand eines zuvor ... angelegten Falls zurück'. It also differentiates itself from siblings by explicitly naming claimondo_melde_schaden/claimondo_rueckruf as the creation tools and by framing the use case as a returning customer asking 'wo steht mein Fall?'.

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 clearly states when to use the tool: for returning customers asking about a previously reported case, and only for cases created via the named sibling tools. It provides clear context but does not explicitly list exclusions or alternative tools for scenarios outside this use case.

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?

The description states it is read-only and anonymous, and explicitly says 'legt nichts an und meldet keinen Schaden', which adds a behavioral dimension beyond the raw annotations. It also reveals key nuances: wunschtermin only influences slot ranking rather than acting as a hard filter, and an older API version may lack buchungs_url with the map as fallback path. These are non-obvious behavioral facts an agent needs to know.

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?

Though the description is longer than average, it earns its length: sections are organized with headings/paragraphs, front-loaded with purpose and sibling contrast, and every paragraph adds either selection guidance, response-shape context, or critical link behavior that is absent from the structured data. The 'WICHTIG' and 'Hinweis' blocks are particularly high-value operational guidance.

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 includes the full output/return shape, explains the ranking-affecting nature of wunschtermin, gives a practical when-to-use example, and documents two URL-ownership situations that could easily cause user-facing errors. It even states how the tool relates to claimondo_melde_schaden via gutachter[].id plus termin.start. For a 4-parameter search tool, this is complete and decision-relevant.

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 Args section largely restates the schema definitions (format, required-alternative relation between plz and ort, wunschtermin semantics, response_format). It does not add meaningfully new parameter semantics beyond what the schema already documents. The optional-plz/ort interplay is mentioned in both the schema and the description, but there is no extra contract detail.

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 opens with a specific verb and resource: 'Findet buchbare Partner-Kfz-Gutachter MIT freien Terminen im Umkreis einer deutschen Postleitzahl'. It explicitly distinguishes itself from the sibling claimondo_finde_sachverstaendige, which returns only an anonymized list. An agent can immediately tell what this tool does and how it differs from its nearest alternative.

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 clear when-to-use guidance: 'Nutzer will einen Gutachter-Termin sehen/vergleichen'. It also names the sibling alternative and the condition that selects it — claimondo_finde_sachverstaendige for an anonymized list, this tool for bookable experts with concrete free slots. Additional practical guidance (link buchungs_url, do not link interaktive_karte_url when recommending one expert, use the map only for 'show all nearby') further clarifies correct usage.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description reinforces this with 'Read-only und anonym' and adds behavioral details like returning a distance-sorted list, map image URL, and callback phone number. It does not contradict annotations. A minor gap is no mention of rate limits or auth, but given the tool's nature, this is acceptable.

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 well-structured with clear sections: purpose, read-only note, args, returns, usage. It is concise without being overly terse; every sentence adds value. Could be slightly more streamlined, but overall effective.

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?

Given the tool has 4 parameters and an output schema, the description provides a complete overview: what it does, inputs, output structure, and when to use. It lacks error handling details, but as a finder tool this is sufficient. The output schema is explicitly described in the 'Returns' section.

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 schema already documents all parameters. The description's 'Args' section repeats most schema descriptions and adds examples (e.g., '50670'). It clarifies that PLZ OR ort must be provided, which is a slight addition. Baseline 3 is appropriate.

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 title and description clearly state it finds certified Kfz experts near a German postal code. The verb 'finde' and resource 'Sachverstaendige' are specific, and the description distinguishes from siblings like 'finde_gutachter_termine' (which finds appointment slots) and 'melde_schaden' (report damage).

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?

Explicit 'Use when' and 'Nicht für' sections provide clear guidance on when to use this tool (user asks for a car expert) and when not to (reporting damage, booking appointments). This helps the agent select the right tool among siblings.

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?

Annotations declare readOnly/openWorld/idempotent/non-destructive, and the description adds the crucial behavioral detail that the output deliberately omits company names, phone numbers, and addresses, instead requiring the agent to link the user to werkstatt_finder_url. It also warns against inventing names or contact data. This is a meaningful transparency beyond the structured annotations.

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 well structured with clear headings, short bullets, and an early explanatory section. Every paragraph earns its place: purpose, routing logic, argument details, and output behavior. The tone is dense but not bloated, and the most important 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?

For a read-only, 4-parameter lookup tool, the description covers the complete flow: when to call, which arguments to provide, and what to do with the output. It also addresses edge cases like unclear fault and how to present results without fabricating data. The few implicit details (e.g., that plz/ort are mutually required in practice) are strongly implied by 'PLZ ODER ort angeben.'

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?

Only 25% of the schema parameters have descriptions, but the description's Args section compensates well. It explains PLZ is a 5-digit German postal code, gives an example, explicitly says 'PLZ ODER ort angeben' (provided the relationship between the two), and documents radius range/default and response_format choices. This is substantially more meaningful than the raw schema alone.

