Idea validation MCP

Kill bad ideas
before you
build them.

Five gates. One verdict. No cheerleading.

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Mechanism

How it works.

  1. G1 Direct Competitor Scan

    Who is the closest existing thing, what have they shipped, where are they weak?

  2. G2 Market Structure

    Is the market shaped so this idea can win meaningful share?

  3. G3 Platform Risk

    Will a platform shift or hyperscaler ship this in 24 months?

  4. G4 Willingness to Pay

    Will the target customer actually pay enough to make this a business?

  5. G5 Why Now

    What changed in the last 24 months that makes this possible now?

Evidence

Real output.

A real validation run on a B2C focus app. Five gates fired; one passed, three failed, one came back inconclusive. Click any gate to expand its detailed evidence (sources, tiers, DOK layers, verdict). Gate 4, the killshot, is open by default.

G1 Direct Competitor Scan FAIL Confidence Low ⚠ Model judgmentAt least 3 directly-overlapping AI-screen-monitoring products are shipping today. The founder doesn't enter as a first-mover. The "I have the problem too" angle doesn't produce structural differentiation.

DOK 1 · Facts (sourced)

A find_closest_competitor returned a r/ADHD_Programmers thread titled "AI tool that keeps you on track by literally watching your screen" describing a shipping product that watches the screen and uses AI to detect off-task behavior. Exact functional convergence with the proposed idea. Source: reddit.com/r/ADHD_Programmers | Bias: independent
A The Hustle headline "Easily distracted? This AI focus tool will scold you into staying on task" describes a shipping product that "monitors and analyzes your screen to determine whether what you are doing matches your task". Additional direct competitor. Source: thehustle.co | Bias: independent
A IndieHackers post "Every focus app I tried on Mac failed me · here's what finally worked" documents Cold Turkey "too rigid" and Focus (Mac app) "too easy to bypass". Category-wide structural failure pattern across multiple incumbents. Source: indiehackers.com | Bias: independent
A map_competitive_weaknesses fired a Serper query for Fomi negative-sentiment terms ("not worth", "cancelled", "switched from", "disappointed", "terrible") and surfaced churn-out / dissatisfaction language in the category. Source: google.serper.dev | Bias: independent

DOK 2 · Summary (no interpretation)

Multiple direct or adjacent AI-screen-monitoring focus apps already ship today (the r/ADHD_Programmers tool, The Hustle-covered AI focus tool, Focusmo, plus the wider Fomi / Opal / Cold Turkey cohort). Public discussion shows category-wide churn ("Every focus app I tried on Mac failed me"). The targeted Fomi changelog probe failed via URL-guessing fallback; Fomi-specific changelog signal is absent.

DOK 3 · Insight (model judgment)

⚠ Model judgmentAt least 3 directly-overlapping AI-screen-monitoring products are shipping today. The founder doesn't enter as a first-mover. The "I have the problem too" angle doesn't produce structural differentiation. The IndieHackers "every focus app failed me" pattern is best read as a category symptom (users churn through tools) rather than a winnable wedge.

Contradicting evidence (searched) A counter-angled Serper search for "profitable indie AI focus app success story 2025" returned only listicles and adjacent products. No profitable indie AI-screen-monitoring focus app surfaced as a contrarian success.

Gate 1 verdict

FAIL 7 sources · S:1 A:5 B:1 · indep:5 conflicted:2 · Confidence Low
G2 Market Demand PASS Confidence Medium ⚠ Model judgmentDemand for "focus" tools is real and large, but the only profitable comparable surfaced (Rize, $10k+ MRR) is a time-tracker, not an AI-screen-monitoring app. The demand may be for a different shape of product.

