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

No annotations provided, so description carries full burden. It does not disclose what kind of output is returned (e.g., score, category) or any behavioral traits like side effects or 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?

Single sentence, front-loaded with purpose. Could be more structured but is concise and clear.

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?

No output schema, so description should explain return values. It does not. For a single required parameter tool, missing output details is a gap.

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 baseline is 3. Description lists the factors (software, hardware, etc.) which adds context, but does not explain parameters beyond what 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 clearly states the tool estimates technical difficulty based on multiple factors (software, hardware, AI, regulation, team size, time, budget). It distinguishes from siblings 'generate_pitch_summary' and 'validate_startup_idea' which focus on other 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?

No explicit guidance on when to use this tool vs alternatives. It implies use for difficulty assessment, but lacks context for alternatives or when-not-to-use.

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 for behavioral disclosure. It merely says 'generate a concise pitch summary', which implies a read-only text generation but gives no details on side effects, auth requirements, rate limits, or what happens if input is insufficient. Minimal transparency beyond the basic action.

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 with 12 words, highly concise with no waste. It is front-loaded with the core action. However, it could be slightly more informative without sacrificing brevity.

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 4 input parameters, no output schema, and sibling tools, the description is insufficiently complete. It doesn't specify the output format (text or structured), length constraints, or how this tool differs from siblings. For a generation task, more contextual completeness is expected.

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 no additional meaning beyond the schema's parameter descriptions. It does not explain parameter relationships, constraints beyond minLength, or provide examples.

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 concise hackathon or startup pitch summary for an idea. It uses a specific verb ('generate') and resource ('pitch summary'), and the context 'hackathon or startup' adds specificity. While it does not explicitly distinguish from siblings, the sibling tools have distinct purposes (difficulty estimation and validation), making the differentiation implicit.

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 vs alternatives. It only states what the tool does, leaving the agent to infer usage context. No exclusions, prerequisites, or alternative tool references are given.

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?

No annotations are provided, so the description bears full responsibility. It describes what the tool estimates but does not disclose behavioral traits such as whether it makes external API calls, requires authentication, or is read-only. The description lacks depth about side effects or 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 a single sentence that front-loads the core purpose and lists the estimation aspects concisely. It is efficient but could benefit from slight restructuring or bullet points for 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 the tool's complexity (6 parameters, no output schema), the description does not specify the return value or format. It only lists input aspects, leaving the agent to guess the output structure, which is a significant gap.

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 each parameter is described. The tool description adds overarching context about the estimation dimensions, but it does not provide new semantic detail for individual parameters beyond what the schema already offers.

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 validates a startup or hackathon idea by estimating multiple aspects (difficulty, competitors, APIs, monetization, risks, steps, patent search). This distinguishes it from sibling tools like estimate_build_difficulty (focuses only on difficulty) and generate_pitch_summary (focuses only on pitch summary).

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 is comprehensive by listing many estimation aspects, but it does not explicitly state when to use this tool versus its siblings. However, the context is clear enough for an agent to infer usage for full validation rather than single-aspect tasks.

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