lexvibe

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

The description adds value beyond annotations by stating it is 'read-only, instant' and that the answer matches documents LexVibe would produce. Annotations already declare readOnlyHint=true. No contradictions. It does not discuss rate limits or other behaviors, but the added context is sufficient.

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, using three sentences to convey all necessary information. It includes a clear example, and there is no redundant or extraneous text.

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?

Despite lacking an output schema, the description fully explains what the tool returns: frameworks, documents, cookie-consent regime, and GPC status. It also mentions it is read-only and instant, covering essential behavioral aspects for a query tool.

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% for the single parameter 'markets'. The description adds meaning by explaining its purpose ('markets the product serves') and providing an example ('["eu", "us"]'). This adds context beyond the schema's own 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's function with specific verbs and resources: 'return every framework that applies, the documents it must publish, the strictest cookie-consent regime, and whether a universal opt-out signal has to be honoured.' It distinguishes itself from sibling tools like 'generate_policies' by noting it's the read-only version.

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 usage guidance with an example: 'Use it to answer 'I sell to the EU and Texas — what do I need?'.' It also contrasts with generate_policies, indicating when to use this tool instead. However, it does not explicitly state when not to use it.

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 annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Read-only and instant.' It goes beyond annotations by adding crucial behavioral context: defaults 'never over-report risk' and specific compliance deadlines for obligations. This transparency about safety defaults and output behavior exceeds what annotations provide.

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 core purpose, then efficiently enumerates parameter groups in a numbered list. Every sentence earns its place: purpose, deadlines, parameter grouping, and usage guidance. Despite covering 14 parameters, it remains dense and structured with no fluff. This is a model of concise, high-density documentation.

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 (14 optional parameters, no output schema), the description covers the essential aspects: what it returns (risk level, obligations with deadlines), how to answer parameters, and the conservative default behavior. It does not specify the exact structure of the returned obligations, but it gives sufficient high-level information for an agent to know what to expect. The lack of an output schema raises the burden, and a short note on the return format would make it a 5.

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 value by grouping the 14 booleans into three logical categories (transparency triggers, prohibited practices, high-risk triggers) and explaining the dependency between realtimeBiometricPublic and realtimeBiometricLawEnforcement. This organization aids the agent in understanding the parameter structure, going beyond the schema's per-parameter descriptions.

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 function: 'Classify a product's risk level under the EU AI Act — minimal, limited, high or prohibited — and return the applicable obligations, each with its compliance deadline.' This is a specific verb+resource with distinct output. It also distinguishes itself from sibling tools (e.g., check_website, check_store) by focusing on AI Act risk classification.

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 clear context for when to use the tool: it classifies products under the EU AI Act. It gives behavioral guidance ('Answer the boolean questions about what the system does; leave unknowns at their defaults') and notes the tool never over-reports risk. However, it does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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 and destructiveHint=false. The description adds behavioral context by explaining that it follows the developer-declared privacy-policy link, analyzes that page, and returns specific outputs. It explicitly says 'Read-only' and emphasizes no repo/developer-account access, reinforcing the non-invasive nature 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 and front-loaded: three sentences deliver the tool's purpose, input requirements, behavior, and outputs. Every sentence earns its place with no filler or repetition.

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?

Despite no output schema, the description lists four concrete return categories (detected data processing, compliance recommendations, EU AI Act applicability, suggestedAnswers) and specifies supported stores and required input. It lacks edge-case details like privacy-policy link absence, but for a one-parameter tool this is sufficiently 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?

The schema covers the url parameter 100%, including format and examples. The description only restates that it is a public listing URL, adding no meaning beyond the schema. Baseline 3 is appropriate because the schema does the heavy lifting.

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 uses a specific verb ('check') and resource ('app-store listing compliance'), and clearly specifies the input (public App Store or Google Play listing URL). It distinguishes itself from siblings by explaining it follows the privacy-policy link and returns compliance recommendations, EU AI Act applicability, and suggestedAnswers.

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?

It provides clear context: use when you have a public app-store listing URL and no repo or developer-account access. It stops short of explicitly naming alternatives or stating when not to use it, but the scope is well-defined and implies suitable use cases.

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 goes far beyond the readOnlyHint=true annotation by disclosing server-side fetching, the exact meaning of 'fetched' (true only when HTML was actually read), error categories ('unreachable', 'blocked'), and the drift-report semantics (statuses 'in_sync', 'outdated', 'unavailable' with bounded reasons). It also states that detected signals are NOT meaningful when fetch fails. This rich behavioral contract is absent from the annotations and schema.

