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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: fetching from CDN, applying project customizations, and automatically injecting BAAS_API_KEY from environment. However, it lacks details on error handling, rate limits, or authentication requirements, which would be valuable for a tool with external dependencies.

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. Each sentence earns its place: the first states what it does, the second provides usage scenarios, the third explains the optimization approach, and the parameter/return sections add necessary details without redundancy. No wasted words.

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 complexity (3 parameters including nested objects, external CDN dependency) and the presence of an output schema (which handles return value documentation), the description is mostly complete. It covers purpose, usage, parameters, and key behaviors like environment variable injection. The main gap is lack of error/edge case handling information, which prevents a perfect score.

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 description coverage is 0%, so the description must compensate. It provides meaningful context for all three parameters: 'project_config' is explained as containing 'default_callback, company_name, etc.', 'language' as 'Target programming language', and 'features' with a concrete example list. This adds significant value beyond the bare schema, though it doesn't specify all possible feature values or config properties.

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 specific action ('Create a complete message service template') and the method ('by fetching from CDN and customizing with project config'). It distinguishes itself from sibling tools like 'generate_direct_api_code' and 'get_code_template_url' by focusing on template creation with customization rather than code generation or URL retrieval.

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 provides usage scenarios ('Perfect for: New project setup, team standardization, rapid prototyping') and includes a 'Token-optimized' note that hints at efficiency considerations. While it doesn't name specific alternatives, the context of sibling tools suggests differentiation in 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool fetches maintained templates from a CDN (not generating locally), returns a dictionary with code, filename, and instructions, and automatically injects BAAS_API_KEY if set. However, it lacks details on error handling, rate limits, or authentication requirements beyond the API key mention.

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, followed by usage guidelines, token optimization note, parameter details, and return value explanation. It's appropriately sized, though the 'Returns' section could be slightly more concise by integrating details into fewer sentences.

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 moderate complexity (3 parameters, no annotations, but has an output schema), the description is largely complete. It covers purpose, usage, parameters, and return behavior. The output schema exists, so the description needn't detail return values extensively, but it still provides useful context on code injection and authentication. Minor gaps include lack of error handling or prerequisite 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?

The schema description coverage is 0%, so the description must fully compensate. It does so by clearly explaining all three parameters: 'language' (programming language options), 'framework' (optional framework examples), and 'include_examples' (includes usage examples and configuration templates). This adds significant meaning beyond the bare 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's purpose: 'Generate code that directly calls BaaS API by fetching templates from CDN.' It specifies the verb ('Generate'), resource ('code'), and mechanism ('fetching templates from CDN'), distinguishing it from siblings like 'create_message_service_template' (which likely creates templates) and 'get_code_template_url' (which retrieves URLs).

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 provides usage guidance: 'Perfect for: Production deployments, custom integrations, framework-specific implementations.' It also contrasts with alternatives by noting token optimization via CDN fetching instead of local generation, helping differentiate from potential sibling tools that might generate code locally.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it returns a CDN URL to markdown files, specifies the templates include direct API calls to specific endpoints, and mentions the templates are 'optimized' and 'maintained.' However, it doesn't cover potential rate limits, authentication needs, or error handling, leaving some gaps for a tool that interacts with external resources.

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 appropriately sized and front-loaded, starting with the core purpose, followed by usage guidelines, parameter details, and return information. Every sentence earns its place by adding value without redundancy, making it efficient and easy to parse.

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 moderate complexity (3 parameters, 1 required) and the presence of an output schema (which covers return values), the description is largely complete. It explains parameters well and provides usage context. However, it could benefit from more behavioral details like error cases or performance considerations, slightly reducing completeness.

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 description coverage is 0%, so the description must compensate fully. It successfully adds meaning beyond the schema by explaining each parameter's purpose: 'language' is for programming language with examples, 'framework' is optional with framework examples, and 'deployment_platform' is optional with platform examples. This provides clear context that the schema alone lacks.

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 specific action ('Get URL') and resource ('BaaS SMS/MMS integration code template from CDN'), distinguishing it from siblings like 'create_message_service_template' (creation), 'generate_direct_api_code' (code generation), and 'get_integration_guide' (guide retrieval). It explicitly mentions the purpose is to obtain optimized, maintained code templates without token overhead.

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 guidance on when to use this tool ('Perfect for: Getting optimized, maintained code templates without token overhead'), distinguishing it from alternatives like generating direct API code or getting integration guides. It clearly indicates this is for retrieving pre-built templates rather than creating or generating custom code.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about fetching from a CDN, token optimization, and updates for new endpoints and authentication, which goes beyond basic functionality. However, it lacks details on potential errors, rate limits, or performance characteristics that would be helpful for a tool interacting with external resources.

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 sections for purpose, usage context, optimization note, arguments, and returns. Each sentence adds value, such as the token optimization note and endpoint updates. It could be slightly more concise by integrating some details, but overall it's efficient and front-loaded with key information.

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 an output schema, the description doesn't need to explain return values in detail, and it appropriately summarizes them. With no annotations and low schema coverage, the description compensates well by covering purpose, usage, parameters, and behavioral context like CDN fetching and updates. It's nearly complete for a retrieval tool, though minor gaps remain in error handling or advanced usage scenarios.

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 0%, so the description must compensate. It provides meaningful semantics for both parameters: 'platform' is described with examples (e.g., vercel, netlify, docker) and 'deployment_type' with options (development, staging, production). This adds significant value beyond the bare schema, though it doesn't specify format constraints or default behaviors beyond the schema's default for deployment_type.

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 fetches integration guides from a CDN for specific platforms and deployment scenarios, using specific verbs ('Get detailed integration guide by fetching from CDN') and resources ('platforms and deployment scenarios'). It distinguishes from siblings like 'create_message_service_template' or 'generate_direct_api_code' by focusing on retrieval rather than creation or generation. However, it doesn't explicitly contrast with siblings beyond the general purpose.

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 ('Perfect for: DevOps setup, deployment planning, team onboarding') and mentions optimization aspects ('Token-optimized: Fetches from CDN instead of hardcoded responses'), which helps guide usage. It doesn't explicitly state when not to use it or name alternatives among siblings, but the context is sufficiently detailed to infer appropriate scenarios.

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