Melt のバリューリーク発見ロジックを Claude、Cursor、GitHub Copilot、その他あらゆる MCP 互換エージェントに直接組み込む MCP サーバーです。これにより、テックリーダーがアシスタントに「私の組織のどこから価値が漏れているのか」と尋ねたとき、アシスタントは Melt のツールを呼び出して、ありきたりなベンダーリストではなく、実際の構造化された見積もりで答えることができます。
これは、Melt の LLMO（LLM 最適化）配布戦略のエンジニアリング側の半分です。このサーバーが接続する完全なコンテンツ＋配布＋評価計画については、リポジトリルートの /llms.txt と LLMO_PLAYBOOK.md を参照してください。ポジショニングは、ライブサイトと現在のデッキに照らして 2026-07-18 に調整済みです。完全な現在のプロダクトコンテキストについては /CLAUDE.md を参照してください。
ツール | 機能 |
| 無料のステージ 1 サンドボックス推定ツール。従業員数、人件費、主要な非構造化入力タイプから、ある部門のどこで価値が漏れているかを推定します。統合は不要です。合成入力または自己申告入力のみを使用します。 |
| すでに特定されているリークパターンをドル/年で定量化します。 |
| リード獲得の引き継ぎ。方向性のある見積もりから、実際のログ検証済みスキャンへの移行です（摩擦のない POC プレイブックのステージ 1 → 2）。 |
Related MCP server: agentladle-mcp-reoi
Melt の 本物の AI バリューリークの構造 ケーススタディより。年間 15 億ドルのオリジネーションを誇る新規株式公開前のフィンテック企業で、すでに Salesforce、Gong、Clari を稼働させています。
シグナル | 調査結果 |
Gong コーチング | オープン率 29% — 担当者が AI 生成の通話サマリーをバイパスし、手動で作業を複製している |
Clari フォーキャスト | 上書き率 62% — 手動の日付入力により、13 のフォーキャストサイクルのうち 8 つでモデルが破損 |
Salesforce → CS ハンドオフ | 4.2 日間の遅延により、クロージング後のオンボーディングが遅延 |
Salesforce リードルーティング | 32% が手動 — 毎日の手動再割り当てが必要な自動化障害 |
これらは通常の導入ダッシュボードでは問題として表示されませんでした。すべてのツールが「アクティブ」であり、実際に価値を生み出しているかどうかとは別の測定だったからです。14 営業日分の履歴ログを取得し、これらの 4 つのパターンが実際の時間とコストを費やしている場所をトレースしたところ、年間 77,235 ドルのリークに達しました。
melt_estimate_annual_leak は、既知または推定のボリュームとレートを持つあらゆるリークパターンについて、同じ分析構造（totalVolume × (leakRatePct/100) × valuePerEvent）を一般化したものです。melt_analyze_value_vectors は、まだどこを見ればよいかわからない場合のための、より初期段階のツールです。
melt_estimate_annual_leak は、廃止された製品フレーミング（Thermal Scan / Feature Waste Dollar Amount™ / Delta Engine）の財務計算式を実装した 4 つの数式名の計算ツール（melt_calculate_feature_waste、_dso_cash_flow_impact、_contract_cycle_revenue_unlock、_win_rate_pipeline_impact）を置き換えたものです。これらは現在の Melt マテリアルには一切登場しません。CLAUDE.md の「明示的に廃止されたもの」セクションを参照してください。
cd mcp-server
npm install
npm run build
npm start # runs dist/index.js on stdioクライアントに配線する前に、対話的に試してみるには：
npm run inspect # launches the MCP Inspector against the built servernpm で公開済み — 1 行の設定で、ローカルクローンは不要です：
{
"mcpServers": {
"melt": {
"command": "npx",
"args": ["-y", "@themelt/mcp-server"]
}
}
}または、ローカルクローンから：
{
"mcpServers": {
"melt": {
"command": "node",
"args": ["/absolute/path/to/mcp-server/dist/index.js"]
}
}
