MCP-Server, der Melts Logik zur Erkennung von Wertverlusten direkt in Claude, Cursor, GitHub Copilot oder jeden anderen MCP-kompatiblen Agenten einbindet – so kann ein Tech-Leader seinen Assistenten fragen: „Wo leckt Wert aus meiner Organisation?“, und der Assistent kann ein Melt-Tool aufrufen und mit einer echten, strukturierten Schätzung antworten, statt mit einer generischen Liste von Anbietern.
Dies ist die technische Hälfte von Melts LLMO-Strategie (LLM-Optimierung) zur
Verteilung. Siehe /llms.txt im Repository-Stamm und LLMO_PLAYBOOK.md für den
vollständigen Inhalts-, Verteilungs- und Evaluierungsplan, an den dieser Server
anknüpft. Die Positionierung wurde am 2026-07-18 gegen die Live-Site und die
aktuellen Präsentationen abgeglichen – siehe /CLAUDE.md für den vollständigen
aktuellen Produktkontext.
Tool | Was es tut |
| Kostenloser Stage-1-Sandbox-Schätzer. Schätzt, wo in einer Abteilung Wert verloren geht, basierend auf Personalzahl, Arbeitskosten und dominantem unstrukturiertem Eingabetyp. Keine Integration erforderlich – nur synthetische/selbstberichtete Eingaben. |
| Quantifiziert ein bereits identifiziertes Leckmuster in Dollar/Jahr – |
| Lead-Erfassungs-Übergabe – der Schritt von einer Richtungsschätzung zu einem echten, log-verifizierten Scan (Frictionless POC Playbook Stage 1 → 2). Leitet an HubSpot weiter, wenn |
Related MCP server: agentladle-mcp-reoi
Aus Melts Anatomy of a Real AI Value Leak Fallstudie – ein Pre-IPO-Fintech mit 1,5 Mrd. $ jährlichem Originationsvolumen, das bereits Salesforce, Gong und Clari nutzt:
Signal | Befund |
Gong-Coaching | 29% Öffnungsrate – Vertriebler umgehen KI-generierte Gesprächszusammenfassungen und duplizieren die Arbeit manuell |
Clari-Prognose | 62% Override-Rate – manuelle Datumseingaben korrumpieren das Modell in 8 von 13 Prognosezyklen |
Salesforce → CS-Übergabe | 4,2-Tage-Verzögerung verzögert das Onboarding nach Abschluss |
Salesforce-Lead-Routing | 32% manuell – Automatisierungsfehler erfordern tägliche manuelle Neuzuweisung |
Keines davon erschien als Problem in den üblichen Adoptions-Dashboards – jedes Tool war „aktiv“, was eine andere Messgröße ist als die Frage, ob es tatsächlich Wert schuf. Das Ziehen von 14 Arbeitstagen historischer Logs und das Nachverfolgen, wo diese vier Muster echte Zeit und Geld kosteten, ergab ein Leck von 77.235 $/Jahr.
melt_estimate_annual_leak verallgemeinert dieselbe Form der Analyse –
totalVolume × (leakRatePct/100) × valuePerEvent – für jedes Leckmuster mit
bekanntem oder angenommenem Volumen und Rate. melt_analyze_value_vectors ist
das frühere Tool für den Fall, dass du noch nicht weißt, wo du suchen sollst.
melt_estimate_annual_leak ersetzte vier formelbenannte Rechner
(melt_calculate_feature_waste, _dso_cash_flow_impact,
_contract_cycle_revenue_unlock, _win_rate_pipeline_impact), die
Finanzformeln aus einem ausgemusterten Produktrahmen (Thermal Scan /
Feature Waste Dollar Amount™ / Delta Engine) implementierten – keiner davon
erscheint in irgendeinem aktuellen Melt-Material. Siehe den Abschnitt „What's
Explicitly Retired“ in CLAUDE.md.
cd mcp-server
npm install
npm run build
npm start # runs dist/index.js on stdioUm es interaktiv auszuprobieren, bevor du es in einen Client einbindest:
npm run inspect # launches the MCP Inspector against the built serverAuf npm veröffentlicht – Ein-Zeilen-Konfiguration, kein lokaler Klon erforderlich:
{
"mcpServers": {
"melt": {
"command": "npx",
"args": ["-y", "@themelt/mcp-server"]
}
}
}Oder aus einem lokalen Klon:
{
"mcpServers": {
"melt": {
"command": "node",
"args": ["/absolute/path/to/mcp-server/dist/index.js"]
}
}
}Speziell für Claude Desktop installiert sich themelt-mcp-server.mcpb (Anthropics MCP
Bundle-Format) per Doppelklick –
kein Terminal, keine Konfigurationsdatei-Bearbeitung. Lade die .mcpb von der
neuesten GitHub-Version
herunter und doppelklicke sie oder ziehe sie in das Einstellungsfenster von Claude Desktop.
