Claude やその他の AI アシスタントを通じてLunchmoney の取引や予算を操作できるモデル コンテキスト プロトコル (MCP) サーバー。
このツールを使用すると、Lunchmoney の財務データを Claude AI に接続できるため、自然な会話を通じて支出について質問したり、予算を分析したり、財務に関する洞察を得たりすることができます。
Related MCP server: Lunch Money MCP Server
このサーバーは、主に 4 つのツールを提供します。
get-recent-transactions : 過去 N 日間の最近の取引を表示します
search-transactions : 受取人名またはメモのキーワードで取引を検索します
get-category-spending : 特定のカテゴリの支出を分析する
get-budget-summary : 支出、残高、定期的な項目などの詳細な予算情報を取得します。
重要： MCPは、プライバシーの境界を維持しながら、ClaudeがLunchmoneyデータを操作するための構造化された方法を提供します。以下の点にご注意ください。
クロード（ホスト）は、ローカルMCPサーバーに接続するクライアントを作成します。
Lunchmoney APIトークンはローカルマシンに残ります
MCPサーバーはローカルで実行され、LunchmoneyのAPIからデータを取得します。
Lunchmoneyデータへのアクセスには、各リクエストの承認が求められます。
財務について質問すると、クロードはMCPサーバーから特定の情報を要求します。
MCPサーバーはリクエストをローカルで処理し、関連する結果のみを返します。
クロードはあなたの財務データやAPIトークンに直接アクセスすることはできません。
要求された特定の情報（取引概要や予算状況など）のみがクロードと共有されます。
Anthropicのデータ保持ポリシーは、会話の一部であるこれらの要約結果に適用されます。
各サーバー接続は分離されており、明確なセキュリティ境界が維持されています。
MCP の詳細については、次のドキュメントをご覧ください: https://modelcontextprotocol.io/introduction
Claude の公式ドキュメントもご覧ください: https://modelcontextprotocol.io/quickstart/user
Node.js は、コンピューター上 (Web ブラウザー外部) で JavaScript コードを実行できるソフトウェア プラットフォームです。
Node.js をインストールするには:
Windows/Mac :公式Node.jsウェブサイトからインストーラーをダウンロードして実行します。
Homebrew 搭載 Mac : ターミナルでbrew install node実行する
Linux : パッケージ マネージャーを使用します (例: Ubuntu の場合はsudo apt install nodejs )
Node.js をコンピューターにインストールすると、何もダウンロードせずにサーバーを直接実行できます。
Lunchmoney開発者設定からLunchmoney APIトークンを取得します
クロードデスクトップを開く
設定→開発者→ Edit Config移動します
次の構成を追加します。
{
"mcpServers": {
"lunchmoney": {
"command": "npx",
"args": ["-y", "lunchmoney-mcp-server"],
"env": {
"LUNCHMONEY_TOKEN": "your_token_here"
}
}
}
}your_token_here実際の Lunchmoney API トークンに置き換えます。
**重要な注意:**設定を変更した後、変更を有効にするには Claude Desktop を再起動する必要がある場合があります。
Claude Desktop で設定が完了すると、次のような質問をすることができます。
「過去1週間の最近の取引を表示してください」
「Amazonでのすべての取引を検索」
「先月レストランにいくら使った？」
「経費としてタグ付けされた取引を検索する」
「今月の予算の概要を見せてください」
「2024年1月から3月までの予算状況はどうなっていますか？」
「食費の予算はあといくら残っているの？」
「予算を超過しているカテゴリを表示」
モデルコンテキストプロトコル（MCP）は、アプリケーションが大規模言語モデル（LLM）にコンテキストを提供する方法を標準化するオープンプロトコルです。MCPはAIアプリケーション用のUSB-Cポートのようなもので、AIモデルをさまざまなデータソースやツールに接続するための標準化された方法を提供します。
MCP の主な利点は次のとおりです。
LLMにデータと機能を公開するための標準化された方法
人間参加型セキュリティ（すべてのアクションにはユーザーの承認が必要）
構築済みの統合エコシステムの拡大
複数のAIモデルおよびアプリケーションで動作します
Claude によると、MCP サーバーに接続できないそうです。
Claudeの開発者設定の構成が正しいことを確認してください
設定を変更した後、Claude Desktopを再起動してみてください
Lunchmoney APIトークンが有効であることを確認してください
Claude は Lunchmoney コマンドを認識しません:
クロードで新しい会話を始める
クエリで Lunchmoney を明示的に言及してみてください（例: 「最近の Lunchmoney 取引を表示」）
予算データでは、日付の境界として月を使用する必要があります（例：2024-01-01 から 2024-01-31）
取引には任意の日付範囲を使用できます
すべての金銭的価値は元の通貨で返されます
マサチューセッツ工科大学
貢献を歓迎します！お気軽にプルリクエストを送信してください。
Get budget summary for a specific time period
| Name | Required | Description | Default |
|---|---|---|---|
| start_date | Yes | Start date (YYYY-MM-DD, should be start of month) | |
| end_date | Yes | End date (YYYY-MM-DD, should be end of month) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves a 'budget summary' but doesn't explain what that summary includes (e.g., categories, totals, trends), whether it's read-only (implied but not explicit), or any limitations like rate limits or authentication needs. This leaves significant gaps for an agent to understand the tool's 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?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the 'budget summary' output contains, how it's structured, or any behavioral traits like error handling. For a tool with two parameters and no structured output documentation, this leaves the agent with insufficient context to use it effectively.
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?
The schema description coverage is 100%, with both parameters ('start_date' and 'end_date') fully documented in the schema. The description adds minimal value by mentioning 'time period,' which aligns with the schema but doesn't provide additional context like format examples or edge cases beyond what's already specified.
