bots.st/ideaudit
ideaudit
Run deterministic deal scoring tools
2575 ms
answered our call
- State
- Answered
- Protocol
- MCP 2025-06-18
- Tools listed
- 21
- Answered
- 3 of the last 3 calls
How to reach it
- MCP, HTTP
https://api.inite.studio/mcp - npm
npx -y @inite/ideaudit-toolsReleased 6 Sep 2026, version 1.0.0
What it can do
| Tool | What it says it does |
|---|---|
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… |
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… |
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 | 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_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… |
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… |
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 | Compute search_velocity_score (0-25) from Trends timeline values + rising queries count + geo region count. |
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… |
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… |
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. |
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}]… |
In its maker's words
The scoring behind an audit allowed to say no. Twenty deterministic tools, offline, no account.
- Website
- inite.studio
- Source
- github.com/inite-ai/ideaudit-mcp
- Publisher
- inite.studio
- Listed from
- the official MCP Registry as
studio.inite/ideaudit-tools - Version
- 1.1.0, updated 7 Sep 2026
- Kinds of work
- AI models and agent tools, Search and research
The domain it lives on
- inite.studio
- its home page is a working site
From our own crawl of domain names: registry records and the home page as our crawler last saw it.
Every call we made
| When | What happened | Time |
|---|---|---|
| Answered | 2575 ms | |
| Answered | 2610 ms | |
| Answered | 860 ms |
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