MantleKit Launch Planner

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

Annotations declare readOnlyHint=true and destructiveHint=false, but the description adds minimal behavioral context. The word 'estimate' implies approximation, but this is already suggested by the tool name. No additional behavioral traits (e.g., data source, accuracy assumptions) are disclosed.

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 of 10 words, with no redundant information. It is efficiently front-loaded and every word earns its place.

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 an output schema exists, the description does not need to explain return values. Annotations cover safety. However, the description is short and does not mention usage guidelines or parameter context beyond schema. For a simple estimation tool with 12 parameters, it is mostly complete but could add more context.

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 meaning beyond the schema's property descriptions. For example, 'appIdea' is already described as 'Plain-English description of the app idea.' No extra value is provided.

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: estimating time saved by using MantleKit versus building from scratch for a SaaS project. It uses a specific verb ('estimate') and resource ('MantleKit vs Custom Build'), and it's distinct from sibling tools (generate_project_blueprint, match_theme, 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 provides no guidance on when to use this tool versus its siblings. It does not mention any prerequisites, when not to use it, or alternatives. The context signals show sibling tools exist, but the description fails to differentiate usage scenarios.

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=true, idempotentHint=true, and destructiveHint=false, which indicate a read-only, non-destructive operation. The description adds no additional behavioral context (e.g., side effects, authorization needs, or output handling). While consistent, it does not enhance understanding 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 a single, front-loaded sentence that conveys the core function without extraneous words. Every word earns its place, making it highly concise and easily scannable.

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 (12 parameters) and the presence of an output schema, the description provides a reasonable high-level context ('pages, dashboard sections, entities, and MVP scope'). However, it does not elaborate on the output structure or how the parameters map to the blueprint, leaving some context to be inferred from the schema alone. An output schema exists, so detailed return values are not necessary, but a bit more context could improve completeness without harming conciseness.

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 descriptive parameter descriptions. The tool description adds no new meaning to the parameters; it only summarizes the output. Per guidelines, baseline is 3 when coverage is high, and the description does not compensate further.

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: converting an app idea into a MantleKit project blueprint with specific components like pages, dashboard sections, entities, and MVP scope. The verb 'turn' combined with the output 'blueprint' makes the purpose unambiguous, and it distinguishes itself from sibling tools (compare_build_vs_custom, match_theme, recommend_setup) 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 implies usage context (when you have an app idea and want a blueprint) but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance. The agent can infer usage from the title and description, but lacks explicit exclusions or comparisons.

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, indicating a safe, read-only, idempotent operation. The description aligns with these annotations by stating it 'recommends' a theme, which is inherently non-destructive. While the description adds no further behavioral details (e.g., about output format), it is consistent and does not contradict 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 of 15 words, front-loading the action and resource. Every word is informative and no extraneous information is present. It is optimally concise for 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?

Given the tool's simplicity (recommendation task), full schema coverage, presence of annotations, and existence of an output schema, the description is complete. It communicates the tool's function and relevant inputs sufficiently. The output schema covers return value details, so the description does not need to explain them.

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% description coverage, meaning each parameter already has a description in the schema (e.g., 'appType: Type of product being built'). The description adds 'product type, audience, and brand direction' which maps directly to parameters but does not provide additional semantic meaning beyond the schema. Per rubric, baseline is 3 when schema coverage >80%.

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: 'Recommend the best MantleKit theme for a product type, audience, and brand direction.' The verb 'recommend' and resource 'MantleKit theme' are specific, and the inputs (product type, audience, brand direction) are explicitly mentioned. This distinguishes it from sibling tools like 'compare_build_vs_custom' 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 tool should be used when a user needs a theme recommendation based on product type, audience, and brand direction. However, it does not explicitly state when to use this tool versus the alternatives (e.g., 'compare_build_vs_custom', 'recommend_setup'), nor does it provide any exclusion criteria. The usage context is implied but not fully guided.

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, idempotentHint, and destructiveHint, so the description adds no further behavioral context (e.g., how recommendations are made, whether they are deterministic, or if user can influence them). The description is safe but minimal.

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?

Single sentence with no wasted words. It is front-loaded and concise, covering the tool's purpose efficiently.

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 an output schema (not shown), the description is very brief for a tool with 11 parameters and a recommendation task. It does not explain how to best provide input (e.g., the appIdea should be detailed) or what the output contains. More context would improve usability.

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?

All 11 parameters are described in the input schema (100% coverage). The description does not add any extra semantic meaning beyond what the schema provides (e.g., no hints about which parameters are more important or how they interact). 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?

Description explicitly states 'Recommend the best MantleKit tier, theme, and built-in feature set for a new app idea.' This provides a specific verb and resource, clearly distinguishing it from siblings like compare_build_vs_custom, generate_project_blueprint, and match_theme, which focus on comparison, blueprint generation, and theme matching respectively.

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?

No explicit guidance on when to use this tool versus its siblings. The usage context ('for a new app idea') is implied but not contrasted with alternatives, such as when to use generate_project_blueprint instead. Would benefit from conditional use cases.

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