io.github.omer907/melt-mcp-server

Est-ce que io.github.omer907/melt-mcp-server est sûr ?

Au dernier audit MCP Hub, le serveur MCP « io.github.omer907/melt-mcp-server » est classé « Vérifié ». Aucun comportement malveillant détecté au test comportemental. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Versions auditées & empreintes

Vérifiez la version que vous utilisez : son empreinte doit correspondre à celle listée ici — sinon vous n'utilisez pas la version auditée.

⚠ La dernière version publiée (0.1.5) n'a pas encore été auditée. Un nouveau pentest est programmé ; les verdicts ci-dessous portent sur les versions listées.

Vous utilisez ce serveur ? Suivez-le (créez un compte) pour être alerté dès qu'une nouvelle version paraît ou que le verdict change.

Les 3 outils

• melt_analyze_value_vectors≈ 565 tokens

  Estimates where AI/software value is most likely leaking out of a single department, based on headcount, labor cost, and the type of chaotic/unstructured input it processes manually today. Use this when a tech leader asks where value is being lost or where AI would create the most immediate impact in their org, before any real data integration exists — this is Melt's free Stage-1 Sandbox estimate. Output is directional, from synthetic/self-reported inputs, not an audited figure — for a real finding tied to an actual system log, follow up with melt_request_scan. Also answers what earlier Melt materials called 'AI ROI leverage' or 'AI value vectors' — same estimate, older name.

  Paramètres, retour & exemple (4)

  Paramètres

  • headcount · number · requis
    Total operational personnel in the target unit (not the whole company). Must be positive.
  • departmentType · string · requis
    The organizational unit being evaluated. Must be one of: Operations, Finance, Engineering, Legal, GBS, Customer Success. Map loosely-named teams to the closest primitive (e.g. RevOps -> Operations, AR/Billing -> Finance, IT -> Engineering, Compliance -> Legal, Shared Services -> GBS, Support/CS/Client Success -> Customer Success).
  • averageHourlyLaborCost · number · optionnel
    Blended fully-loaded hourly labor cost for manual processors in this unit, in USD. Default of 45 is a reasonable US mid-market planning assumption if the caller doesn't know the real figure.
  • primaryUnstructuredDataInput · string · requis
    The dominant chaotic input the unit processes by hand today. Must be one of: PDF_INVOICES, CUSTOMER_TICKETS, LOGISTICS_DOCUMENTS, MANUAL_EXCEL. Choose the closest match: PDF_INVOICES for document-first bottlenecks, CUSTOMER_TICKETS for conversational/support-first bottlenecks, LOGISTICS_DOCUMENTS for shipping/customs/supply-chain paperwork, MANUAL_EXCEL for spreadsheet-driven reconciliation or reporting work.

  Exemple d'appel

  {"name": "melt_analyze_value_vectors", "arguments": {"headcount": 1, "departmentType": "texte", "primaryUnstructuredDataInput": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• melt_estimate_annual_leak≈ 509 tokens

  Quantifies a specific, already-identified value-leak pattern in dollars per year — e.g. reps bypassing a coaching tool's summaries, manual overrides corrupting a forecasting model, a manual handoff between two systems. Use this when a leak pattern and its rough volume/rate are already known or hypothesized. This mirrors Melt's real scan methodology (see the fintech case study: a 29% Gong bypass rate, a 62% Clari override rate, and a 4.2-day manual handoff combined into a $77,235/yr finding) — it is a directional estimate from self-reported numbers, not a scan against real system logs. For an audited figure, follow up with melt_request_scan. Covers what earlier Melt materials called 'Feature Waste Dollar Amount' (money leaking on licensed-but-unused software) and general 'AI ROI leverage' c

  Paramètres, retour & exemple (4)

  Paramètres

  • leakRatePct · number · requis
    Percentage of that volume exhibiting the leak behavior, between 0 and 100 (e.g. 29 for a 29% bypass rate, 62 for a 62% override rate).
  • totalVolume · number · requis
    Total annual volume of the relevant event or transaction — e.g. total call briefs generated, total deals closed, total support tickets, total lead assignments.
  • valuePerEvent · number · requis
    Dollar value at risk per leaking event, in USD — e.g. average deal value, loaded hourly cost of manual rework, cost of a delayed handoff day.
  • leakDescription · string · requis
    Plain-language description of the leak pattern observed or hypothesized — e.g. 'reps bypassing Gong call summaries and logging notes from memory', 'manual Slack handoff between Sales and Customer Success', 'guessed close dates overriding the forecasting model'.

  Exemple d'appel

  {"name": "melt_estimate_annual_leak", "arguments": {"leakRatePct": 1, "totalVolume": 1, "valuePerEvent": 1, "leakDescription": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• melt_request_scan≈ 410 tokens

  Submits a request for a Melt scan — the next step after Melt's free Stage-1 Sandbox estimate, moving to a real, log-verified value-leak finding tied to a dollar figure and a source system. Call this only after the user has explicitly asked to be connected with Melt or to book/request a scan — never submit contact details the user hasn't provided themselves. Earlier Melt materials called this a 'Thermal Scan' — same request, current name is just 'a scan' (no fixed 2-week/pricing claim attached anymore).

  Paramètres, retour & exemple (5)

  Paramètres

  • notes · string · optionnel
    Any free-text context from the conversation that would help a Melt rep prep the call — trigger event, tech stack, urgency.
  • company · string · optionnel
    The prospect's company name. Needed to route the request — if omitted, the tool asks for it instead of failing schema validation.
  • contactName · string · optionnel
    Name of the requester, if known.
  • contactEmail · string · optionnel
    Business email of the requester, for scan scheduling follow-up. Needed to route the request — if omitted, the tool asks for it instead of failing schema validation.
  • departmentsOfInterest · array · optionnel
    Departments the requester wants scanned first, if they expressed a preference.

  Exemple d'appel

  {"name": "melt_request_scan", "arguments": {"notes": "texte", "company": "texte", "contactName": "texte", "contactEmail": "texte", "departmentsOfInterest": []}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

@themelt/mcp-server@0.1.5