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 long but information-dense, with purposeful structuring via colons and examples. It front-loads the core purpose and then uses conditional clauses to explain edge cases. No sentence is wasted; however, the density makes it moderately heavy to parse. It earns a 4 rather than 5 due to the complexity of the prose, but remains efficient for the tool's 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?

Despite the absence of an output schema, the description fully covers the result contract: 'fetched' semantics, 'fetchError' reasons, drift key with statuses and bounded reasons, and how to interpret each. It also explains when to treat results as unknown. For a tool with this complexity, this is outstandingly complete – the agent has all necessary context to act 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 schema already provides full descriptions of both 'url' and 'appId' (100% coverage). The description adds value by explaining the behavioral consequence of appId: 'When provided (or when the page already carries the installed LexVibe snippet), the result includes a drift report.' It also mentions that url can be a bare domain, reinforcing the schema. No new parameter details are needed beyond the schema, but the behavioral linkage elevates it above 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 opens with 'Free legal-compliance check of a public website' – a specific verb ('check') and resource ('website compliance'). It clearly distinguishes from siblings like check_ai_act and check_store by enumerating the detected data-processing categories (analytics, marketing pixels, payments, generative AI, email collection, third-party sharing) and the output (legal documents, cookie-consent setup, EU AI Act applicability). This leaves no ambiguity about what the tool does.

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: 'Run it again after adding any SDK, analytics, payment, auth or AI integration.' It also explains when results are unreliable ('when false... report the check as inconclusive, not as clean') and how to handle the drift status 'unavailable' ('treat as unknown, never as in sync'). This gives the agent clear decision rules for invoking and interpreting 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 indicate readOnlyHint=false and destructiveHint=false, which are generic. The description adds valuable behavior: the claim link expires in 30 minutes, confirmation triggers scanning and legal document generation, and the tool replaces placeholders. It also discloses the async nature via needing to call get_claim_status afterward. However, it doesn't mention what happens if neither url nor appName is provided (validation error) or potential side effects like overwriting existing data beyond the placeholder replacement.

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 paragraph that is information-dense but front-loaded with the main action. Each sentence adds new information: what it does, what it returns, expiration, confirmation effects, and follow-up. It's somewhat long but justified due to the multi-step process. No filler; slightly better than average.

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 tool has 6 params but no required ones, no output schema, and complex async behavior. The description covers the key steps: returning a claim link, expiry, user confirmation side effects, and the need to poll get_claim_status. It doesn't describe error cases (e.g., invalid URL) or what the install snippet contains, but given the output schema is absent and sibling tools exist for those, the description is fairly complete. The 30-minute expiry is noted, which is critical.

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 all parameters are documented with descriptions. The description itself adds context by grouping 'url' and 'appName' as alternatives and referencing check_ai_act and check_website outputs. However, it doesn't explain the interplay of all parameters (e.g., how platform interacts with url) beyond what the schema already states. Baseline 3 is appropriate because the schema carries the load.

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 creates a real LexVibe app in the user's account and replaces the YOUR_APP_ID placeholder. It distinguishes from siblings by naming get_claim_status and get_install_snippet as follow-up tools, and the context of claim link user confirmation makes the purpose specific and actionable.

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 show the returned link to the user, that it expires in 30 minutes, and instructs to call get_claim_status with the returned code after confirmation. It also provides a prerequisite: provide at least `url` or `appName`. This clearly directs when to use this tool and what to do next, effectively guiding flow.

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?

Beyond the readOnlyHint and destructiveHint annotations, the description discloses the output format (Markdown drafts), the difference between anonymous template-based generation and AI-tailored hosted versions, the locale cap, and the account requirement. This significantly enriches understanding of the tool's behavior 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 well-structured and front-loaded with the core purpose. It consists of four sentences, each providing essential information: what is generated, the output format, the integration with check tools, and the anonymous vs. account-based modalities. No redundant or filler content is present.

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?

Despite having no output schema, the description effectively conveys what the tool produces (Markdown drafts) and how the parameters influence the result (e.g., markets and locales). It covers the key operational details for a complex tool with nested objects and six parameters, making it 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?

The input schema already covers all parameters (100% coverage), so the baseline is 3. The description adds value by explicitly tying the `answers` parameter to check_website/check_store outputs and explaining that locales and markets determine legal frameworks and document languages. This goes beyond the schema's basic field descriptions.

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 function: generating legal documents (privacy policy, terms of service, AI disclosure) localized to target markets. It is specific about the resource and distinguishes it from sibling tools like check_website and check_store, which analyze rather than generate documents.

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 clear usage context, including how to pass suggestedAnswers from check_website or check_store to tailor documents. It also explains when the anonymous template-based generation applies (up to 3 locales) versus when a LexVibe account is needed. However, it does not explicitly state when not to use this tool or name alternative generation tools, so it stops short of a 5.

