Ideabob Validation

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

No annotations are provided, so the description carries full burden. It discloses the output format (JSON ready for PDF) but lacks details on side effects, size limits, rate limits, or whether the report is generated on demand or fetched. Minimal behavioral context.

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 is concise but lacks structure (e.g., bullet points or separate sections). It efficiently conveys purpose but could be more organized.

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 minimal schema (one parameter, no output schema, no annotations), the description is incomplete. It fails to mention return value structure, error conditions, prerequisites (e.g., must have completed analyses), or input format details. The tool's usage context is insufficiently addressed.

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 one parameter (ideaId) with no description coverage from the tool description. The description does not explain the parameter's meaning, format, or constraints beyond what the schema already provides. This adds no value.

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 verb 'Export' and the resource 'a complete idea report' with specific contents (scores, validations, etc.), and specifies the output format (clean JSON for PDF). This distinguishes it from sibling tools that generate individual components like competitor analysis or 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?

The description implies this tool is for exporting the full report, but does not explicitly state when to use it versus the sibling tools (e.g., use after individual analyses are complete). There is no mention of prerequisites or exclusions.

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 provided, so description bears full burden. It does not mention whether the tool is read-only, has side effects, requires authentication, or has rate limits. The description only states it 'returns' data, implying no modification, but this is not explicit.

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 concise sentences with no wasted words. The first sentence states the purpose, the second details input options and output contents.

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 clear purpose, the description lacks details on output format (no schema), parameter explanation for all 4 params, and behavioral traits. It is incomplete given no annotations and no output schema.

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%, requiring the description to compensate. It only explains idea and websiteUrl as part of the ad-hoc mode, but omits explanation for ideaId (though mentioned in text) and description parameter, leaving 2 of 4 parameters unexplained.

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 runs AI-powered competitor analysis and distinguishes from siblings (export, score, validate market fit) by specifying the exact output: competitors, strengths/weaknesses, positioning, and competitive score.

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 says to provide either an existing ideaId or ad-hoc { idea, websiteUrl }, guiding the agent on two usage modes. However, it does not mention when not to use this tool or compare to alternatives like score_idea or validate_market_fit.

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?

Without any annotations, the description carries full burden. It discloses that the tool scores across 9 weighted dimensions and returns a structured output, but does not mention any side effects, authentication requirements, rate limits, or data usage. The behavioral traits are moderately transparent but lack 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?

The description is very concise: two sentences that front-load the purpose and follow with the return elements. No redundant information, 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?

For a scoring tool, the description covers the basic purpose and output but omits details like the specific 9 dimensions, weighting criteria, or scoring scale. Without an output schema, more context on the breakdown and reasoning would be helpful for complete 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?

The schema coverage is 100% (all parameters have descriptions). The tool description adds global context that parameters feed into a weighted scoring algorithm, but does not enrich individual parameter meanings beyond what the schema already provides. Baseline 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?

The description clearly states the tool evaluates a startup or product idea across 9 weighted dimensions and returns specific outputs (Opportunity Score, decision label, breakdown, reasoning). The verb 'score' and noun 'idea' are unambiguous, and the tool is distinct from siblings like 'export_idea_report' or '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?

The description implies the tool is for initial idea assessment by mentioning the opportunity score and breakdown, but it does not provide explicit guidance on when to use this tool versus its siblings (e.g., when to prefer scoring over market fit validation). No when-not-to-use or alternative recommendations 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?

Without any annotations, the description relies solely on text. It explains what the tool produces but does not disclose side effects, idempotency, or prerequisites beyond 'completed analysis.' There is no mention of whether the tool is read-only or modifies data, so behavioral transparency is moderate.

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 main action and then provides a detailed list of outputs. While the list is somewhat long, it is dense and informative, without unnecessary filler. It could be more concise by grouping outputs, but it is not excessively verbose.

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 description extensively lists outputs, which is useful, but it does not explicitly state that all prerequisite analyses must be completed, nor does it address error cases or return format. Given there is no output schema and no annotations, the description leaves some gaps about expected behavior.

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 only parameter is ideaId, and the schema description coverage is 0%. However, the description says 'on an existing idea,' which adds meaningful context that the parameter refers to an idea with completed analyses, going beyond the schema's simple type definition. This partially compensates for the lack of schema documentation.

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: 'Run the Strategic Intelligence Engine on an existing idea.' It specifies the action (run), the resource (Strategic Intelligence Engine), and enumerates specific outputs (market landscape, competitive map, white space, etc.), making it easy to distinguish from sibling tools like score_idea or generate_competitor_analysis.

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 'on an existing idea' and 'synthesizes every completed analysis' implies the tool should be used after other analyses have been run. It provides clear context for when to use it, but does not explicitly mention alternatives or when not to use it, so it falls short of a perfect score.

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 full burden. It lists outputs but does not disclose any behavioral traits such as side effects, API calls, rate limits, or whether the tool is read-only. 'AI-powered' implies some processing time, but this is insufficient for an agent to understand the tool's impact.

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 a clear list of outputs. No extraneous information. Could be more structured (e.g., separate lines for inputs/outputs) but is concise and front-loaded with the core action.

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 (AI-powered, multiple outputs), the description is too sparse. It doesn't explain what the outputs mean, any prerequisites (e.g., user authentication), or how the results are generated. Lacks output schema or example usage, making it incomplete for agent 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 0% (no parameter descriptions in schema). Description only clarifies two parameters (ideaId and idea/description), ignoring websiteUrl and targetProblem. It adds minimal meaning beyond the schema, leaving three parameters unexplained.

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 market fit validation and lists specific outputs (problem hunt, size check, competition map, etc.). It mentions two input modes (ideaId or ad-hoc description), distinguishing from siblings. However, the verb 'Run' is generic and doesn't fully differentiate from similar analysis tools.

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?

Provides guidance on input options: 'Provide either an existing ideaId or an ad-hoc idea description.' This hints at required inputs but is not explicit about minimum requirements nor when to use this tool over siblings like generate_competitor_analysis or score_idea. No when-not-to-use or alternatives mentioned.

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