DOK 1 · Facts (sourced)

A r/productivity has 4,195,056 subscribers. Very large active community in the productivity space (estimate_demand_signals via Reddit /about.json live fetch). Source: reddit.com/r/productivity | Bias: independent
A r/ADHD has 2,232,448 subscribers. Primary target demographic for focus apps is large and active. Source: reddit.com/r/ADHD | Bias: independent
A Rize, an indie time-tracking SaaS, publicly reports >$10k MRR on IndieHackers. At least one indie comparable in the adjacent (time-tracking) mechanic clears the B2C-solo lifestyle threshold. Source: indiehackers.com | Bias: independent
A TechCrunch 2025-12-25 roundup of distraction blockers (Opal, Freedom, etc.) confirms continued press attention and launch cadence in the focus-app category. Source: techcrunch.com | Bias: independent

DOK 2 · Summary (no interpretation)

Demand-side signal is strong: very large communities (~4.2M r/productivity, ~2.2M r/ADHD, ~2.2M r/getdisciplined), an IndieHackers comparable (Rize) above $10k MRR in adjacent time-tracking, and continued press coverage of new entrants. Niche reachability for B2C-solo framing is clearly met.

DOK 3 · Insight (model judgment)

⚠ Model judgmentDemand for "focus" tools is real and large, but the only profitable comparable surfaced (Rize, $10k+ MRR) is a time-tracker, not an AI-screen-monitoring app. The demand may be for a different shape of product. For B2C-solo framing the niche-reachability bar is met, so Gate 2 itself does not kill the idea, but the upside is capped by Gate 4 dynamics.

Contradicting evidence (searched) No contradicting evidence surfaced. Treat as a gap, not confirmation. The framework searched; the absence is logged honestly per spec §11 anti-pattern 2.

Gate 2 verdict

PASS 5 sources · A:5 · indep:5 · Confidence Medium
G3 Platform & Big-Tech Risk FAIL Confidence Low ⚠ Model judgmentApple owns the Screen Time / Managed Settings API the product must call, and the same App Review Guidelines surface that killed Spotlight-style and Notes-style third-party apps gates entry. Android Digital Wellbeing applies the same vector on the Android side.

DOK 1 · Facts (sourced)

A assess_platform_dependency scored Gate 3 platform-dependency risk at 3/5 ("Material platform dependency with mixed enforcement history") with 4 detected restrictive platforms (Apple Screen Time API, Android Digital Wellbeing, iOS/Apple platform APIs, macOS). The tool's verdict explicitly invokes the Apple "system feature" pattern that "has killed prior categories (Spotlight → launchers, Notes → note apps)". Source: google.serper.dev · Apple Screen Time API ToS / policy churn | Bias: independent
S Apple App Review Guidelines (developer.apple.com/app-store/review/guidelines/). The first-party rulebook governing Screen Time / focus apps. Any iOS focus app is structurally subject to this policy surface. Source: developer.apple.com | Bias: conflicted
S Apple Developer Forums "Managed Settings" tag. The Screen Time API surface every iOS focus app must build on, with active third-party developer issues reported. Source: developer.apple.com/forums | Bias: conflicted
S Microsoft Learn Q&A thread documents Microsoft Viva Insights Focus Plan failing to schedule focus time. Microsoft already ships focus-management as part of Microsoft 365, confirming hyperscaler presence in the category. Source: learn.microsoft.com | Bias: conflicted
A assess_platform_dependency's deplatforming-retro fan-out (Serper query for Apple Screen Time API "lost access" / "deplatformed" / "rejected" founder retros on Medium/HN/IndieHackers) returned no usable hits. Logged honestly as empty per spec §11 anti-pattern 2. Does NOT mean the platform is safe. Source: google.serper.dev | Bias: conflicted

DOK 2 · Summary (no interpretation)

The product is structurally dependent on Apple's Screen Time / Managed Settings API (or equivalent Android Digital Wellbeing). Both restrictive platforms governed by App Review Guidelines policy churn. assess_platform_dependency scored Gate 3 platform-risk at 3/5, explicitly invoking the Apple "system feature" pattern that killed Spotlight-style launchers and Notes-style note apps. […] Microsoft already ships Viva Insights Focus Plan.