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?

Beyond the readOnlyHint annotation, the description reveals key behaviors: pending status shape, the 30-minute link expiry, and the fact that on success it returns the real app ID, install snippet, and privacy-policy URL. It also instructs to replace the placeholder, which is non-obvious and valuable. No contradiction 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 two sentences, front-loaded with the primary purpose, and every detail earns its place. It is concise without being under-specified, and the structure flows logically from purpose to behavior to usage note.

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 simple 1-parameter schema and no output schema, the description fully explains both the input (claim code) and the output shape (pending vs. successful response with real ID and snippet). It even covers the expiry behavior and post-processing instruction. Nothing is missing.

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 fully describes the only parameter (code) with a description matching the tool's mention. The description does not add any additional semantics beyond what the schema already provides, so the baseline score of 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 states a specific verb and resource: 'Check whether the user has confirmed a claim created with claim_app.' This clearly distinguishes it from siblings like claim_app (which creates the claim) and get_install_snippet (which retrieves a snippet but not for pending claims). The purpose is 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 provides clear when-to-use context: it is the polling tool for claim confirmation. It also tells the user to wait and call again if status is pending. However, it doesn't explicitly state when not to use it or name alternatives, so a 4 is appropriate.

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 and destructiveHint=false. The description adds meaningful behavioral context beyond annotations: the output varies by platform, includes a JSX variant for React/Next.js, and specifies exact placement contexts (before </head>, App Store Connect/Google Play Console fields). No contradiction 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 compact: two sentences with no filler. It front-loads the core purpose and then efficiently covers web, iOS/Android, and the no-app-id case, earning its place for each clause.

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 tool with no output schema, the description covers all necessary use cases: web script snippet with JSX variant, iOS/Android hosted policy URL and exact store fields, and the prerequisite of an app id with a clear fallback to claim_app. This is sufficiently complete for an agent to select and 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?

Schema coverage is 100%—all three parameters have clear descriptions. The tool description adds some context around platform-specific output but does not add parameter-level meaning beyond the schema. Since the schema already documents appId, accent, and platform thoroughly, the baseline score of 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 uses the specific verb 'Return' and clearly identifies the resource ('what to install for a LexVibe app id'), then enumerates platform-specific outputs. It distinguishes itself from sibling tools like claim_app (create an app) and generate_policies (generate policies), so the agent knows this is about retrieving an install snippet.

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 names the alternative: 'No app id yet? Call claim_app to create one in the user's account.' It also clarifies that web returns a script snippet and iOS/Android return policy URLs and store fields, giving the agent clear context on when this tool is appropriate and what to use instead when precondition not met.

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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds 'Read-only, instant,' which provides performance expectations and reinforces safety. 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 front-loaded with the core action ('List every privacy and AI framework…') and includes many examples, which is informative but somewhat lengthy. Still, every sentence adds value and no fluff.

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 specifies output fields (id, jurisdiction, effective date, official-text URL) despite no output schema. It covers filtering and read-only nature. It lacks pagination details, but for a static list, it is sufficiently 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 description coverage is 100% with both parameters already described (market enum, jurisdiction string). The description lists example jurisdictions (e.g., 'us-tx', 'br', 'eu') but adds no new syntax or constraints beyond the schema. Baseline score of 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 lists every privacy and AI framework LexVibe tracks, with specifics about 60 laws across 60+ jurisdictions and examples like GDPR, ePrivacy, EU AI Act, etc. It distinguishes from siblings like lookup_framework by indicating it returns a comprehensive list with optional filters.

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 explains optional filtering by market or jurisdiction, implying usage for browsing or overview. It does not explicitly state when to use this tool versus alternatives like applicable_frameworks or lookup_framework, but the context is clear enough for a simple list operation.

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 and destructiveHint=false. The description reinforces these with 'Read-only, instant, no signup,' adding value beyond the structured fields.

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, front-loaded with the primary action, and efficiently uses two sentences to cover purpose, details, and usage guidance 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?

Given no output schema, the description lists many returned fields and includes official text and authority, which is complete enough for a lookup tool with strong annotations.

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% and the parameter description includes examples and a reference to list_frameworks for obtaining valid ids, adding meaning beyond the 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 description clearly states it looks up a privacy or AI law by id and returns structured facts, listing specific fields. It distinguishes itself from sibling tools like list_frameworks by focusing on detailed lookup vs. listing ids.

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 advises calling list_frameworks first if the id is unknown, providing clear context for when to use an alternative. No explicit when-not-to-use, but the guidance is sufficient.

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