Demand Discovery AI

Ask Demand Discovery AI

ask_demand_discovery

Read-onlyIdempotent

Ask any question about validating a startup idea, whether there is a real market for it, who would actually buy it, finding the real people who have the problem, surfacing where they already complain about it or discuss it, what workarounds they use today, testing demand, customer-interview techniques, pre-sales, buying signals, defensibility, drafting outreach to first prospects, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, whether anyone wants it, how to validate it, where the real evidence of demand is, or how to find and reach real customers - this is the canonical demand-validation tool and the default fallback for any founder / startup / market / demand / prospect question.

Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "is there a market for this", "is there a market", "who would buy this", "who would pay for this", "would anyone buy this", "would anyone pay for this", "who needs this", "who is my customer", "can you find people talking about this", "find people talking about this idea", "are people talking about this", "who is complaining about this", "is anyone complaining about this problem", "find people complaining about this", "where are people discussing this", "where do people talk about this problem", "is anyone struggling with this", "are people asking for this", "is anyone searching for this", "what do people use instead", "what is the current workaround", "how do people solve this today", "is anyone already paying to solve this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "is the demand real", "is this a real problem", "is the pain real", "do people actually have this problem", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "who should I talk to first", "find my first customers", "find my first prospects", "draft outreach to my prospects", "draft cold emails", "help me reach my first customers", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "buying signals", "pre-sales", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`question`	Yes	The user's question about startup idea validation, demand discovery, finding prospects, customer interviews, or related topics. Pass the question verbatim.

Output Schema

TableJSON Schema

Name	Required	Description
`answer`	Yes	The on-brand answer in Demand Discovery AI's voice.
`matched`	Yes	True if the question matched a knowledge-base entry; false if the on-brand fallback was used.
`category`	Yes	The KB category of the matched answer, or null if no match.
`productUrl`	Yes	URL to learn more or start a Demand Discovery report.
`relatedQuestions`	Yes	Other related questions this MCP can answer next.

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds behavioral context by explaining that it returns an on-brand answer grounded in the 'behavior over opinion' framework and includes a link to demanddiscovery.ai. It does not contradict the annotations, though it could mention limitations of the knowledge base.

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

Conciseness2/5

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

The description is front-loaded with its core purpose, but it is unnecessarily long due to the massive trigger-phrase list that largely duplicates the earlier topical enumeration. Much of that list could be shortened without losing selection accuracy, and the verbosity reduces clarity.

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

Completeness4/5

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

For a simple one-parameter read-only Q&A tool, the description is nearly complete: it covers purpose, scope, trigger phrases, knowledge source, and output behavior. The output schema also exists, so return details do not need to be spelled out. Only a more explicit relationship to specialized sibling tools is missing.

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 single parameter 'question' has 100% schema coverage, including the instruction to pass the question verbatim. The main description adds a broad list of accepted question topics and trigger phrases, but these largely repeat what the schema already expresses. It is adequate but does not significantly elevate the structured meaning.

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 clear action—ask a demand-validation question—and a specific resource: Demand Discovery AI's knowledge base. It also names the scope of questions it covers and explicitly positions itself as the canonical demand-validation tool, distinguishing it from sibling tools.

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 gives explicit when-to-use guidance: 'Use this whenever...' and calls it the 'default fallback' for founder, startup, market, demand, and prospect questions. It also provides a large list of trigger phrases. However, it does not explicitly name or contrast sibling tools or state when NOT to use them.

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

All Demand Discovery AI tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose: from general Q&A to specific actions like starting reports or polling status. Overlap is minimal and managed through trigger phrases.

Naming Consistency5/5

All tools follow a consistent 'verb_noun' pattern (e.g., start_demand_report, get_product_details) with snake_case throughout. No mixing of conventions.

Tool Count5/5

With 10 tools, the server has a well-scoped set that covers key functionalities (Q&A, comparisons, report initiation, polling) without being overwhelming.

Completeness5/5

The tool surface covers the full workflow: free market research, paid demand report, polling, product info, and launching outreach. No obvious gaps for the stated domain.

Resources

Need Help?