DOK 3 · Insight (model judgment)

⚠ Model judgmentThis is the killshot gate. Apple owns the Screen Time / Managed Settings API the product must call, and the same App Review Guidelines surface that killed Spotlight-style and Notes-style third-party apps gates entry. A cloud-screenshot-based third-party app is structurally exposed to both API revocation and competitive encroachment. Android Digital Wellbeing applies the same vector on the Android side. […]

Contradicting evidence (searched) No contradicting evidence surfaced. Treat as a gap, not confirmation. The framework searched; the absence is logged honestly per spec §11 anti-pattern 2.

Gate 3 verdict

FAIL 7 sources · S:5 A:2 · indep:1 conflicted:6 · Confidence Low
G4 Willingness to Pay FAIL Confidence Low ⚠ Model judgmentNew entrants face a ~$10/mo category ceiling combined with the documented "ADHD-tax" churn pattern. Cloud-screenshot processing adds inference cost on top of an already-thin margin profile.

DOK 1 · Facts (sourced)

S RescueTime live pricing lists $7 / $9 / $12 per month. The category ceiling for an established B2C productivity SaaS sits in the single-digit to low-double-digit dollar range. Source: rescuetime.com/pricing | Bias: conflicted
S Freedom live pricing lists $3.33 / $8.99 per month freemium tiers. Additional confirmation of the sub-$10/mo B2C ceiling. Source: freedom.to/pricing | Bias: conflicted
S Opal lists $8.29 and $19.99 per month. Direct competitor in the same B2C focus-app mechanic. Source: opal.so/pricing | Bias: conflicted
S Wayback Machine snapshots from March + May 2026 confirm Opal and Focus Bear pricing has been stable at the sub-$10/mo ceiling for at least 2 months. Historical anchor, not an artifact of a single fetch. Source: web.archive.org | Bias: independent
A IndieHackers: Session, an App Store focus app, reports $2,000 MRR with ~750 installs/month. Illustrative B2C-solo ceiling for focus apps that aren't one of the top 1–2 winners. Source: indiehackers.com/product/session-2 | Bias: independent
B find_pricing_anchors auto-flagged 5 G2 / Capterra churn signals including "refund the overpayment" and "cancelled they didn't even bother to respond". […] Source: g2.com | Bias: independent

DOK 2 · Summary (no interpretation)

The focus-app category prices in the $3–$30/mo range with most consumer tiers between $3–$12/mo (RescueTime, Freedom, Opal, Focus Bear). Two Wayback snapshots confirm pricing stability. find_pricing_anchors auto-flagged 5 churn / refund signals. IndieHackers comparables (Session $2k MRR, Rize $10k+ MRR) show the ceiling. […]

DOK 3 · Insight (model judgment)

⚠ Model judgmentThe B2C-solo target ($5k–$50k MRR) is achievable only at the top of this category. New entrants face a ~$10/mo ceiling combined with the documented "ADHD-tax" churn pattern. A classic labor-of-love combination. Cloud-screenshot processing adds inference cost on top of an already-thin margin profile. […]

Contradicting evidence (searched) Rize publicly reports >$10k MRR on IndieHackers with a top tier at $29.99/mo, demonstrating that pricing above the $10/mo ceiling is achievable for adjacent time-tracking. Neither Rize nor Cold Turkey uses cloud-screenshot AI monitoring, and both have multi-year market presence.

Gate 4 verdict

FAIL 10 sources · S:7 A:2 B:1 · indep:5 conflicted:5 · Confidence Low
G5 Why Now INCONCLUSIVE Confidence Low ⚠ Model judgment"AI got better" is the spec's canonical anti-pattern for Why Now. The evidence doesn't surface a sharper thesis (specific API opening, regulatory change, remote-work behavior shift). The absence of evidence is itself the signal.

