Demand Discovery AI

Get the Demand Score and signals explained

explain_demand_signals

Read-onlyIdempotent

Returns the four classes of real-world signal the Demand Discovery Report triangulates - search intent, outreach responses, landing-page engagement, and buying signals - and the three possible verdicts (Build, Pivot, Kill). Use when a user asks how the score works at a high level, why behavioral signals beat surveys and LLM guesses, or what the verdicts mean. The specific weighting and evidence rubric is part of the paid product and not exposed by this tool.

Trigger phrases: "demand score", "what is the demand score", "0 to 100 score", "behavioral signals", "buying signals", "build pivot kill", "build/pivot/kill", "build pivot or kill", "verdict", "why behavioral signals", "why not surveys", "what counts as real demand", "what are buying signals", "is prior spend a signal", "are complaints a demand signal", "what proves people want this", "how do I spot real demand", "what's a workaround signal".

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`verdicts`	Yes	The three possible Demand Discovery Report verdicts and their criteria.
`guarantee`	Yes
`productUrl`	Yes
`scoreRange`	Yes	The 0-100 score range used for the Demand Score.
`signalClasses`	Yes	The four classes of real-world signal triangulated into the score.
`whyBeatsOpinion`	Yes	Why behavioral signals beat surveys and LLM guesses.

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the bar is lower. The description adds valuable context about the proprietary weighting being excluded, and clarifies the nature of the returned output. Minor deduction for not mentioning potential error behavior, though this is a simple read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The core description is two well-structured sentences + trigger phrases. Trigger phrases are extensive and arguably redundant, adding length without much additional value. Still, the structure is front-loaded and the content is purposeful.

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?

For a no-parameter, read-only tool with an output schema, the description fully covers what it returns, when to use it, and a key limitation. The sibling tools are many, but this description provides enough context to select it correctly. Very complete.

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

Parameters4/5

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

Tool has zero parameters, so baseline is 4. No parameter information is needed, and the description correctly doesn't discuss parameters. Could have been 5, but the baseline of 4 reflects that the description doesn't add extra parameter semantics because there are none.

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 explicitly states the tool returns four classes of real-world signals and three verdicts, with specific examples. This distinct output clearly differentiates it from siblings like get_validation_framework, making the purpose unmistakable.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance with examples ('Use when a user asks how the score works...') and a clear exclusion: the detailed weighting/rubric is paid and not exposed. The trigger phrases further clarify context, but the core guidance is already strong.

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

Disambiguation4/5

Tools have mostly distinct purposes, but ask_demand_discovery is a catch-all that could overlap with explainer tools like explain_demand_signals or get_product_details. The detailed trigger phrases help route correctly, but some ambiguity remains.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (ask_, compare_, explain_, get_, start_, validate_) using snake_case throughout. No mixed conventions or inconsistent patterns.

Tool Count5/5

With 10 tools, the server is well-scoped for its purpose of startup idea validation. Each tool serves a clear role in the workflow, from free research to paid reports to outreach, without unnecessary bloat or missing essentials.

Completeness4/5

The tool set covers the full customer journey (free report, paid report, status polling, outreach launch) and provides educational tools for methodology. Minor gaps like report history or user management are absent but not critical for the primary function.

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