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ideaudit

REMOTE · API.INITE.STUDIO · 2 COMPONENTS · SCANNED SEP 25

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

+3 this week 67 Trust /100
Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →

Endpoint Security57
Transport & Reachability100
Schema Quality & AI Usability65
  • AI-judged instruction clarity (good).Pass
  • Context-footprint check failed: tool/resource definitions use about 2492 tokens (~118/item across 21 items; 21 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management60
  • Stability observed for 18 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage73
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 18% of tool parameters carry a description.Partial
Tool Safety75
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "derive_kill_criteria" implies "kill" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
  • An AI judge read all 22 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Install

How do I install the ideaudit MCP server?

ideaudit is a hosted endpoint at https://api.inite.studio/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

remote · api.inite.studio

# add to Claude Code
claude mcp add --transport http studio-inite-ideaudit-tools 'https://api.inite.studio/mcp'
// .cursor/mcp.json
{
  "mcpServers": {
    "studio-inite-ideaudit-tools": {
      "url": "https://api.inite.studio/mcp"
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "studio-inite-ideaudit-tools": {
      "type": "http",
      "url": "https://api.inite.studio/mcp"
    }
  }
}
# ~/.codex/config.toml
[mcp_servers.studio-inite-ideaudit-tools]
url = "https://api.inite.studio/mcp"
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "studio-inite-ideaudit-tools": {
      "type": "remote",
      "url": "https://api.inite.studio/mcp",
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add studio-inite-ideaudit-tools --url 'https://api.inite.studio/mcp' --transport streamable-http
# ~/.hermes/config.yaml
mcp_servers:
  studio-inite-ideaudit-tools:
    url: "https://api.inite.studio/mcp"
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "studio-inite-ideaudit-tools": {
      "Transport": "http",
      "Url": "https://api.inite.studio/mcp"
    }
  }
}
# add to Vellum
assistant mcp add studio-inite-ideaudit-tools -t streamable-http -u 'https://api.inite.studio/mcp'
// mcp.json
{
  "mcpServers": {
    "studio-inite-ideaudit-tools": {
      "type": "http",
      "url": "https://api.inite.studio/mcp"
    }
  }
}

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 25 Sept 26 0
    • We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
  • 24 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

  • 22 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.

  • 20 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.

  • 18 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

  • 15 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

  • 13 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

  • 11 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 25 Sept 2026 · Probed https://api.inite.studio/mcp

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=inite.studio CN=WE1,O=Google Trust Services,C=US 4 Aug 2026 2 Nov 2026 ECDSA 256 ECDSA-SHA256 1da1c84631c195460e98211105889d68
SANs: inite.studio, *.inite.studio
CN=WE1,O=Google Trust Services,C=US (CA) CN=GTS Root R4,O=Google Trust Services LLC,C=US 13 Dec 2023 20 Feb 2029 ECDSA 256 ECDSA-SHA384 7ff31977972c224a76155d13b6d685e3
CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE 15 Nov 2023 28 Jan 2028 ECDSA 384 SHA256-RSA 7fe530bf331343bedd821610493d8a1b

Background: What to check on a remote MCP endpoint →

DNSSEC insecure

Validation of api.inite.studio. — Not signed

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
studio. present 1354 8 Verified
inite.studio. absent Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation
Authentication No authorisation required

The endpoint answered without asking for a token. Anyone who knows the URL can reach it.

Result No authorisation required
HTTP status 200

Background: How OAuth 2.1 works in the 2026 MCP spec →

Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://api.inite.studio/mcp Verified 200
http (plaintext) http://api.inite.studio/mcp HTTPS enforced 301 https://api.inite.studio/mcp
MCP tools · 21 exposed · ~2,426 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
compute_barrier ~70

Compute barrier_score (0-24) + label (PRISTINE/OPEN/COMPETITIVE/CROWDED) from competitor counts + SERP noise fraction.

NameTypeReqDescription
adjacentCompetitorCountinteger––
directCompetitorCountintegeryes–
serpNoisenumber––

No output schema declared.

No examples provided.

compute_budget_proof ~90

Compute budget_proof_score (0-10) + label (STRONG/CONFIRMED/WEAK/ABSENT) + purchase_intent_pct from pricing hits + review-site hits + intent mentions.

NameTypeReqDescription
hasNamedPricingboolean––
pricingHitsCountintegeryes–
purchaseIntentMentionsinteger––
reviewSiteHitsCountinteger––

No output schema declared.

No examples provided.

compute_build_complexity ~79

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.

NameTypeReqDescription
externalApisCountintegeryes–
integrationsCountinteger––
stackComplexityTagsarray––

No output schema declared.

No examples provided.

compute_collection_scores ~61

Compute 12 deterministic collection scores (0-100) + badges + death reason for an enriched idea. Pure math. No external calls.

NameTypeReqDescription
analysisIdstringyes–
enrichedDataobjectyesEnrichedData with canonical_idea signals.

No output schema declared.

No examples provided.

compute_crossed_matrix ~184

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.

NameTypeReqDescription
hasMajorContradictionboolean––
lensScoresarrayyes–
observerobject–Founder profile that crosses with the substrate idea to produce an observer-relative verdict. When omitted, only the substrate verdict is returned.
sectorstring––
stagestringyes–
stageProbabilitiesobject––
unresolvedContradictionsinteger––

No output schema declared.

No examples provided.

compute_dealbreakers_v2 ~200

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.

NameTypeReqDescription
hasMajorContradictionboolean––
lensScoresarrayyes–
observerobject–Founder profile that crosses with the substrate idea to produce an observer-relative verdict. When omitted, only the substrate verdict is returned.
sectorstring––
stagestringyes–
stageProbabilitiesobject––
unresolvedContradictionsinteger––

No output schema declared.

