MCP-сервер, который встраивает логику Melt по обнаружению утечек ценности напрямую в Claude, Cursor, GitHub Copilot или любого другого MCP-совместимого агента, — так что когда технический руководитель спрашивает своего ассистента «где из моей организации утекает ценность», ассистент может вызвать инструмент Melt и ответить реальной структурированной оценкой, а не общим списком вендоров.
Это инженерная половина стратегии распространения LLMO (LLM Optimization) от Melt. См. /llms.txt в корне репозитория и LLMO_PLAYBOOK.md — там полный план контента + распространения + оценки, в который встраивается этот сервер. Позиционирование сверено 2026-07-18 с живым сайтом и текущими презентациями — см. /CLAUDE.md для полного актуального контекста продукта.
Инструмент | Назначение |
| Бесплатный оценочный инструмент этапа 1 (Stage-1 Sandbox). Оценивает, где в одном отделе утекает ценность, на основе численности сотрудников, стоимости труда и доминирующего типа неструктурированных входных данных. Интеграция не требуется — только синтетические/самостоятельно указанные входные данные. |
| Количественно оценивает уже выявленный паттерн утечки в долларах в год — |
| Передача на этап захвата лидов — переход от ориентировочной оценки к реальному сканированию, подтверждённому логами (Frictionless POC Playbook, этапы 1 → 2). Направляет запрос в HubSpot, если заданы |
Related MCP server: agentladle-mcp-reoi
Из кейса Melt Anatomy of a Real AI Value Leak — финтех-компания накануне IPO с $1,5 млрд годового объёма выданных кредитов, уже использующая Salesforce, Gong и Clari:
Сигнал | Результат |
Коучинг Gong | 29% открытий — представители обходят сгенерированные ИИ сводки звонков и дублируют работу вручную |
Прогнозирование Clari | 62% переопределений — ручные записи дат искажают модель в 8 из 13 циклов прогнозирования |
Передача Salesforce → CS | Задержка в 4,2 дня, откладывающая онбординг после закрытия сделки |
Маршрутизация лидов Salesforce | 32% вручную — сбои автоматизации, требующие ежедневного ручного переназначения |
Ничего из этого не отображалось как проблема в обычных дашбордах внедрения — каждый инструмент был «активен», но это другое измерение по сравнению с тем, создавал ли он реальную ценность. Анализ 14 рабочих дней исторических логов и отслеживание того, где эти четыре паттерна стоили реального времени и денег, в сумме дали утечку на $77 235/год.
melt_estimate_annual_leak обобщает ту же форму анализа — totalVolume × (leakRatePct/100) × valuePerEvent — для любого паттерна утечки с известным или предполагаемым объёмом и долей. melt_analyze_value_vectors — инструмент более раннего этапа для случаев, когда вы ещё не знаете, где искать.
melt_estimate_annual_leak заменил четыре калькулятора с формульными названиями (melt_calculate_feature_waste, _dso_cash_flow_impact, _contract_cycle_revenue_unlock, _win_rate_pipeline_impact), которые реализовывали финансовые формулы из снятой с производства продуктовой концепции (Thermal Scan / Feature Waste Dollar Amount™ / Delta Engine) — ни одна из них не фигурирует в актуальных материалах Melt. См. раздел «What's Explicitly Retired» в CLAUDE.md.
cd mcp-server
npm install
npm run build
npm start # runs dist/index.js on stdioЧтобы поэкспериментировать с ним интерактивно, прежде чем подключать к клиенту:
npm run inspect # launches the MCP Inspector against the built serverОпубликован на npm — конфигурация в одну строку, локальный клон не нужен:
{
"mcpServers": {
"melt": {
"command": "npx",
"args": ["-y", "@themelt/mcp-server"]
}
}
}Или из локального клона:
{
"mcpServers": {
"melt": {
"command": "node",
"args": ["/absolute/path/to/mcp-server/dist/index.js"]
}
}
}Для Claude Desktop в частности, themelt-mcp-server.mcpb (формат MCP Bundle от Anthropic) устанавливается двойным кликом — без терминала и без редактирования файлов конфигурации. Скачайте .mcpb из последнего релиза на GitHub и либо дважды кликните по нему, либо перетащите его в окно настроек Claude Desktop.
Чтобы пересобрать его из исходников:
npm run build:mcpb # produces themelt-mcp-server.mcpbМанифест (mcpb-build/manifest.json) поддерживается вручную, а не генерируется автоматически из TypeScript-исходников — если имя, параметры или описание инструмента меняются, обновите массив tools в манифесте.
dist/index.js (stdio) — именно он настраивается в локальной установке Claude Desktop/Cursor. dist/httpServer.js — альтернативная точка входа, реализующая транспорт MCP Streamable HTTP, — на неё указывала бы будущая веб-кнопка «Launch Hosted MCP» (LLMO_PLAYBOOK.md, Задача 3.2), чтобы можно было попробовать инструменты без локальной установки.
npm run build
PORT=3000 npm run start:http # POST MCP JSON-RPC to http://localhost:3000/mcpПо замыслу не хранит состояние — без идентификатора сессии, новый экземпляр сервера на каждый запрос. Аутентификация опциональна через MCP_HTTP_API_KEY (по умолчанию не задан): если он не задан, конечная точка остаётся полностью открытой — это соответствует границе доверия для того, что здесь доступно сегодня (калькуляторы только для чтения плюс форма захвата лидов, та же граница, что и контактная форма публичного сайта). Задайте его, прежде чем размещать за этим транспортом что-то более чувствительное:
MCP_HTTP_API_KEY=some-long-random-value PORT=3000 npm run start:httpКаждый запрос /mcp теперь требует Authorization: Bearer some-long-random-value — при отсутствии или неверном ключе возвращается 401. Сравнение выполняется через crypto.timingSafeEqual, а не обычным строковым ===, чтобы по времени ответа нельзя было подобрать ключ побайтово. Пока нигде не развёрнут; это код, а не живой URL — развёртывание (Vercel/Fly/Render и т.п.) — отдельное решение на более поздний срок.
Каждый вызов инструмента (успешный или с ошибкой) добавляет одну строку в mcp-server/analytics.jsonl (в gitignore) и выводит однострочную сводку в stderr — имя инструмента, ok/error и код ошибки, если применимо. Намеренно исключает денежные суммы, контактные данные и произвольные текстовые заметки; хранится отдельно от персональных данных (PII) из leads.jsonl. Именно это отвечает на вопросы «пользуется ли этим кто-нибудь» и «какое описание инструмента сбивает модели с толку», независимо от проверки llmo-eval, основанной только на цитировании.
Переменная | Обязательна | Назначение |
| Нет | Переопределяет Portal ID HubSpot по умолчанию для |
| Нет | Используется в паре с |
| Нет | Порт для |
| Нет | Если задан, требует |
Реальные значения Portal ID / Form ID по умолчанию уже встроены в код (это не секреты — те же значения раскрываются в любом публичном embed-фрагменте HubSpot), поэтому melt_request_scan обращается к реальному конвейеру Melt без какой-либо настройки. Если отправка в HubSpot по какой-либо причине не удаётся, запросы сохраняются в mcp-server/leads.jsonl (в gitignore), а не теряются.
Опубликован в npm-организации @themelt (создана 2026-07-20, владелец omer_melt) под лицензией MIT. npm publish фактически однонаправлен — npm разрешает удаление публикации в течение 72 часов, но настоятельно не рекомендует этого и полностью блокирует его, как только у пакета появляются зависимые проекты, поэтому считайте любую опубликованную версию постоянной.
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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