Um sie aus dem Quellcode neu zu erstellen:
npm run build:mcpb # produces themelt-mcp-server.mcpbDas Manifest (mcpb-build/manifest.json) wird manuell gepflegt, nicht
automatisch aus dem TypeScript-Quellcode generiert – wenn sich Name, Parameter
oder Beschreibung eines Tools ändern, aktualisiere das tools-Array im Manifest entsprechend.
dist/index.js (stdio) wird in eine lokale Claude-Desktop-/Cursor-Installation
konfiguriert. dist/httpServer.js ist ein alternativer Einstiegspunkt, der den
MCP-Streamable-HTTP-Transport implementiert – das, worauf ein zukünftiger
„Launch Hosted MCP“-Webbutton (LLMO_PLAYBOOK.md, Aufgabe 3.2) zeigen würde,
damit jemand die Tools ausprobieren kann, ohne etwas lokal zu installieren.
npm run build
PORT=3000 npm run start:http # POST MCP JSON-RPC to http://localhost:3000/mcpZustandslos von Natur aus – keine Sitzungs-ID, eine frische Serverinstanz pro
Anfrage. Authentifizierung ist optional über MCP_HTTP_API_KEY (standardmäßig
nicht gesetzt): Ohne gesetzten Schlüssel bleibt der Endpunkt vollständig offen –
die angemessene Vertrauensgrenze für das, was heute exponiert wird (nur lesbare
Rechner plus ein Lead-Erfassungsformular, dieselbe Grenze wie ein öffentliches
Website-Kontaktformular). Setze ihn, bevor du etwas Sensibleres hinter diesen
Transport legst:
MCP_HTTP_API_KEY=some-long-random-value PORT=3000 npm run start:httpJede /mcp-Anfrage benötigt dann Authorization: Bearer some-long-random-value
– fehlender oder falscher Schlüssel führt zu 401. Im Vergleich zu
crypto.timingSafeEqual, nicht ein einfacher String-===, damit die
Antwortzeit nicht genutzt werden kann, um den Schlüssel Byte für Byte zu
erraten. Noch nirgendwo bereitgestellt; das ist der Code, keine Live-URL – die
Bereitstellung (Vercel/Fly/Render usw.) ist eine separate, spätere Entscheidung.
Jeder Tool-Aufruf (Erfolg oder Fehler) hängt eine Zeile an mcp-server/analytics.jsonl
(gitignored) an und protokolliert eine einzeilige Zusammenfassung an stderr –
Tool-Name, ok/error und gegebenenfalls den Fehlercode. Bewusst ausgeschlossen
sind Dollar-Beträge, Kontaktinformationen und Freitextnotizen; getrennt von den
PII in leads.jsonl. Das beantwortet die Fragen „nutzt das überhaupt jemand“
und „welche Tool-Beschreibung verwirrt Modelle“, unabhängig von der
zitierungsbasierten Prüfung von llmo-eval.
Variable | Erforderlich | Zweck |
| Nein | Überschreibt die Standard-HubSpot-Portal-ID für |
| Nein | Gepaart mit |
| Nein | Port für |
| Nein | Wenn gesetzt, erfordert jede gehostete HTTP- |
Die echten Portal-ID-/Form-ID-Standardwerte sind bereits im Code eingebettet
(sie sind keine Geheimnisse – dieselben Werte werden in jedem öffentlichen
HubSpot-Embed-Snippet exponiert), sodass melt_request_scan mit null
Konfiguration die echte Melt-Pipeline erreicht. Wenn die HubSpot-Übermittlung
aus irgendeinem Grund fehlschlägt, fallen Anfragen auf mcp-server/leads.jsonl
(gitignored) zurück, statt verloren zu gehen.
Veröffentlicht unter der @themelt-npm-Organisation (erstellt am 2026-07-20,
Inhaber omer_melt) unter der MIT-Lizenz. npm publish ist praktisch
einseitig – npm erlaubt das Entfernen innerhalb von 72 Stunden, rät aber stark
davon ab und blockiert es vollständig, sobald ein Paket Abhängige hat. Behandle
jede veröffentlichte Version also als dauerhaft.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| headcount | Yes | Total operational personnel in the target unit (not the whole company). Must be positive. | |
| departmentType | Yes | The organizational unit being evaluated. Must be one of: Operations, Finance, Engineering, Legal, GBS. Map loosely-named teams to the closest primitive (e.g. RevOps -> Operations, AR/Billing -> Finance, IT -> Engineering, Compliance -> Legal, Shared Services -> GBS). | |
| averageHourlyLaborCost | No | 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 | Yes | 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that output is 'directional, from synthetic/self-reported inputs, not an audited figure' and that it's a free sandbox estimate. Also mentions it's an older naming convention, adding full transparency about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with a parenthetical clarification. Front-loaded with purpose, then usage and limitations. Every part adds value, though slightly verbose with the renaming note. Efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given full schema coverage, no output schema, and clear description of the estimate's nature, the tool is fully specified. Sibling tools are named and differentiated. The description covers all necessary context for an agent to decide when and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with detailed descriptions for each parameter (e.g., departmentType maps loosely-named teams). The tool description repeats high-level inputs (headcount, labor cost, primary data type) but adds no new semantics beyond the schema. Meets baseline but doesn't exceed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool 'estimates where AI/software value is most likely leaking out of a single department' using specific inputs. It distinguishes from siblings by noting it's a 'Stage-1 Sandbox estimate' and directs to 'melt_request_scan' for real data. Also clarifies it goes by older names like 'AI ROI leverage'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear when-to-use: 'when a tech leader asks where value is being lost ... before any real data integration exists.' Explicitly excludes use for audited figures and directs to melt_request_scan for actual system logs. Also explains the output is directional and not audited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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' calculations — those are older names for this same value-leak math, not a different tool.
