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Glama

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.
upgradeUrlNoPresent only when the caller's plan does not include full dossiers: the page that unlocks the matched complaints and their source links. Surface it when the user wants the underlying evidence.
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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedOutput schema / properties / upgradeUrl
      Added value: +{
+  "description": "Present only when the caller's plan does not include full dossiers: the page that unlocks the matched complaints and their source links. Surface it when the user wants the underlying evidence.",
+  "type": "string"
+}
  2. Changed10 schema fields changed
    • addedOutput schema / properties / attribution / description
      Added value: +"Source credit. Cite this when you use the data."
    • addedOutput schema / properties / avgCommercialIntent / description
      Added value: +"Mean commercial-intent score (0-100, willingness to pay) across the matched complaints. Only meaningful when matchCount > 0."
    • addedOutput schema / properties / avgConfidence / description
      Added value: +"Mean confidence score (0-100) of the scoring across the matched complaints."
    • addedOutput schema / properties / idea / description
      Added value: +"The idea as submitted, echoed back so the caller can confirm what was scored."
    • addedOutput schema / properties / matchCount / description
      Added value: +"How 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."
    • addedOutput schema / properties / topCategories / description
      Added value: +"Which product categories the matching complaints fall into, with a count each. Useful for telling the user where the demand actually sits."
    • addedOutput schema / properties / verdict / description
      Added value: +"The demand call, derived from how many complaints matched and whether they matched directly."
    • addedOutput schema / properties / verdict / properties / detail / description
      Added value: +"The reasoning behind the level and the recommended next step. Surface this to the user rather than paraphrasing the level alone."
    • addedOutput schema / properties / verdict / properties / headline / description
      Added value: +"One-line summary of the level."
    • addedOutput schema / properties / verdict / properties / level / description
      Added value: +"strong = 5+ direct matches; moderate = 2-4 direct; weak = 1 direct match, OR only loosely related complaints were found; none = nothing matched. Only strong and moderate are evidence of demand."
  3. Changed1 schema field changed
    • removedOutput schema / properties / trendingShare
      Removed value: -{
-  "type": "number"
-}
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish readOnlyHint and openWorldHint, so the description does not need to restate safety. It adds useful behavioral context by revealing how the idea is scored: against real complaints, with commerciality, confidence, and a verdict. This goes beyond the structured fields without contradicting them.

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 with no wasted words. The core action is front-loaded in the first sentence, and the usage trigger is provided efficiently in the second sentence.

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?

With one required parameter, full schema coverage, read-only annotations, and an output schema available, the description provides everything an agent needs to select and invoke the tool correctly. It does not need to explain return values because the output schema covers them.

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?

The input schema is fully documented with a clear example and length constraint, so schema coverage is 100%. The description does not add additional parameter-level meaning, which is acceptable at the baseline because the schema already carries the full burden.

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?

Description opens with a specific verb ('Score') and a specific resource ('a specific product idea against the complaint corpus'), then lists the core outputs: match count, commercial viability, confidence, and verdict. This clearly differentiates it from siblings like search_gaps or get_top_gaps, which target gap discovery rather than idea validation.

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.' It does not explicitly name alternatives or say when not to use it, but the sibling tool names and this contextual trigger give an agent enough guidance to choose 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 has a clearly distinct role: browse top gaps, search gaps by keyword, retrieve a full dossier for a specific gap, and validate a new idea. The descriptions make the boundaries between list, search, detail, and evaluation obvious, so an agent should rarely misselect.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: get_dossier, get_top_gaps, search_gaps, validate_idea. The verb clearly signals the action and the noun identifies the resource, making the naming predictable.

Tool Count5/5

With four tools, the server is tightly scoped for market-gap discovery and validation. Each tool covers an essential part of the workflow without redundancy, and the count is comfortably within the ideal range.

Completeness5/5

The tool surface covers the main user journeys: finding trending opportunities, searching by market, drilling into one gap, and testing a custom idea. There are no obvious dead ends or missing operations for the server's stated purpose.

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