Ideabob Validation

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

With no annotations, the description should disclose side effects, auth needs, or rate limits. It only says the output is ready for PDF generation, lacking details on whether the export is destructive or requires authentication.

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 that efficiently conveys the tool's purpose and output format. It lists components but remains readable. Could be slightly shorter.

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 tool with one parameter and no output schema, the description adequately explains the output (JSON for PDF). However, it lacks input examples, error handling, or hints about when to call this after using sibling tools.

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 parameter ideaId has no description in the schema (0% coverage), and the tool description does not clarify its meaning, format, or source. The agent must infer it from context.

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 exports a complete idea report with specified components (scores, validations, etc.) as JSON for PDF generation. It distinguishes from sibling tools that focus on individual analyses.

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 this tool is used to get the full report but does not explicitly state when to use it versus alternatives like generate_competitor_analysis or score_idea. No when-not or prerequisite information provided.

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 carries the full burden. It states the tool is 'AI-powered' and returns analysis, but does not disclose whether it is read-only, any side effects, authentication needs, or limitations. For a generative analysis tool, more transparency on safety and behavior is expected.

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 two sentences with no wasted words. The first sentence states the core purpose, and the second provides details on inputs and outputs, following a logical structure.

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 parameters, no output schema, and no annotations, the description covers essential usage and outputs. It is missing details about the description parameter and potential output format, but overall adequately informs an 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 coverage is 0%, so the description adds meaning beyond the schema. It clarifies that idea and websiteUrl are paired for ad-hoc use, while ideaId is an alternative. However, the description parameter is not explained, leaving its purpose ambiguous.

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 the tool runs AI-powered competitor analysis, specifies input options (ideaId or ad-hoc with idea and websiteUrl), and lists outputs (competitors, strengths/weaknesses, positioning, competitive score). It distinguishes from sibling tools like export_idea_report, score_idea, and validate_market_fit.

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?

Description explains when to use ideaId versus ad-hoc inputs, providing clear context. However, it does not explicitly state when not to use the tool or mention alternative approaches.

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, description discloses main output (score, label, breakdown, reasoning) and the 9 dimensions, but lacks additional behavioral context like idempotency, limits, or side effects.

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?

Two sentences with no wasted words. First sentence states action, second outlines return value. Efficient and front-loaded.

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 (9 dimensions) and no output schema, the description provides essential output details (score range, decision label, breakdown, reasoning). Could elaborate on the dimensions or scoring process, but sufficient for basic understanding.

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 schema already describes parameters adequately. Description does not add new meaning beyond stating the scoring context, resulting in a baseline score.

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 scores a startup/product idea across 9 dimensions and returns a score, decision label, breakdown, and reasoning. Specific verb 'score' and resource are present, but no explicit differentiation from sibling tools like export_idea_report.

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 guidance on when to use this tool versus alternatives. Does not mention prerequisites, exclusions, or comparison to 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?

With no annotations, the description carries the burden of disclosing behavior. It mentions 'AI-powered' and the returns, but does not address side effects, storage, or rate limits. It is adequate but lacks depth.

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?

Two sentences efficiently convey purpose and input/output. No redundant words, though a structured list of parameters would improve 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?

With 5 parameters, no output schema, and no annotations, the description is incomplete. It lists outputs but not their format or structure, and does not clarify valid input combinations or constraints.

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 explain parameters. It only hints at 'ideaId' and 'description' usage, ignoring 'idea', 'websiteUrl', and 'targetProblem'. Without mapping, the agent cannot use all parameters correctly.

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 performs 'AI-powered market fit validation' and lists specific outputs (problem hunt, size check, competition map, traffic prediction, verdict). It distinguishes from siblings like 'score_idea' and 'generate_competitor_analysis' by covering a broader validation scope.

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 does not provide guidance on when to use this tool versus alternatives. It only states input options ('ideaId or ad-hoc idea description') but no context on prerequisites, fallbacks, or exclusions.

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