Ideabob Validation

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

No annotations provided, so description carries full burden. It discloses that the output is JSON for PDF generation, but does not mention side effects, authentication needs, or data persistence. The description is adequate but lacks depth about potential costs or restrictions.

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 front-loads the purpose and key details (export, report, JSON for PDF). No wasted words; every part adds value.

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 has one required parameter, no output schema, and 0% schema coverage, the description covers the main purpose and output format adequately. It could mention the return structure or size limits, but for a simple export tool, it is reasonably 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 coverage is 0%, so description must compensate. It explains that the output is a complete report, indirectly clarifying that ideaId must correspond to an existing idea. However, it does not detail the ideaId format or constraints beyond the schema's uuid type. Still, for a single parameter, the description adds sufficient 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 as JSON for PDF generation, specifying included sections (scores, validations, etc.). The verb 'export' and resource 'idea report' are specific, and the context distinguishes it from siblings like generate_competitor_analysis which focuses on a subset.

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 full report is needed, but does not explicitly state when not to use it or mention alternatives. Sibling tools exist for individual analyses (competitor analysis, scoring, validation), but no guidance is given on choosing between them.

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 must carry the burden of disclosing behavioral traits. It notes that it is 'AI-powered', implying potential variability in output, but does not mention any side effects, data persistence, or performance constraints. The statement is moderately transparent but lacks details on safety (e.g., write operations, impact on data).

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 at two sentences. It front-loads the purpose and key input modes. However, the second sentence is slightly dense, listing multiple output components in a run-on manner, but still effective.

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 4 parameters, no output schema, and moderate complexity (two input modes), the description sufficiently explains the tool's purpose and output. It could improve by clarifying optionality (all params are optional when ideaId is used) and by describing the use of the description parameter. However, it provides a complete picture for selecting the tool over siblings.

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 add meaning beyond the schema. It explains that 'idea' and 'websiteUrl' form an ad-hoc pair, while 'ideaId' is an alternative. It adds context for 'description' indirectly by relating all parameters to the goal of competitor analysis. However, it does not specify which parameters are optional or provide constraints like the relationship between idea and websiteUrl.

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 'Run' and the resource 'AI-powered competitor analysis'. It specifies the two modes of input (existing ideaId or ad-hoc idea+websiteUrl) and lists the output components: competitors, strengths/weaknesses, positioning, and competitive score. This distinguishes it from siblings like score_idea or validate_market_fit which likely 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?

The description explains the two input modes (ideaId vs ad-hoc idea+websiteUrl), giving the user clear context on how to invoke the tool. However, it does not explicitly state when not to use it or contrast with alternatives like export_idea_report or score_idea, leaving some room for ambiguity.

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 description carries full burden. It discloses that scoring is across 9 dimensions, returns a composite score and label, but doesn't mention any destructive actions, rate limits, or auth needs. The behavior is described as a non-destructive evaluation, but lacks depth on processing or guarantees.

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, no filler. First sentence states action and result; second adds specific output details. 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 no output schema, the description explicitly lists return values (Opportunity Score 0-100, decision label, breakdown, reasoning), which is sufficient for an evaluation tool. However, lacks detail on the 9 dimensions or scoring criteria, but that may be acceptable given complexity.

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%, and description adds meaning beyond schema: it clarifies the purpose of concept as 'short name/title' and that additionalContext can include 'description, problem, monetization notes'. This supplements schema descriptions which exist but are brief.

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 evaluates startup/product ideas across 9 weighted dimensions, producing an Opportunity Score, decision label, breakdown, and reasoning. It specifies verb 'Score', resource 'startup or product idea', and output format, distinguishing it from sibling tools 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 when to use (to score an idea) but provides no guidance on when not to use or alternatives. Without siblings details, it's not clear when to prefer this over validate_market_fit or generate_competitor_analysis, but the unique scoring output suggests distinct use case.

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 specific outputs, but does not disclose any side effects, cost implications, or whether it modifies data. It does not contradict annotations since annotations are absent, but it also provides no information about idempotency, failure modes, or rate limits.

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 concise: two sentences that front-load the purpose and then list inputs and outputs. No extraneous information. However, it could be slightly more structured (e.g., breaking inputs and outputs).

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 (5 parameters, no output schema, no annotations), the description partially fills gaps by listing return types and alternative input modes. But it lacks parameter details for websiteUrl, description, and targetProblem, and assumes knowledge of what 'market fit validation' entails. It is adequate but not thorough.

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 compensate for the 5 parameters. The description mentions idea and ideaId as alternative inputs, but does not explain the other three parameters (websiteUrl, description, targetProblem) or their relationships. It adds some meaning beyond the schema by grouping ideaId and idea, but leaves gaps.

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: 'Run AI-powered market fit validation.' It lists the return outputs (problem hunt, size check, etc.) and mentions two alternative inputs (ideaId or ad-hoc idea description), which helps distinguish its functionality. However, it does not differentiate from sibling tools like generate_competitor_analysis or score_idea, which might overlap.

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 tells when to use it (when market fit validation is needed) and notes that you can provide either ideaId or idea description. However, it does not specify when NOT to use it or how it differs from siblings like score_idea or generate_competitor_analysis. No explicit alternatives are mentioned.

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