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INITE Studio MCP server

Which MCP servers are INITE Studio's own, from the official registry and from asking its own domain, whether they answer, and what they offer. A program checked each of these, nobody copied them from a product page.

read 2026-10-26

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Whose server it is
  • Registry namestudio.inite/ideaudit-toolsverified in the MCP registry

The registry name sits under inite.studio. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.

When we asked itchecked 2026-10-26
  • Stateanswerschecked 2026-10-26
  • Tools21
  • Server calls itselfideaudit
  • Server version0.1.0
  • Protocol version2025-06-18

On 2026-10-26 the server answered initialize and tools/list without a sign-in. First answer in 108 ms.

Should an agent connect itNot proven either way yet
  • Its tools do every use case we checked: score a startup idea against market, pain and competition signals; decide go, refine or kill on a new idea.
  • Checked on 2 of the 7 days it takes to judge reliability.

Once connected

  • For "decide go, refine or kill on a new idea", the likely first call is get_started.
  • Parameters are untyped or undescribed in 19 of 21 tools: check arguments before calling.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

Measured dailydaily checks: 2, the first on 2026-10-26
  • INITE MCP scoreafter a weeka score needs 7 daily checks: 2 so far
  • What an agent can do with it2 of 2 use cases
  • Answeredmeasuring2 of the 7 daily checks it takes to print a share
  • Median first answer78 ms
  • Space in an agent's context≈ 4,111 tokensthe definitions of all its tools (21), which an agent carries on every turn
  • Tool definitionsC · 68 of 100
  • Typed results0 of 21 toolsdeclared outputSchema: results come typed, read once
  • Parameters without a description: in 19 of 21
  • Not saying whether it only reads or can destroy: 21 of 21

What an agent can do with it

  • can do · score a startup idea against market, pain and competition signals
  • not sure · pull live search, Reddit and funding data into an audit
  • can do · decide go, refine or kill on a new idea

Each of the product's use cases, put to Jev against this server's tool list: can an agent do it with these tools?

Worth a look

None of the five warning signs in these definitions.

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (25%), doing the product's job (25%), definitions (20%), context (10%), use (10%) and how readily a client connects (10%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

Tools it offers21 tools · read 2026-10-26
  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

How to connect

Add the server to your MCP client config. Claude Desktop, Cursor and most other clients read this shape:

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

Add it

Claude Code, in a terminal

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

Add to CursorAdd to VS Code

claude.ai, ChatGPT and other clients: add https://api.inite.studio/mcp as a custom connector in the settings.

Also shipped through: npm

Each line is something a program found when it went looking: the registry entry, the server's own answer, a spec that parses. A missing line means we did not find it, not that it does not exist.