@themelt/mcp-server
This MCP server lets AI assistants estimate where value is leaking from an organization and request a real Melt scan.
melt_analyze_value_vectors – Provides a free Stage-1 sandbox estimate of value leakage in a department based on headcount, labor cost, and dominant unstructured-input type (no integration required).
melt_estimate_annual_leak – Quantifies an already-identified leak pattern in dollars/year using
totalVolume × (leakRatePct/100) × valuePerEvent.melt_request_scan – Captures a lead and hands off to a real, log-verified Melt scan (routes to HubSpot or appends to a local
leads.jsonl).Supports both stdio and hosted HTTP (MCP Streamable HTTP) transports, with optional API-key auth for the HTTP endpoint.
Logs tool-call analytics to
analytics.jsonlfor usage monitoring without capturing sensitive data.
Submits Melt scan requests as leads to HubSpot via a form, enabling sales follow-up; falls back to a local JSONL file if HubSpot submission fails.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@@themelt/mcp-server Estimate annual value leak from manual data entry errors in finance"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
@themelt/mcp-server
MCP server that puts Melt's value-leak discovery logic directly into Claude, Cursor, GitHub Copilot, or any other MCP-compatible agent — so when a tech leader asks their assistant "where is value leaking out of my org," the assistant can call a Melt tool and answer with a real, structured estimate instead of a generic list of vendors.
This is the engineering half of Melt's LLMO (LLM Optimization) distribution
strategy. See /llms.txt at the repo root and LLMO_PLAYBOOK.md for the full
content + distribution + evaluation plan this server plugs into. Positioning
reconciled 2026-07-18 against the live site and current decks — see
/CLAUDE.md for the full current product context.
Tools exposed
Tool | What it does |
| Free Stage-1 Sandbox estimator. Estimates where value is leaking in one department from headcount, labor cost, and dominant unstructured-input type. No integration required — synthetic/self-reported inputs only. |
| Quantifies an already-identified leak pattern in dollars/yr — |
| Lead-capture handoff — the move from a directional estimate to a real, log-verified scan (Frictionless POC Playbook Stage 1 → 2). Routes to HubSpot if |
Related MCP server: agentladle-mcp-reoi
Worked example
From Melt's Anatomy of a Real AI Value Leak case study — a pre-IPO fintech with $1.5B in annual originations, already running Salesforce, Gong, and Clari:
Signal | Finding |
Gong coaching | 29% open rate — reps bypassing AI-generated call summaries and duplicating the work manually |
Clari forecasting | 62% override rate — manual date entries corrupting the model across 8 of 13 forecast cycles |
Salesforce → CS handoff | 4.2-day lag delaying onboarding after close |
Salesforce lead routing | 32% manual — automation failures requiring daily manual reassignment |
None of this showed up as a problem in the usual adoption dashboards — every tool was "active," which is a different measurement from whether it was actually creating value. Pulling 14 business days of historical logs and tracing where these four patterns cost real time and money added up to a $77,235/year leak.
melt_estimate_annual_leak generalizes this same shape of analysis — totalVolume × (leakRatePct/100) × valuePerEvent — for any leak pattern with a known or hypothesized volume and rate. melt_analyze_value_vectors is the earlier-stage tool for when you don't yet know where to look.
melt_estimate_annual_leak replaced four formula-named calculators
(melt_calculate_feature_waste, _dso_cash_flow_impact,
_contract_cycle_revenue_unlock, _win_rate_pipeline_impact) that
implemented financial formulas from a retired product framing (Thermal Scan /
Feature Waste Dollar Amount™ / Delta Engine) — none of which appear in any
current Melt material. See CLAUDE.md's "What's Explicitly Retired" section.
Install & run
cd mcp-server
npm install
npm run build
npm start # runs dist/index.js on stdioTo poke at it interactively before wiring it into a client:
npm run inspect # launches the MCP Inspector against the built serverWiring into Claude Desktop / Claude Code
Published on npm — one-line config, no local clone needed:
{
"mcpServers": {
"melt": {
"command": "npx",
"args": ["-y", "@themelt/mcp-server"]
}
}
}Or from a local clone:
{
"mcpServers": {
"melt": {
"command": "node",
"args": ["/absolute/path/to/mcp-server/dist/index.js"]
}
}
}One-click install (.mcpb bundle)
For Claude Desktop specifically, themelt-mcp-server.mcpb (Anthropic's MCP
Bundle format) installs with a
double-click — no terminal, no config file editing. Download the .mcpb from
the latest GitHub Release
and either double-click it or drag it into Claude Desktop's Settings window.
