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DDMarketer

by io.github.CodePhantom-1

Validate an idea against real demand

validate_idea
Read-only

Score a specific product idea against the complaint corpus: how many real complaints match it, how commercial they are, how confident the scoring is, and a verdict. Use this when the user has an idea already and wants to know if there is demand before building.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ideaYesThe idea in plain words, e.g. 'a tool to reconcile Shopify payouts with my accounting' (3-300 chars).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
ideaYesThe idea as submitted, echoed back so the caller can confirm what was scored.
verdictYesThe demand call, derived from how many complaints matched and whether they matched directly.
matchCountYesHow many complaints matched. IMPORTANT: when verdict.level is 'weak' because only loosely related complaints were found, this counts those loose matches, which do NOT validate the idea. Never report matchCount as direct evidence without checking verdict.level.
attributionYesSource credit. Cite this when you use the data.
avgConfidenceNoMean confidence score (0-100) of the scoring across the matched complaints.
topCategoriesNoWhich product categories the matching complaints fall into, with a count each. Useful for telling the user where the demand actually sits.
avgCommercialIntentNoMean commercial-intent score (0-100, willingness to pay) across the matched complaints. Only meaningful when matchCount > 0.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the baseline is lower, but the description adds useful behavioral detail about what the scoring returns: complaint matches, commercial value, confidence, and a verdict. It accurately reflects a read-only analysis operation and does not contradict 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loads the core action and expected outputs, and the usage guidance is appended without redundancy. Every sentence contributes meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a single parameter, an output schema, and protective annotations. The description covers what the tool does, what it returns, and when to use it, which is complete for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema fully documents the 'idea' parameter with a plain-words example and character limits. The tool description adds little beyond restating 'specific product idea,' so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Score') and clearly identifies the resource ('a specific product idea against the complaint corpus') plus the key outputs: match count, commercial viability, confidence, and verdict. It is clearly distinct from the sibling tools by focusing on validating an existing idea rather than exploring or searching gaps.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'when the user has an idea already and wants to know if there is demand before building.' This is strong usage guidance, though it does not explicitly name sibling alternatives or state when not to use them. The context is clear enough for an agent to route correctly.

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

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool serves a distinct stage in the workflow: surfacing top opportunities, searching by keyword, and validating a specific idea. There is no meaningful overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a clear verb_noun pattern with lowercase snake_case: get_top_gaps, search_gaps, validate_idea. The naming is predictable and immediately communicates each tool's action.

Tool Count5/5

Three tools is well-scoped for a specialized market-research server. Each tool earns its place and together they cover the core workflow without unnecessary bulk.

Completeness5/5

The toolset covers the full intended workflow: open-ended discovery, market-specific search, and validation of a concrete idea. There are no obvious missing operations for this narrow domain.

Resources