Cabgo Builder

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds valuable context: 'Public (no auth)' (auth requirement), 'Returns the full product catalog' (scope of response). No 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?

Three sentences, each essential: first states core function, second gives usage triggers, third provides explicit negative guidance. No waste, front-loaded with key info.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple info tool with no parameters or output schema, the description covers purpose, when/not to use, auth requirements, and return content. Completely adequate for an AI agent to select and 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?

Zero parameters; baseline for 0 params is 4. Description compensates by explaining what the tool returns (full catalog details). Schema coverage is trivially 100%.

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?

States specific verb 'describe' and resource 'what Cabgo is / full product catalog'. Explicitly distinguishes from sibling tools like cabgo_create_my_app and cabgo_pricing by noting when not to use it.

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 when to use (user asks what Cabgo is) and when not to use (as pre-step for cabgo_create_my_app), with clear alternative (go directly to cabgo_create_my_app).

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, openWorldHint=true, destructiveHint=false. The description adds valuable context: free workspace (no payment), OAuth user becomes owner automatically, multiple tenants allowed (re-call creates new workspace), and billing handled externally. 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 dense but well-organized, covering purpose, usage rules, behavioral notes, and parameter hints in one paragraph. It could be slightly more scannable with bullet points, but every sentence adds value with no 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 6 parameters and no output schema, the description sufficiently covers the tool's effect (materializes workspace, queues build), ownership, reusability, and limitations. It doesn't describe return values, but for a mutation tool this is acceptable. The description ensures the agent can use the tool safely.

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 has 100% description coverage, but the description adds practical guidance: prompts to ask for businessType and brandName, mentions city and primaryColor as optional, and notes defaults for primaryColor. This helps the agent understand which parameters are essential and their typical values.

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 explicitly states the tool materializes a free Cabgo workspace and queues an Android build. It distinguishes from siblings by naming cabgo_install_instructions for setup questions and mentions branding tweaks via other tools. The verbs 'create', 'launch', 'build me', 'set up an app for' clearly specify trigger intent.

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-call guidance with example verbs and a clear alternative (cabgo_install_instructions). It instructs the agent to ask two specific questions before calling, ensuring proper context collection.

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?

Description adds substantial behavioral context beyond annotations: no auth required, return fields (companyId, activationUrl, welcome email), preset one-call bootstrap, triggerBuild queue, default cash-only, trial duration, and required fields. 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 efficient but somewhat lengthy due to detailed parameter explanations. Front-loaded with core purpose and key features. Bolded phrases help scanning. Slight room for trimming 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?

Given 16 parameters, high schema coverage, no output schema, and nested objects, the description thoroughly covers behavior, defaults, required fields, and important notes. An agent can confidently invoke this tool with the provided information.

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 94% schema coverage, the description still adds significant value: ownerEmail reuse instruction, companyName as rider-facing, presetId avoids extra call, triggerBuild queues APK, presetContext overrides. Each parameter's meaning is enriched beyond the schema's basic 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?

Description clearly states it creates a Cabgo tenant in TRIAL state with no payment upfront. It distinguishes from siblings by specifying that presetId eliminates the need for a separate apply_preset call, and that triggerBuild queues an APK. The verb 'create' and resource 'trial tenant' are specific.

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 context: public (no auth), cash-only default, trial duration 14 days, and instructs to reuse ownerEmail from chat. Implies usage scenarios (trial creation) but does not explicitly state when to use alternatives like cabgo_start_signup.

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 read-only and non-destructive. The description adds behavioral context: that most assets are auto-generated and the operator only provides API access. It also details specific actions for each platform, which is beyond the annotation scope.

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 slightly dense but front-loaded with the core purpose. It uses multiple clauses without being overly verbose. Could be slightly more concise, but the information is well-organized.

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 adequately explains the checklist content and platform-specific steps. The tool has low complexity (1 optional param), so the description covers the essential context for an AI agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'platform' is fully described in the schema with enum values and a default. The description adds value by explaining the implications of each choice (e.g., for Play vs App Store), which provides meaning 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 returns the operator checklist for publishing. It specifies that most assets are auto-generated, so the operator only provides API access. This distinguishes it from sibling tools like cabgo_create_my_app or cabgo_install_instructions.

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: it's public, no auth, and explains platform-specific steps for Play and App Store. It also mentions the default platform value. However, it does not explicitly state when not to use this tool or list 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=true and destructiveHint=false, so the description adds value by describing the return content and parameter usage without contradicting annotations. No behavioral gaps remain.

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 detailed with bold structuring and many trigger phrases, which aids discoverability but adds length. Could be more concise while retaining key usage 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?

Despite having no output schema and one simple param, the description fully covers what the tool returns, when to use it, and how to use the parameter. No information gaps remain for an AI agent to correctly select and invoke the 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% with clear enum and description for the single optional param. The description repeats the enum values in a practical format, adding marginal clarity but not essential meaning 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 returns step-by-step installation and account setup instructions. It explicitly distinguishes itself from siblings by stating 'Use THIS for any installation / setup / connection' and provides specific trigger phrases, making purpose 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 explicitly states when to use (installation/setup/connection questions, new users) and provides trigger phrases. However, it does not directly mention when NOT to use or compare to sibling tools, though context is strong.

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 and destructiveHint=false. The description adds valuable context: it is public (no auth), informational only, and warns against generating payment URLs or handling purchases. This goes 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 well-structured. It starts with the key fact 'Public (no auth): informational pricing reference', then lists specific prices, and ends with crucial usage warnings. Every sentence serves a purpose.

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 no parameters and is a simple informational reference, the description is complete. It covers what the tool returns, its limitations, and the required follow-up action. No output schema exists, but return values are sufficiently described.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, and the schema coverage is 100%. The description adds meaning by detailing the exact pricing and packages returned, which enriches the empty 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 'informational pricing reference' and lists specific prices for monthly SaaS subscriptions and one-time packages. This distinguishes it from sibling tools like cabgo_about, cabgo_create_my_app, etc., which serve different purposes.

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 'Informational only' and provides clear instructions on what not to do (generate a payment URL) and what to do (direct users to the website). It implies when to use this tool: when pricing information is needed. 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?

Annotations are minimal (readOnlyHint=false, destructiveHint=false). The description adds valuable behavioral context: it states the tool is 'public (no auth)' and generates a URL, implying no side effects beyond creating a signup link. It doesn't detail the URL's behavior or backend actions, but the added context is sufficient for most agent decisions. Score 4 for meaningful supplementation 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 three sentences long, front-loaded with purpose and context. Each sentence contributes unique information: tool function, alternative recommendation, and usage condition. It could be slightly tighter but is well-structured and efficient. Score 4.

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 explains the tool's purpose, when to use it, and alternatives. However, it lacks details on the output (what the signup URL looks like, if it expires) and does not give guidance on which of the 8 optional parameters are typically needed. This leaves some ambiguity for the 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 description coverage is 88%, so the baseline is 3. The description does not elaborate on parameters beyond mentioning 'plan' in the flow context. It adds no new semantic guidance for parameters like phone, source, appName, etc., relying entirely on the schema. Therefore, no adjustment from 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 the tool generates a signup URL for a specific legacy flow (pay first then configure). It uses a specific verb ('generate') and resource ('signup URL'), and distinguishes it from the recommended cabgo_create_trial. 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?

The description explicitly tells when to use this tool ('only when the operator explicitly asks to pay before creating, or when targeting a marketing landing page') and when not to (recommends cabgo_create_trial for chat-driven onboarding). This provides clear decision criteria for the agent.

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