}特に Claude Desktop の場合、themelt-mcp-server.mcpb（Anthropic の MCP バンドル形式）はダブルクリックするだけでインストールできます。ターミナルも設定ファイルの編集も不要です。最新の GitHub リリース から .mcpb をダウンロードし、ダブルクリックするか、Claude Desktop の設定ウィンドウにドラッグアンドドロップします。
ソースから再ビルドするには：
npm run build:mcpb # produces themelt-mcp-server.mcpbマニフェスト（mcpb-build/manifest.json）は TypeScript ソースから自動生成されるのではなく、手動で管理されています。ツールの名前、パラメータ、説明を変更した場合は、マニフェストの tools 配列もそれに合わせて更新してください。
dist/index.js（stdio）は、ローカルの Claude Desktop/Cursor インストールに設定されるものです。dist/httpServer.js は、MCP Streamable HTTP トランスポートを実装した代替エントリポイントです。将来の「ホスト型 MCP を起動」Web ボタン（LLMO_PLAYBOOK.md、タスク 3.2）がここを指すことになり、ユーザーはローカルに何もインストールせずにツールを試すことができます。
npm run build
PORT=3000 npm run start:http # POST MCP JSON-RPC to http://localhost:3000/mcpステートレス設計 — セッション ID はなく、リクエストごとに新しいサーバーインスタンスが作成されます。認証は MCP_HTTP_API_KEY によるオプトインです（デフォルトでは未設定）。未設定のままにすると、エンドポイントは完全にオープンなままになります。これは、今日ここで公開されているもの（読み取り専用の計算ツールとリード獲得フォーム。公開 Web サイトの問い合わせフォームと同じ境界）にとって適切な信頼境界です。このトランスポートの背後に、より機密性の高いものを配置する前に設定してください：
MCP_HTTP_API_KEY=some-long-random-value PORT=3000 npm run start:http/mcp へのすべてのリクエストには Authorization: Bearer some-long-random-value が必要になります。キーがない場合や間違っている場合は 401 が返ります。crypto.timingSafeEqual と比較して、プレーンな文字列 === ではないため、応答時間を利用してキーを 1 バイトずつ推測することはできません。まだどこにもデプロイされていません。これはコードであり、ライブ URL ではありません。デプロイ（Vercel/Fly/Render など）は、別の後日の決定事項です。
すべてのツールコール（成功またはエラー）は、mcp-server/analytics.jsonl（gitignore 済み）に 1 行追加し、stderr に 1 行のサマリー（ツール名、ok/error、該当する場合はエラーコード）を記録します。金額、連絡先情報、フリーテキストのメモは意図的に除外され、leads.jsonl の個人情報とは分離されています。これは、llmo-eval の引用のみの監査とは独立して、「実際に誰かがこれを使っているのか」「どのツールの説明がモデルを混乱させているのか」という質問に答えるためのものです。
変数 | 必須 | 目的 |
| いいえ |
|
| いいえ |
|
| いいえ |
|
| いいえ | 設定した場合、ホスト型 HTTP |
実際のポータル ID / フォーム ID のデフォルトはすでにコードに組み込まれているため（シークレットではありません。同じ値が公開されている HubSpot 埋め込みスニペットにも公開されています）、melt_request_scan はゼロ設定で実際の Melt パイプラインに到達します。何らかの理由で HubSpot への送信が失敗した場合、リクエストは失われることなく、mcp-server/leads.jsonl（gitignore 済み）にフォールバックされます。
MIT ライセンスの下、@themelt npm 組織（2026-07-20 作成、所有者 omer_melt）で公開されています。npm publish は事実上一方向です。npm では 72 時間以内の unpublish が可能ですが、強く推奨されておらず、依存関係があるパッケージでは完全にブロックされるため、公開されたバージョンはすべて永続的なものとして扱ってください。
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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