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ideaudit

studio.inite/ideaudit-tools · v1.1.0

Measured by MCP Station

Description written by the publisher, not verified

The scoring behind an audit allowed to say no. Twenty deterministic tools, offline, no account.

Connect

claude mcp add --transport http ideaudit-tools https://api.inite.studio/mcp

{
  "mcpServers": {
    "ideaudit-tools": {
      "url": "https://api.inite.studio/mcp"
    }
  }
}

Tools as reported by the server

• get_started
  What this server is, what it will do for you right now without an account, and what an account adds. Call this first if you have no API key — it answers in one round trip instead of sending you to a website.
• compute_barrier
  Compute barrier_score (0-24) + label (PRISTINE/OPEN/COMPETITIVE/CROWDED) from competitor counts + SERP noise fraction.
• compute_budget_proof
  Compute budget_proof_score (0-10) + label (STRONG/CONFIRMED/WEAK/ABSENT) + purchase_intent_pct from pricing hits + review-site hits + intent mentions.
• compute_build_complexity
  Compute build_complexity_penalty (0-10, higher = worse) + per-factor breakdown. Hard tags: ml/realtime/blockchain/hardware/compliance/custom-ai/regulated/on-device-ai/iot.
• compute_collection_scores
  Compute 12 deterministic collection scores (0-100) + badges + death reason for an enriched idea. Pure math. No external calls.
• compute_crossed_matrix
  Crossed-product audit explorer. Same input as compute_dealbreakers_v2 — returns substrate verdict (no-observer baseline) + crossed verdict (when observer supplied) + a 5-row matrix of {solo, cofounded_technical, cofounded_business, domain_expert, serial} archetype verdicts. Never persists; meant for
• compute_dealbreakers_v2
  Methodology v2 dealbreakers — stage-aware weights + confidence-weighted lens scoring + risk-asymmetric verdict (GO requires score≥80 AND zero red flags AND avg confidence≥0.6). Optional `observer` triggers the crossed-product pipeline: substrate verdict (no-observer baseline) PLUS crossed verdict (o
• compute_funding_momentum
  Compute funding_momentum_score (0-10) + badge (HOT/WARM/COOL/COLD) from tier-weighted funding-article hit counts.
• compute_hiring_demand
  Compute hiring_demand_score (0-10) from priority-weighted ATS site hit counts (use registries/hiring-sources for priorities).
• compute_lrs_composite_v2
  LRS composite v2 — 6 components (SV, Pain, Barrier, Monet, X-Signal, Budget-Proof). Default Python weights 0.18/0.22/0.18/0.14/0.18/0.10 sum=1.0. Returns BOTH weighted score and equal-weight baseline (per OECD Handbook + Greco 2018 — equal-weight is defensible default when no outcome calibration exi
• compute_lrs_composite
  Compose lrs_final_100 (0-100) + label (WEAK/EMERGING/GOOD/STRONG/ELITE) + leaderboard_eligible flag + sub-percent breakdown. Weights: sv 0.25, sp 0.30, barrier 0.25, monetization 0.20.
• compute_monetization
  Compute monetization_score (0-21) + label + has_pricing_anchors from pricing anchors + model tags + deal cycle hint.
• compute_multi_source_tam
  Multi-source TAM consensus. Pass 2-3 sources of market-size text. Optional `estimateYear` per source — when supplied, the result includes yearRange and a hasStaleData flag (true if the span exceeds 5 years). Outliers are dropped by modified Z-score over the median absolute deviation when n≥4. Return
• compute_ppc_spend_signal
  Wave 5 N.4 — compute ppc_spend_score (0-10) + label (STRONG/CONFIRMED/WEAK/ABSENT) + market_saturation from PPC traffic projection (avgCpcUsd, totalMonthlySpendUsd, optional competitorBidders + competition). Feed numbers from dataforseo_ad_traffic.
• compute_search_velocity_v2
  Search velocity (0-25) v2 — canonical 0.40*volume + 0.30*trend + 0.20*intent + 0.10*geo. CRITICAL: externalVolumeNorm MUST come from external sources (Amazon BSR / app store installs / job-board postings) — NOT the Trends timeline (would double-count, since Trends is itself normalized 0-100 within w
• compute_search_velocity
  Compute search_velocity_score (0-25) from Trends timeline values + rising queries count + geo region count.
• compute_social_pain
  Compute social_pain_score (0-30) + total mentions + dominant perspective (business/consumer/trend/mixed).
• compute_urgency_composite
  Compose composite_urgency_score (0-10) + badge (LOW/MEDIUM/HIGH/VERY_HIGH/EXTREME) from 3 sub-scores: news, pain, hiring.
• compute_x_signal
  Compute x_signal_score (0-20) + recency share + positivity rate from X/Twitter mention counts.
• derive_kill_criteria
  Derive a falsifiable, data-driven list of kill criteria from upstream signals — the outputs of validate_unit_economics and compute_dealbreakers_v2, plus an ICP drift count. Returns one row per rule with {rule, threshold, status, evidence?}, where status is tripped_now / monitor / cleared. Replaces p
• validate_unit_economics
  Sanity-check a unit-economics row before publishing it in a business-model slide. Catches the math-drift class of failures (customers × ARPU ≠ revenue), enforces the LTV/CAC ≥ 1.5 floor, the cohort-positivity check, and CAC payback bounds. Returns {ok, errors[{rule, severity, detail}], derived{ratio

Repository · Website · JSON · Text for agents · Registry updated 2026-09-07