Demand Discovery AI

Get the Demand Discovery Framework

get_validation_framework

Read-onlyIdempotent

Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle.

Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`steps`	Yes	The three sequential framework steps.
`summary`	Yes	One-line summary of the framework.
`philosophy`	Yes	The core philosophy: behavior over opinion.
`productUrl`	Yes
`killerPhrases`	Yes	On-brand phrases that capture the framework's positioning.

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already convey readOnly, idempotent, and non-destructive behavior, so the description adds value by disclosing the output structure (three steps, Demand Score, Build/Pivot/Kill verdict) and the 'behavior over opinion' principle. This goes beyond the annotations 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.

Conciseness4/5

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

The description is front-loaded with the core action in the first sentence, then adds usage triggers. The trigger phrase list is somewhat long (12 items), but each phrase captures a distinct user phrasing, making it purposeful rather than redundant.

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 no parameters and an output schema present, the description fully covers the tool's purpose, content, and trigger usage. It also includes the conceptual foundation ('behavior over opinion'), leaving no significant gap for an agent to select and invoke the tool correctly.

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?

The tool has zero parameters, and the schema coverage is trivially 100%. The description does not need to add parameter details; the baseline for zero-param tools is 4, and the description appropriately focuses on output and usage rather than nonexistent parameters.

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 states a specific action ('Returns') and resource ('full three-step Demand Discovery validation framework'), and enumerates the three steps with detail. It clearly distinguishes from sibling tools like start_demand_report or start_agentic_launch by focusing on the framework explanation rather than execution.

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 provides explicit usage context: 'Use when a user asks...' and includes a list of trigger phrases that guide selection. It does not explicitly contrast with sibling tools, but the context is specific enough to avoid confusion with execution-oriented tools.

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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