DOK 1 · Facts (sourced)

S find_why_now_signals surfaced 4 "recent enablers" from hyperscaler dev-doc fan-out. All are general policy / forum surfaces (Apple App Review Guidelines, Apple Developer Forums Managed Settings, Microsoft Visual Studio 2026 release notes, Google ML guides). None describes a discrete enabler that unlocks AI screen-monitoring specifically. Source: developer.apple.com | Bias: conflicted
A find_yc_rfs_alignment returned 0 keyword matches against YC S26 RFS for focus / screen-time / on-task. YC is not currently pulling for this category. Source: ycombinator.com/rfs | Bias: conflicted
A EU regulatory query via Serper (site:ec.europa.eu) surfaced no focus-app-specific tailwind. The broader EU AI Act regulatory framework is a headwind for cloud-screenshot processing of user activity, not an unlock. Source: ec.europa.eu | Bias: independent
A TechCrunch 2025-12-25 "best distraction blockers" rounds up Opal / Freedom. Continued category cadence is driven by editorial calendar (New Year resolutions) and personality-led launches, not a specific 2025–2026 enabler unlock. Source: techcrunch.com | Bias: independent

DOK 2 · Summary (no interpretation)

No specific articulable why-now thesis surfaced for AI-screen-monitoring focus apps. Hyperscaler dev-doc fan-out returned only general policy / forum surfaces. YC RFS shows zero category pull. EU regulatory environment is a headwind, not a tailwind. Press cadence is driven by editorial calendar, not an enabler unlock.

DOK 3 · Insight (model judgment)

⚠ Model judgment"AI got better" is the spec's canonical anti-pattern for Why Now (§9). The available evidence does not surface a sharper thesis (a specific platform API opening, regulatory change forcing screen monitoring, or behavior shift tied to remote work). The absence of evidence is itself the signal here.

Contradicting evidence (searched) No contradicting evidence surfaced. Treat as a gap, not confirmation. The framework searched; the absence is logged honestly per spec §11 anti-pattern 2.

Gate 5 verdict

INCONCLUSIVE 4 sources · S:1 A:3 · indep:2 conflicted:2 · Confidence Low

Verdict math

The fail-2 rule.

Five gate verdicts collapse to one overall verdict by a fixed table. No model discretion at the rollup.

Gate verdicts Overall
5 PASS GO
4 PASS + 1 INCONCLUSIVE CONDITIONAL GO
4 PASS + 1 FAIL CONDITIONAL GO
3 PASS + 2 (FAIL or INCONCLUSIVE) CONDITIONAL GO
2+ FAIL NO-GO

Anti-bias by construction

Four structural defenses.

Confirmation bias is the failure mode of every idea-validation tool. Veto answers it at four points in the pipeline.

  1. Every fact carries a quality tier (S/A/B/C/D) and a bias flag. No fact ships without provenance.
  2. Contradicting-evidence search is mandatory. Every gate fans out a counter-search before the verdict resolves.
  3. Three Validation Checks audit verdicts before they render. Internal consistency, source quality, anti-confirmation.
  4. The Spiky POV section is blank. The framework never decides for the user. The reader writes the take.

Install

Add the connector.

Veto speaks MCP over Streamable HTTP. Add it as a custom connector in Claude or ChatGPT using one URL, sign in with email, and you're connected. Nothing to paste, no config file.

Recommended

claude.ai (web and desktop) custom connector

No config file, no token to copy. Sign in with email and you're connected.

  1. In claude.ai, open Settings → Connectors → Add custom connector.
  2. Paste the connector URL below, click Add, then Connect.
  3. Enter your email when Veto asks, then click the one-time sign-in link we send. You land back in claude.ai, connected.
https://getvetoed.com/mcp

Veto signs you in over OAuth. There's no token to paste; authorization happens in your browser. Same 400 tool-calls/day free tier.

Also supported

ChatGPT custom connector

Added via Developer mode using the same URL. No token to paste.

