MCP server

ideaudit

studio.inite/ideaudit-tools

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

inite.studio (verified by the MCP Registry)

Status

Working at last check

Tools

21

Token cost

About 4,701 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

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

Gemini CLI

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

Cursor

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

VS Code

{"servers":{"ideaudit-tools":{"type":"http","url":"https://api.inite.studio/mcp"}}}

Antigravity

agy mcp add ideaudit-tools https://api.inite.studio/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ideaudit-tools","server_url":"https://api.inite.studio/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ideaudit-tools","server_url":"https://api.inite.studio/mcp"}

Tools

• compute_barrier ~125 tokens
  Compute barrier_score (0-24) + label (PRISTINE/OPEN/COMPETITIVE/CROWDED) from competitor counts + SERP noise fraction.
• compute_budget_proof ~136 tokens
  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 ~135 tokens
  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 ~112 tokens
  Compute 12 deterministic collection scores (0-100) + badges + death reason for an enriched idea. Pure math. No external calls.
• compute_crossed_matrix ~630 tokens
  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 ~650 tokens
  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 ~143 tokens
  Compute funding_momentum_score (0-10) + badge (HOT/WARM/COOL/COLD) from tier-weighted funding-article hit counts.
• compute_hiring_demand ~132 tokens
  Compute hiring_demand_score (0-10) from priority-weighted ATS site hit counts (use registries/hiring-sources for priorities).
• compute_lrs_composite ~176 tokens
  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 ~384 tokens
  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_monetization ~144 tokens
  Compute monetization_score (0-21) + label + has_pricing_anchors from pricing anchors + model tags + deal cycle hint.
• compute_multi_source_tam ~237 tokens
  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 ~171 tokens
  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 ~141 tokens
  Compute search_velocity_score (0-25) from Trends timeline values + rising queries count + geo region count.
• compute_search_velocity_v2 ~431 tokens
  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_social_pain ~163 tokens
  Compute social_pain_score (0-30) + total mentions + dominant perspective (business/consumer/trend/mixed).
• compute_urgency_composite ~136 tokens
  Compose composite_urgency_score (0-10) + badge (LOW/MEDIUM/HIGH/VERY_HIGH/EXTREME) from 3 sub-scores: news, pain, hiring.
• compute_x_signal ~132 tokens
  Compute x_signal_score (0-20) + recency share + positivity rate from X/Twitter mention counts.
• derive_kill_criteria ~198 tokens
  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
• get_started ~89 tokens
  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 ~236 tokens
  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

Browse

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