MantleKit Launch Planner

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

Annotations already declare the tool as read-only and idempotent, and the description aligns by saying 'estimate'. It adds a small scoping detail ('for a SaaS project') but does not describe any further behavioral aspects like output format or limitations. With annotations covering safety, this is an acceptable 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 a single, front-loaded sentence with no filler. Every word contributes to the purpose, making it exceptionally concise 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?

The tool has a rich input schema and an output schema, so the description doesn't need to explain parameters or return values. The one-sentence summary is sufficient for understanding the core function, though it could hint at what factors the estimate considers (e.g., team size, features) to edge closer to 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%, so the baseline is 3. The description mentions no parameters at all, relying entirely on the input schema to explain appIdea, needsCms, teamSize, etc. It adds no extra meaning beyond what the schema provides.

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 ('Estimate') and names the resource ('where MantleKit saves time versus building from scratch'), scoped to SaaS projects. This clearly distinguishes it from sibling tools like generate_project_blueprint or recommend_setup.

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 implies the use case (comparing MantleKit against a custom build for SaaS), but provides no explicit when/when-not guidance or references to alternatives. The context is clear, but the tool doesn't state when it should be chosen over 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 declare read-only and idempotent behavior, so the description does not need to repeat safety traits. It adds useful context about the blueprint contents but does not disclose additional behavioral traits such as processing details or limitations. 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 a single sentence, front-loaded with the action verb, and contains no redundant information. It conveys the core purpose in an efficient, well-structured manner.

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 generation tool with a comprehensive schema and an output schema, the description provides sufficient context about the tool's role and deliverables. It does not explain return values or edge cases, but the output schema handles that, making this adequately 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%, so the baseline is 3; the description does not add parameter-level meaning beyond what is already in the schema. It only references 'app idea' generally, which maps to required parameters but offers no additional clarity.

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 a specific verb ('Turn') and resource ('app idea into a MantleKit project blueprint') and enumerates concrete outputs (pages, dashboard sections, entities, MVP scope). This distinguishes it from sibling tools like match_theme or compare_build_vs_custom, which focus on different aspects.

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 implies use cases (when you have an app idea and need a blueprint) but does not explicitly state when to use this tool over siblings or provide exclusions. The context is clear but there is no direct comparison with 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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context such as ranking behavior, result format, or limitations, but does not contradict the annotations either.

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?

A single, front-loaded sentence that communicates the tool's purpose and primary inputs with no wasted words. Every element is meaningful and easy to scan.

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 presence of a complete output schema and informative annotations, the description covers the essential decision context. It could mention that audience and brandAdjectives are optional, or explain how 'best' is determined, but the combination of schema and description is sufficient for most selection use cases.

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 parameters are already well-documented. The description maps 'product type', 'audience', and 'brand direction' to appType, audience, and brandAdjectives, but adds no extra semantics beyond what the schema already provides.

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 ('Recommend'), a clear resource ('MantleKit theme'), and the key inputs ('product type, audience, and brand direction'). This clearly distinguishes it from sibling tools like compare_build_vs_custom or generate_project_blueprint, which address different decisions.

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 conveys a clear context: use this tool when you need a theme recommendation based on product type, audience, and brand direction. It does not explicitly list exclusions or alternatives, but the use case is sufficiently clear for a recommendation tool of this scope.

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, and destructiveHint=false, covering the safety profile. The description adds no extra behavioral context beyond restating the purpose, such as what inputs it relies on or what the recommendation process involves. This is acceptable but not additive.

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, front-loaded sentence with no fluff. Every word contributes to the tool's 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?

With 11 parameters, the schema documents them all and an output schema exists. The description explains the overall goal and context, though it could have explicitly noted that it considers the user's needs (as captured by the boolean fields). Still, the combination is adequate for a recommendation 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?

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description itself does not elaborate on parameters, but the schema handles them well, so no additional compensation is needed.

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 a specific verb ('Recommend'), a specific target ('best MantleKit tier, theme, and built-in feature set'), and context ('for a new app idea'). This makes it distinct from sibling tools like compare_build_vs_custom or match_theme.

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 phrase 'for a new app idea' implies the appropriate use case, but there is no explicit guidance on when not to use it or how it differs from sibling tools. It provides enough context to infer usage, but no exclusions or alternatives are mentioned.

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