  1. In ChatGPT on the web, open Settings → Connectors → Advanced settings and turn on Developer mode.
  2. Back in Connectors, click Create. Name it Veto, paste the URL below as the MCP Server URL, and set authentication to OAuth.
  3. Click Create, sign in with your email when prompted, and click Allow. To use it, open a chat and pick Veto from + → More → Developer mode.
https://getvetoed.com/mcp

Requires ChatGPT Plus, Pro, Business, Enterprise, or Edu on the web. Developer mode is in beta and not available on Free; on Business and Enterprise a workspace admin must enable it first. Same OAuth email sign-in, same 400 tool-calls/day free tier.

Veto runs on a shared pool of live web-search credits. If they run out, add your own key on your account page to keep getting live results. A free key is available at serper.dev.

5 prompts · 14 tools

Inside the framework.

Prompts are what you call from Claude or ChatGPT. Tools are what runs underneath. The LLM picks them as it works through each gate.

What you call

  • validate_idea Runs the full 5-gate validation on a product idea. Calls every tool the gates need; produces a NO-GO / GO / CONDITIONAL GO verdict with sourced evidence.
  • generate_test_cards Input your business idea. Get 3-7 hypotheses that must be true for the business to work, each with a cheap test design, an exact metric, and a pass/fail threshold. Strategyzer Test Card format. Pair with validate_idea to focus tests on the weakest gates.
  • run_single_gate Runs a single gate in isolation. Useful when you want to stress-test just the platform-risk dimension, or just the pricing, without the full report.
  • steelman_against Argues against your own idea. Forces the strongest possible case that this product should not exist.
  • quick_kill_check Five-minute disqualification scan. Cheap, surface-level. Catches the obvious kills before you commit to a full validation.

What runs underneath

Gate 1 · Direct Competitor Scan

  • find_closest_competitor Surfaces the closest existing thing in your category, across Web, Product Hunt, and Hacker News.
  • map_competitive_weaknesses Mines Reddit, HN, and the wider web for user complaints. Separates structural weaknesses from surface gripes.
  • read_competitor_changelog Reads a competitor's changelog for failure signals: setup creep, churn language, platform gaps.

Gate 2 · Market Structure

  • estimate_demand_signals Composes GitHub activity, subreddit subscribers, and search-cluster mentions into one demand signal: strong / moderate / weak / none.
  • find_public_revenue_signals Surfaces public revenue evidence (IndieHackers entries, MRR tweets, SEC filings) for your category and its competitors.

Gate 3 · Platform Risk

  • assess_platform_dependency Detects platforms your product sits on top of (App Store, OpenAI, Shopify, etc.) and scores deplatforming risk.
  • check_big_tech_encroachment Scans Apple WWDC, Google I/O, MS Build, Meta Connect, AWS re:Invent for category overlap. The 'will Apple ship this' check.

Gate 4 · Willingness to Pay

  • find_pricing_anchors Reads competitor pricing pages (live + Wayback) and scans G2/Capterra. Auto-flags categories where pricing dropped 25%+ in 24 months.

Gate 5 · Why Now

  • find_why_now_signals Surfaces recent enablers (new APIs, platform shifts, YC RFS touchpoints) that make this idea possible now, not 2 years ago.
  • find_yc_rfs_alignment Scores your idea against YC's current Request for Startups. A signal that smart investors think the category matters.

Cross-gate (synthesis)

  • check_what_not_to_build Check a product idea against Garaža's curated 'what not to build' tarpit-idea list (garaza.org). Flags whether the idea resembles a known tarpit category (social/event platforms, urban-efficiency apps, HR platforms, tourist platforms, di…
  • finalize_validation_report The rollup. Takes the per-gate evidence and produces the final markdown artifact with verdict, killshots, and blank Spiky POV.
  • get_category_failure_modes Searches post-mortems and shutdown stories. Returns the structural failure patterns specific to your category.
  • scan_producthunt_launches Pulls Product Hunt launches in your category. Weighs engagement (comments / votes) to spot high-conviction vs. viral-but-shallow.