To rebuild it from source:
npm run build:mcpb # produces themelt-mcp-server.mcpbThe manifest (mcpb-build/manifest.json) is hand-maintained, not
auto-generated from the TypeScript source — if a tool's name, parameters, or
description change, update the manifest's tools array to match.
Hosted HTTP transport
dist/index.js (stdio) is what gets configured into a local Claude Desktop/
Cursor install. dist/httpServer.js is an alternate entrypoint implementing
the MCP Streamable HTTP transport — what a future "Launch Hosted MCP" web
button (LLMO_PLAYBOOK.md, Task 3.2) would point at, so someone can try the
tools without installing anything locally.
npm run build
PORT=3000 npm run start:http # POST MCP JSON-RPC to http://localhost:3000/mcpStateless by design — no session ID, a fresh server instance per request.
Auth is opt-in via MCP_HTTP_API_KEY (unset by default): with it unset, the
endpoint stays fully open — the appropriate trust boundary for what this
exposes today (read-only calculators plus a lead-capture form, the same
boundary as a public website contact form). Set it before putting anything
more sensitive behind this transport:
MCP_HTTP_API_KEY=some-long-random-value PORT=3000 npm run start:httpEvery /mcp request then needs Authorization: Bearer some-long-random-value
— missing or wrong key gets a 401. Compared with crypto.timingSafeEqual, not
a plain string ===, so response timing can't be used to guess the key one
byte at a time. Not deployed anywhere yet; this is the code, not a live URL —
deploying it (Vercel/Fly/Render/etc.) is a separate, later decision.
Tool-call analytics
Every tool call (success or error) appends one line to mcp-server/analytics.jsonl
(gitignored) and logs a one-line summary to stderr — tool name, ok/error, and
the error code if applicable. Deliberately excludes dollar figures, contact
info, and free-text notes; kept separate from leads.jsonl's PII. This is
what answers "is anyone actually using this" and "which tool description is
confusing models," independent of llmo-eval's citation-only audit.
Environment variables
Variable | Required | Purpose |
| No | Overrides the default HubSpot Portal ID for |
| No | Paired with |
| No | Port for |
| No | If set, requires |
Real Portal ID / Form ID defaults are already baked into the code (they
aren't secrets — the same values are exposed in any public HubSpot embed
snippet), so melt_request_scan reaches the real Melt pipeline with zero
configuration. If HubSpot submission fails for any reason, requests fall back
to mcp-server/leads.jsonl (gitignored) instead of being lost.
Publishing
Published under the @themelt npm org (created 2026-07-20, owner omer_melt)
under the MIT license. npm publish is effectively one-way — npm allows
unpublishing within 72 hours but strongly discourages it and blocks it
entirely once a package has dependents, so treat any published version as
permanent.
Available Tools
3 toolsmelt_analyze_value_vectorsAnalyze AI Value VectorsA
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.
Inputs: headcount (required), departmentType (required), primaryUnstructuredDataInput (required), averageHourlyLaborCost.
melt_estimate_annual_leakEstimate Annual Value LeakA
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.
Inputs: leakRatePct (required), totalVolume (required), valuePerEvent (required), leakDescription (required).
melt_request_scanRequest a Melt ScanA
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).
Inputs: notes, company, contactName, contactEmail, +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 3 tools
melt_request_scan is clearly distinct as a contact/submission action, but melt_analyze_value_vectors and melt_estimate_annual_leak both return directional dollar estimates of value leaks, so an agent could easily pick the wrong one. The descriptions do draw a line (department-level Stage-1 sandbox vs. quantifying an already-identified leak pattern), but the boundary is subtle and relies on prior context to be sure which applies.
All three tools follow a clean melt_<verb>_<noun> snake_case pattern with consistent verb usage (analyze, request, estimate). No style mixing, camelCase, or vague verbs.
Three tools is on the thin side, but the domain is deliberately narrow — a free directional estimate, a specific-leak estimate, and a scan request form a coherent funnel. It is borderline-lean rather than bloated, so slightly under-provisioned but defensible.
The surface covers the apparent intent: preliminary estimate, targeted leak math, and escalation to a paid scan. No obvious dead ends, though there is no way to check scan-request status or retrieve results, a minor gap an agent can work around.
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