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 action ('Get') and resource ('budget summary') with a specific scope ('for a specific time period'), which makes the purpose understandable. However, it doesn't differentiate this tool from its siblings like 'get-category-spending' or 'get-recent-transactions', which likely provide related but different budget/transaction data.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get-category-spending' or 'get-recent-transactions', nor does it specify prerequisites, exclusions, or appropriate contexts for usage beyond the time period requirement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get spending in a category
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Category name | |
| days | No | Number of days to look back |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get spending' implies a read operation, but it doesn't specify if this requires authentication, has rate limits, returns historical or real-time data, or what format the spending data is in. This is a significant gap for a tool with no annotation coverage.
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, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly.
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's complexity (a read operation with two parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'spending' entails (e.g., total amount, list of transactions), how results are returned, or any behavioral traits, leaving the agent with insufficient context.
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?
The description doesn't add any parameter semantics beyond what the schema provides. With 100% schema description coverage, the schema already documents 'category' as 'Category name' and 'days' as 'Number of days to look back' with a default of 30. The baseline score of 3 is appropriate since the schema does the heavy lifting.
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 'Get spending in a category' clearly states the verb 'Get' and resource 'spending in a category', making the purpose understandable. However, it doesn't distinguish this tool from siblings like 'get-budget-summary' or 'get-recent-transactions' which might also involve spending data, so it lacks sibling differentiation.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'search-transactions' for broader queries or 'get-budget-summary' for aggregated data, leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get recent transactions
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to look back | |
| limit | No | Maximum number of transactions to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Get recent transactions' implies a read operation but reveals nothing about permissions, rate limits, pagination, error conditions, or what 'recent' means contextually. For a tool with zero annotation coverage, this leaves critical behavioral traits undocumented.
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 extremely concise at three words with zero wasted text. It's front-loaded and efficiently communicates the core function without unnecessary elaboration. While under-specified, it earns full marks for brevity and structure.
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's moderate complexity (2 parameters, no output schema, no annotations), the description is incomplete. It fails to explain what 'recent' means, how results are ordered, what data fields are returned, or how it differs from sibling tools. The agent lacks sufficient context to use this tool effectively without trial and error.
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 both parameters ('days' and 'limit') well-documented in the schema. The description adds no parameter semantics beyond what the schema already provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 'Get recent transactions' is a tautology that essentially restates the tool name without adding meaningful differentiation. It specifies the verb 'Get' and resource 'transactions' but lacks specificity about scope or how it differs from sibling tools like 'search-transactions'. This provides minimal value beyond the name itself.
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 provides no guidance on when to use this tool versus alternatives like 'search-transactions' or 'get-category-spending'. There's no mention of context, prerequisites, or exclusions. The agent must infer usage from the name alone, which is insufficient for informed tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search transactions by keyword
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Search term to look for | |
| days | No | Number of days to look back | |
| limit | No | Maximum number of transactions to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'search' implies a read operation, the description doesn't address important behavioral aspects like whether this requires authentication, what happens with no results, whether results are paginated, or any rate limits. For a search tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 extremely concise at just three words, with zero wasted language. It's front-loaded with the essential information (search transactions) and specifies the mechanism (by keyword) efficiently. Every word serves a clear purpose in this minimal description.
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?
For a search tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what constitutes a 'transaction' in this context, what fields are searched, the format of results, or how the search algorithm works. The agent would need to guess about the tool's behavior and output based on minimal information.
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?
The description mentions searching 'by keyword' which aligns with one of the three parameters. However, with 100% schema description coverage, all parameters are already well-documented in the schema itself. The description adds minimal value beyond what's in the structured schema, meeting the baseline expectation when schema coverage is complete.
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's purpose with a specific verb ('search') and resource ('transactions'), and specifies the search mechanism ('by keyword'). However, it doesn't differentiate this tool from its sibling 'get-recent-transactions' which also deals with transactions, leaving some ambiguity about when to use one versus the other.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tools (get-budget-summary, get-category-spending, get-recent-transactions) or explain when keyword searching is preferable to other transaction retrieval methods. The agent receives no contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get-budget-summaryget-category-spendingget-recent-transactionssearch-transactionsScored across 4 tools
Each tool has a clearly distinct purpose: get-budget-summary targets budget overviews, get-category-spending focuses on category-level data, get-recent-transactions retrieves chronological transaction lists, and search-transactions enables keyword-based queries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb-noun pattern with hyphens (e.g., get-budget-summary, search-transactions). The naming is uniform across all four tools, using 'get' for retrieval operations and 'search' for querying, which enhances predictability and readability.
With 4 tools, the count is reasonable for a personal finance domain, covering key operations like summaries, category analysis, and transaction retrieval. However, it feels slightly thin as it lacks tools for creating or updating data (e.g., adding transactions or categories), which might be expected in a full-featured finance server.
The tool set covers read-only operations well, including summaries, category spending, and transaction searches, but there are notable gaps. It lacks CRUD capabilities for transactions, categories, or budgets (e.g., create, update, delete), which limits agents to viewing data without modifying it, potentially causing workflow dead ends.
The Ramp MCP server enables users to securely connect Ramp with AI assistants like ChatGPT and Claude to query financial data and take actions using natural language. It transforms Ramp's developer API into a SQL interface that LLMs can query, allowing admins to analyze spend trends, identify cost savings, and run complex SQL analyses on comprehensive datasets (transactions, purchase orders, vendors, users), while all users can manage cards, view transactions, request reimbursements, and get expense policy answers.
An MCP server that provides read access to your cloud storage providers, bank accounts and more.
Query your real net worth, spending, transactions, budgets and portfolio from any MCP client.
Personal finance for AI agents — onboard, import statements, categorize & budget over MCP.
