# ideaudit

- Registry name: `studio.inite/ideaudit-tools` · version 1.1.0
- Categories (automatic, keyword rules): Crypto & blockchain, Finance & markets, Build & deploy, Developer tools, Web search, Search & data
- Authentication at last check: No auth
- Declared risk: Not declared (tools carry no read/destructive hints)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=studio.inite%2Fideaudit-tools
- Server page: https://mcpstation.net/s/studio.inite--ideaudit-tools.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://api.inite.studio/mcp`

## Tools (as reported by the server, not verified)

- `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

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html