Claimondo — Kfz-Sachverständigen-Finder

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which are not contradicted. The description adds valuable behavioral context: it states the tool produces explanations and always suggests next steps, and it explicitly mentions the tool is for general information only (read-only). No destructive actions are implied.

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 necessary given the tool's complexity. It front-loads the core purpose and includes examples and required follow-up actions. Minor redundancy could be trimmed, but overall it is clear and well-structured.

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's complexity (decoding multiple insurance tactics and providing actionable next steps), the description is fully complete. It covers input, process, output, and mandatory follow-up actions. An output schema exists, so return value explanation is not needed.

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 has 100% coverage for the single parameter 'text' with a description. The description merely reiterates to pass the letter text as 'text', adding minimal semantic value beyond the schema. 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 description clearly states the tool decodes insurance letters, identifies specific phrases used to reduce/delay claims, explains their meaning and the claimant's rights, and always offers the next step of an independent expert appointment or callback. This distinct purpose differentiates it from siblings 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 tells when to use the tool: when a letter from the opposing car liability insurance is received. It clarifies the tool provides general information, not individual legal advice, and emphasizes that a consultation must include offering an expert appointment, guiding the agent to use sibling tools for follow-up.

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 provide readOnlyHint, idempotentHint, and destructiveHint. The description adds context such as 'Read-only und anonym — legt nichts an und meldet keinen Schaden' and explains that actual booking goes through other channels. No contradictions.

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 a clear main sentence, differentiation, usage hints, and structured Args/Returns. It is front-loaded and informative, though slightly verbose.

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 parameter count, schema coverage, annotations, and output schema described in the description, it covers most aspects. It could be clearer about the plz/ort requirement, but overall it is complete.

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%, but the description adds nuance beyond the schema, such as explaining that wunschtermin only influences slot ranking (no hard filter) and that plz or ort must be provided. This adds value.

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 finds buchable Kfz experts with free slots near a German postal code, using the verb 'findet' and resource 'Partner-Kfz-Gutachter MIT freien Terminen'. It distinguishes itself from the sibling tool claimondo_finde_sachverstaendige, which only provides an anonymized list.

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 explains when to use the tool (e.g., 'Nutzer will einen Gutachter-Termin sehen/vergleichen') and contrasts it with claimondo_finde_sachverstaendige, providing clear alternatives.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds specific behavioral details: read-only, anonymous, does not create anything or report a claim, and provides data-privacy-friendly results. It also describes the return format (sorted list, map image, interactive map link, callback phone), going 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?

The description is well-structured and efficient: a concise purpose statement, a behavioral disclaimer, an args list with defaults, a return structure, and explicit usage guidance. Every sentence adds value, with no fluff 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?

Given the tool's complexity (4 parameters, 0 required, output schema present), the description is fully complete. It covers all parameters, return values, privacy aspects, and usage conditions. The output schema is embedded, and annotations further enhance transparency. No gaps remain.

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 baseline is 3. The description's 'Args' section reiterates parameter details and adds usage hints like 'PLZ ODER ort angeben' and default values. While it largely repeats schema info, it organizes it in a user-friendly way, earning a slight improvement to 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 tool's purpose is clearly stated: finding certified Kfz-Sachverständige near a German postal code. The verb 'Findet' identifies the action, and the resource is explicitly described. It distinguishes itself from siblings by emphasizing read-only and anonymous nature, contrasting with tools for reporting damage or booking appointments.

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 includes explicit 'Use when:' and 'Nicht für:' sections, providing clear guidance on appropriate usage. It states that the tool is for finding experts, not for reporting damage, booking appointments, or legal advice. The context of sibling tools further reinforces differentiation.

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 indicate a write operation, but the description adds crucial context: it creates a lead, sends a WhatsApp link, and requires GDPR consent. It details the rejection behavior if consent is missing and outlines the return structure. No contradictions with 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 labeled sections (WICHTIG, Ablauf) and front-loads the core purpose. Though verbose, it is justified given the complexity and legal requirements. Could be slightly more concise, but overall efficient.

