ideaudit MCP Server

by inite Your server? Claim it

answering

ideaudit is answering right now. Last checked 2 min ago. 57 installs a week from npm. It exposes 21 tools. Last commit 7 Sep 2026.

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

Installs per day peak 247 · avg 10 · -73% w/w

a month agotoday

Uptime history 12 days of history · worst day 98%

12 days agonow

100.0%

Uptime 24h

92 of 92 checks

Tools

read from the server

678 ms

Response time

average over 24h

Installs / week

npm and PyPI

ideaudit missed 2 checks this week

Everything else answered, so this is steady rather than shaky. We check every 15 minutes, which is how a one-off gets told apart from the start of a pattern, and how you hear about the next one within the hour instead of from your users.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 2 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

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

~/Library/Application Support/Claude/claude_desktop_config.json

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

~/.codex/config.toml

[mcp_servers.ideaudit-tools]
url = "https://api.inite.studio/mcp"

.cursor/mcp.json

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

.vscode/mcp.json

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

Available tools 21

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

compute

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 the dashboard "view as [archetype]" dropdown and for previewing a verdict before committing to it.

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 (observer-perturbed weights, risk-tolerance shifted thresholds) PLUS 5-row archetype matrix. The KILL gate (≥2 blockers / score<50) is observer-invariant — fatal stays fatal.

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 exists). buildComplexityPenalty 0-10 subtracted from score. sectorProfile (ai_native/creator/crypto) opt-in reshuffles SV→0.16, X→0.20. Labels: THE_ROAR (≥80) / PROMISING (≥60) / EXPERIMENTAL (≥40) / WEAK_SIGNAL (<40).

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. Returns the extracted dollar amounts + consensus median + an agreement score 0..1, where 1 means every source lands within 20% of the median.

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 window). trendNorm is derived internally from trendsTimelineValues. Trends peak<50 zeroes the trend component (Yotpo SEO floor). Optional daysSinceLastSignal applies exponential freshness decay (search half-life 90d).

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

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 prose kill criteria, which are tautologies that can never fire.

started

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

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{ratios}}. Skills MUST regenerate the row when ok=false (block-severity errors); warn-severity errors should be surfaced in the final report but do not gate publication. No LLM calls.

Endpoints

URL	Transport	State	Latency	Checked
https://api.inite.studio/mcp	streamable-http	answering	616 ms	2 min ago

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ideaudit — questions

Answers built from our own checks of this server.

What can ideaudit do?

It exposes 21 tools, read directly from the server on our last check. Among them: compute_barrier, compute_budget_proof, compute_build_complexity, compute_collection_scores, compute_crossed_matrix, compute_dealbreakers_v2 and 15 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

Is ideaudit working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 678 ms. The bar chart above shows every period we have measured.

How do I connect ideaudit?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does ideaudit need an API key?

No. ideaudit completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 21 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is ideaudit?

It answers our handshake in 678 ms on average, which is faster than 18% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use ideaudit?

The npm package @inite/ideaudit-tools was installed 57 times in the last week. Week over week that is -73%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is ideaudit open source?

We cannot say either way: written in TypeScript and 0 stars on GitHub, but we could not determine the licence, and without one the code is not open source by default.