No examples provided.

compute_funding_momentum ~62

Compute funding_momentum_score (0-10) + badge (HOT/WARM/COOL/COLD) from tier-weighted funding-article hit counts.

NameTypeReqDescription
hitsByTierobjectyes–
recent30dHitsinteger––

No output schema declared.

No examples provided.

compute_hiring_demand ~47

Compute hiring_demand_score (0-10) from priority-weighted ATS site hit counts (use registries/hiring-sources for priorities).

NameTypeReqDescription
sitesarrayyes–

No output schema declared.

No examples provided.

compute_lrs_composite ~113

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.

NameTypeReqDescription
barrierScorenumberyes–
monetizationScorenumberyes–
searchVelocityScorenumberyes–
socialPainScorenumberyes–

No output schema declared.

No examples provided.

compute_lrs_composite_v2 ~256

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).

NameTypeReqDescription
barrierScorenumberyes–
budgetProofScorenumberyes–
buildComplexityPenaltynumber––
monetizationScorenumberyes–
searchVelocityScorenumberyes–
sectorProfilestring–Opt-in sector weight override. Default uses Python canonical weights.
socialPainScorenumberyes–
xSignalScorenumberyes–

No output schema declared.

No examples provided.

compute_monetization ~83

Compute monetization_score (0-21) + label + has_pricing_anchors from pricing anchors + model tags + deal cycle hint.

NameTypeReqDescription
dealCyclestring–instant/days/weeks/months/quarters
modelTagsarray–e.g. ["subscription","usage","marketplace"]
pricingAnchorsCountintegeryes–

No output schema declared.

No examples provided.

compute_multi_source_tam ~117

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.

NameTypeReqDescription
inputsarrayyes–

No output schema declared.

No examples provided.

compute_ppc_spend_signal ~118

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.

NameTypeReqDescription
avgCpcUsdnumberyes–
competitionnumber––
competitorBiddersinteger––
totalMonthlySpendUsdnumberyes–

No output schema declared.

No examples provided.

compute_search_velocity ~74

Compute search_velocity_score (0-25) from Trends timeline values + rising queries count + geo region count.

NameTypeReqDescription
geoRegionCountinteger––
risingQueriesCountinteger––
timelineValuesarrayyesMonthly Trends values 0-100 (e.g. last 10-12 months).

No output schema declared.

No examples provided.

compute_search_velocity_v2 ~270

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).

NameTypeReqDescription
daysSinceLastSignalnumber–Optional: days since most recent confirming signal. Triggers exponential freshness decay (half-life 90d).
externalVolumeNormnumberyesNormalized 0-1 demand volume from EXTERNAL sources (Amazon, app stores, jobs). Caller normalizes before passing.
geoSpreadNormnumberyes0-1 geographic spread (regions with interest > threshold).
intentNormnumberyes0-1 commercial/transactional intent ratio.
trendsTimelineValuesarrayyesMonthly Trends values 0-100. Used ONLY to derive trendNorm — never as raw volume.

No output schema declared.

No examples provided.

compute_social_pain ~70

Compute social_pain_score (0-30) + total mentions + dominant perspective (business/consumer/trend/mixed).

NameTypeReqDescription
categoryCountsobject––
intentMentionsinteger––
painMentionsintegeryes–
urgencyMentionsinteger––

No output schema declared.

No examples provided.

compute_urgency_composite ~80

Compose composite_urgency_score (0-10) + badge (LOW/MEDIUM/HIGH/VERY_HIGH/EXTREME) from 3 sub-scores: news, pain, hiring.

NameTypeReqDescription
hiringSignalScorenumberyes–
newsSignalScorenumberyes–
painSignalScorenumberyes–

No output schema declared.

No examples provided.

compute_x_signal ~75

Compute x_signal_score (0-20) + recency share + positivity rate from X/Twitter mention counts.

NameTypeReqDescription
founderMentionsinteger––
mentionsCountintegeryes–
recent7dCountinteger––
sentimentNegativeinteger––
sentimentPositiveinteger––

No output schema declared.

No examples provided.

derive_kill_criteria ~139

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.

NameTypeReqDescription
dealbreakersobject–The result of compute_dealbreakers_v2.
icpDriftCountinteger––
unitEconobject–The result of validate_unit_economics.

No output schema declared.

No examples provided.

get_started ~55

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.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

validate_unit_economics ~183

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.

NameTypeReqDescription
annualRevenuenumberyes–
arpunumberyes–
cacnumber––
customersnumberyes–
grossMarginnumber––
ltvnumber––
monthlyChurnnumber––

No output schema declared.

No examples provided.

Common questions

What is the ideaudit MCP server?

ideaudit is an MCP server listed in the public MCP registry as studio.inite/ideaudit-tools. The scoring behind an audit allowed to say no. Twenty deterministic tools, offline, no account. This page covers its hosted endpoint (https://api.inite.studio/mcp).

Is the ideaudit MCP server safe to use?

ideaudit scores 67 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the ideaudit MCP server expose?

ideaudit exposes 21 tools: get_started, compute_barrier, compute_budget_proof, compute_build_complexity, compute_collection_scores, and 16 more. Their descriptions and schemas cost roughly 2,426 tokens of context every time the server is loaded.

Does the ideaudit MCP server require authentication?

No. We connected to ideaudit without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

Is the ideaudit MCP server still maintained?

ideaudit is still listed as active in the MCP registry. We last reached this channel on 25 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.