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's complexity (10 parameters, legal requirements, multi-step workflow), the description is fully complete. It explains prerequisites, workflow order, return values, and ties in sibling tools. The presence of an output schema is acknowledged.

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 baseline is 3. The description does not add further semantics beyond what the schema already provides for each parameter. While it provides overarching context for einwilligung_erteilt, the parameter-specific description in the schema already covers that.

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: reporting a car damage and triggering expert/appointment booking. It distinguishes itself from sibling tools by outlining the required preceding step (claimondo_finde_gutachter_termine) and explicitly noting it's a write action, not a read.

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 specifies when to use: after finding appointment slots and obtaining user consent. Also states when not to use (without consent) and references the overall workflow, including the prerequisite tool. Warns against providing legal advice, providing clear usage boundaries.

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=true, so the agent knows it is safe. The description adds that the tool provides general information only, not individual legal advice, and always prompts the next step (expert appointment). This additional context about non-legal nature and required follow-up is valuable beyond the 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 concise yet comprehensive, starting with the primary function and then listing specifics. It uses bullet points in the list of claim types and references laws efficiently. Every sentence serves a purpose, and the instruction about offering an expert appointment 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?

Given the tool's complexity (legal domain, multiple claim types, mandatory follow-up), the description covers all necessary aspects: what it does, required input, the follow-up expectation, and its non-legal advisory nature. The existence of an output schema likely covers return values, making the description complete for agent understanding.

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% with descriptions for both parameters: schuldfrage (enum, required) and schadenart (optional). The description repeats that schuldfrage should be asked first but does not add new details about the parameters beyond what is in the schema. Thus, it meets but does not exceed the baseline.

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 that the tool delivers structured compensation claims for car accident victims based on fault, referencing specific claim types and legal sections. It also uniquely includes the mandatory next step of offering an expert appointment, distinguishing itself 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 says to use it for advisory questions ('Nutze es für Beratungsfragen') and instructs to first ask the fault question. It warns that an advisory without offering an expert appointment is incomplete, implying when not to use it or what else must follow. However, it does not explicitly list conditions for avoiding the 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?

Annotations state readOnlyHint=false (write operation) and openWorldHint=true (side effects). The description adds specific behavioral traits: it creates a lead and callback task in a dispatch queue, typical callback time under 15 minutes, and mentions data processing via an AI service in the US. This goes beyond annotations 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?

The description is two short paragraphs, front-loaded with the core purpose and effect. Every sentence is informative: first paragraph gives purpose, effect, and differentiation; second paragraph lists required/optional parameters and the mandatory consent condition. No 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?

Given the 8 parameters, 3 required, annotations, and an output schema, the description covers purpose, effect, usage conditions, and behavioral traits. It does not explain the return value (but output schema exists). It lacks detail on error handling or what happens if consent is false, but overall it is complete enough for an agent to invoke 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 baseline is 3. The description adds crucial context for the consent parameter ('einwilligung_erteilt') by repeating the condition and linking it to data processing. It also summarizes the key required fields (name, phone) and optional ones (type of damage, concern, postal code). This 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 the tool requests a free callback from a Claimondo consultant, with the verb 'Fordert ... an' and the resource 'Telefon-Rückruf'. It explicitly distinguishes itself from sibling 'claimondo_melde_schaden' by calling it the second funnel arm for customers who prefer being called or when other channels don't fit.

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 provides explicit when-to-use conditions: 'für Kunden die lieber angerufen werden (oder wenn kein Slot passt / Daten fehlen)'. It also gives a critical usage rule: 'Rufe dies NUR mit einwilligung_erteilt=true auf, NACHDEM der Nutzer ... zugestimmt hat', making it clear when not to use (without consent).

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