Claimondo — Kfz-Sachverständigen-Finder

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds valuable context: 'Read-only und anonym — legt nichts an und meldet keinen Schaden' and explains that booking happens via map/phone, not through this endpoint. Adds behavioral details beyond 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?

Well-structured with sections: purpose, sibling comparison, args, return structure, usage hint, booking note. Front-loaded with main purpose. Could be slightly more concise but still efficient for the complexity.

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 4 parameters, high annotation coverage, and existence of output schema, the description covers all necessary aspects: what it does, how to use, parameter constraints, return format, and booking process. No gaps for agent to infer.

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 3. Description adds meaning: explains mutual exclusivity of plz/ort, clarifies wunschtermin is a ranking hint not a filter, and describes response_format options. Adds value beyond the 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 finds buchbare Kfz-Gutachter with free appointments in German zip code areas. It explicitly distinguishes from sibling claimondo_finde_sachverstaendige by contrasting anonymized list vs bookable with slots. Very specific verb+resource+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?

Explicit 'Use when' clause gives concrete example ('wann hat ein Gutachter in 50670 Zeit?'). Directly compares to sibling tool and explains when not to use it. Clear guidance on context.

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?

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?

Annotations already indicate readOnlyHint and openWorldHint. The description adds crucial behavioral context: it provides general information (not legal advice) and always triggers a follow-up action (expert offer or callback). This beyond-annotation detail helps the agent understand the tool's side effects and limitations.

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 efficiently written with two main sentences, the first listing the core output and the second giving usage context. It includes a bulleted list inline, which is clear but slightly dense. Every sentence serves a purpose, though a minor reorganization could improve scannability.

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 moderate complexity (2 parameters, output schema exists), the description covers the tool's purpose, input requirements, expected output (structured claims), and mandatory follow-up steps. No important details are omitted for an agent to use it correctly and consistently with the overall workflow.

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 reinforces the required parameter 'schuldfrage' by instructing to ask the user first, and clarifies the optional 'schadenart' as providing context (e.g., Auffahrunfall). This adds practical guidance to the agent beyond the 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 states it delivers structured damage claims based on fault and always includes the next step of offering an expert appointment or phone callback. It specifies the exact claims (Wertminderung, Nutzungsausfall, etc.) and references relevant legal paragraphs, clearly distinguishing from siblings that focus on specific sub-tasks.

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 instructs to use for advisory questions about claims, requires first asking about the fault question, and mandates offering an expert appointment as a necessary next step. It sets clear context and prerequisites without ambiguity.

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.