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 opening sentence states a specific action and resource: 'Findet Claimondo-Partner-Werkstätten im Umkreis einer deutschen Postleitzahl.' It clearly identifies the tool's scope and, through the later routing guidance, explicitly distinguishes it from claimondo_finde_gutachter_termine. This makes the purpose and sibling differentiation immediately clear.

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 'WANN DIESES TOOL — und wann NICHT' section gives explicit conditional guidance: use it first for selbstverschuldete Schäden, use claimondo_finde_gutachter_termine first for unverschuldete Schäden, and use claimondo_pruefe_anspruch when the fault is unclear. It names alternatives, provides the ordering of steps, and explains why, so an agent can correctly select and sequence this tool.

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?

Discloses key behaviors beyond annotations: it's a write action ('ERZEUGT EINEN LEAD'), sends WhatsApp, requires consent, and returns no link/PII. Annotations already indicate non-readOnly, and description adds critical context about side effects and data handling.

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?

Well-structured with bold headings and warnings, but somewhat verbose. Every sentence adds value, though some repetition (e.g., consent explanation). Front-loaded with purpose and warnings, then flow and return values.

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?

Given complexity (10 params, 6 required, output schema exists), description covers prerequisites, workflow, behavior, and return format. It explicitly states what the output contains and what it does not contain, making it complete for an AI agent.

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. Description adds value by explaining the purpose of slot parameters and the consent parameter in the context of the workflow. It clarifies the relationship between sv_id, slot_start, slot_end, and wunschtermin, which aids correct usage.

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?

Description clearly states the primary action ('Meldet einen Kfz-Schaden und stößt die Gutachter-/Termin-Buchung an') and distinguishes from siblings by outlining the prerequisite step (claimondo_finde_gutachter_termine) and the resulting action (sending FlowLink). It uses specific verbs and resources.

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?

Provides explicit when-to-use guidance: after finding surveyor and obtaining consent. Warns about what not to do (no legal advice). Includes step-by-step workflow and explicitly mentions conditions for calling the tool (einwilligung_erteilt must be true).

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 annotations already declaring readOnlyHint and openWorldHint, the description adds significant behavioral context: the consultation is incomplete without offering an expert appointment, it always recommends the next step, it requires a prior fault determination, and it disclaims individual legal advice. No contradiction exists between the description and annotations.

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 somewhat lengthy but each sentence carries arguably important context: the core output, the mandatory next step, the intended usage, the requirement to ask before using, and the legal disclaimer. However, a few elements (e.g., the BGB paragraph references and the duplication of 'Erfrage zuerst die Schuldfrage') could be trimmed without losing clarity.

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?

Even though the output schema is not shown, the description gives a comprehensive picture: what is returned, which inputs to collect first, the interaction with sibling tools, the always-present next action, and the non-legal-advisory caveat. Combined with an output schema and full parameter documentation, this is sufficiently complete for an agent to invoke the tool 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 input schema already provides 100% coverage of all three parameters, including enum values, clear descriptions, and the conditional relationship between 'vollkasko' and 'schuldfrage'. The description reinforces that the Schuldfrage must be asked first, but this is essentially a repeat of the schema explanations. Baseline 3 is appropriate because the schema bears the parameter documentation burden.

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 what the tool does: it delivers structured claims (Wertminderung, Nutzungsausfall, Reparaturkosten, etc.) for a car-accident victim based on fault. It uses a specific verb ('Liefert'), identifies the resource, and contextualizes the output. It also distinguishes itself from siblings by explicitly mentioning follow-up tools and by framing itself as a consultation/support 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 explicitly says to use it for consultation questions ('Nutze es für Beratungsfragen') and tells the agent to first ask for the fault question ('Erfrage zuerst die Schuldfrage'). It also names downstream next steps and includes a statement that this is general information, not individual legal advice. However, it does not explicitly state when NOT to use this tool versus alternatives like 'claimondo_finde_werkstatt' or 'claimondo_melde_schaden'.

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?

Discloses creating a lead and callback task in dispatch queue, typical response time <15 min, and data processing via AI in US. Annotations only show mutation and open world; description adds significant behavioral detail without contradiction.

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: first states purpose and sibling contrast; second gives usage and data. No redundancy; efficiently 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?

Given 8 parameters with full schema, output schema exists, and detailed behavioral info, the description covers usage, prerequisites, and effects completely.

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 100%; description adds usage context like 'Erfrage Name + Telefonnummer + optional' and clarifies wunschzeit: without → quickly. Adds value beyond schema.

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 it requests a callback from a Claimondo consultant and contrasts with sibling claimondo_melde_schaden as the second funnel arm. It specifies verb, resource, and scope.

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?

Explicitly states only call with einwilligung_erteilt=true after customer consent. Provides context: for customers preferring calls or when slots/data missing. Lacks explicit when-not to use but contrasts with sibling.

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