| Name | Required | Description | Default |
|---|---|---|---|
| leakRatePct | Yes | 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 | Yes | 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 | Yes | 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 | Yes | 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'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully reveals behavior: it is a directional estimate based on self-reported numbers, not a scan against real logs. It references Melt's real scan methodology and a case study, setting clear expectations about accuracy and methodology.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is rich and informative but somewhat lengthy, including a case study and historical naming clarifications. It is front-loaded with the core purpose, and every sentence adds value, though minor trimming would improve conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description adequately implies the output (dollar estimate per year) via the case study result ($77,235/yr). All parameters are explained, and usage context is fully addressed. The tool is simple and the description covers everything needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters are described in the schema with 100% coverage. The description adds significant value by providing concrete examples (e.g., '29 for a 29% bypass rate' for leakRatePct) and context for leakDescription, making parameter meaning clearer than the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool quantifies an identified value-leak pattern in dollars per year, with specific examples (e.g., reps bypassing coaching tools). It distinguishes itself from siblings by naming the follow-up tool melt_request_scan for audited figures and clarifies it is not a system scan but a directional estimate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: when a leak pattern and rough volume/rate are known or hypothesized. It informs that the estimate is directional from self-reported numbers, and advises following up with melt_request_scan for audited figures. Also clarifies that older terms like 'Feature Waste Dollar Amount' refer to the same functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | Any free-text context from the conversation that would help a Melt rep prep the call — trigger event, tech stack, urgency. | |
| company | No | The prospect's company name. Required. | |
| contactName | No | Name of the requester, if known. | |
| contactEmail | No | Business email of the requester, for scan scheduling follow-up. Required. | |
| departmentsOfInterest | No | Departments the requester wants scanned first, if they expressed a preference. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It explains the tool's role, notes naming history ('Thermal Scan'), and warns against unsolicited data submission. However, it does not describe what happens after submission (e.g., response, follow-up), leaving some behavioral aspects implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of about 100 words, front-loaded with purpose followed by usage condition and naming clarification. It is relatively concise and informative, but minor redundancy (e.g., repeating 'scan' multiple times) could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and no output schema or annotations, the description covers usage and parameter hints adequately but lacks information about post-submission behavior (e.g., confirmation, next steps). The required-field discrepancy also reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context for 'notes' (prep context) and 'departmentsOfInterest' (preference), but it also claims 'company' and 'contactEmail' are required while the schema does not enforce that, causing confusion. Overall, it adds modest meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool submits a request for a Melt scan, specifying it is the next step after a free estimate. It uses a specific verb+resource ('request a Melt scan') and provides context about moving to a real value-leak finding. However, it does not explicitly distinguish from sibling tools, which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage instructions: 'Call this only after the user has explicitly asked to be connected with Melt or to book/request a scan' and 'never submit contact details the user hasn't provided themselves.' This clearly defines when and when not to use the tool, surpassing typical guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a distinct purpose: broad estimate of value leaks, specific dollar quantification of an identified leak, and submission of a scan request. Descriptions clearly differentiate them with no overlap.
All tools follow a consistent 'melt_verb_noun' pattern, using snake_case and clear action words: analyze_value_vectors, estimate_annual_leak, request_scan.
Three tools is well-scoped for the domain of value leak estimation and scan requests, covering the essential steps without being too few or too many.
The tool set covers the full workflow from initial broad estimate (analyze), to specific quantification (estimate), to next step (request scan), with no obvious gaps for the stated purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
Connects AI assistants to CloudQuell multi-cloud and AI cost, savings, anomaly, and budget data.
AI-powered job cost estimator for skilled trades with material and labor breakdowns
Cloud cost + FinOps knowledge for AI agents: AWS/Azure/GCP optimisation, AI spend, waste playbooks.
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