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Capture daily expenses and classify them through an MCP finance server.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-finance" yet — see the docs or source repo.
Please record an expense: I spent 38 CNY on lunch today at a convenience store, and classify it into an appropriate spending category.
Returns the saved expense entry with a suggested or automatic category.
Record and classify these expenses from today: breakfast 12 CNY, subway 4 CNY, coffee 26 CNY, dinner 58 CNY.
Outputs multiple structured expense records, each with amount and category.
I have an expense 'supermarket shopping, 126 CNY'. Use intelligent classification to decide the category and record it.
Returns the recorded expense along with the category inferred by the model.
Individuals can use this MCP tool to quickly log daily spending and organize expenses by category. This makes it easier to track spending habits over time.
Developers can connect this Node.js and TypeScript MCP server to MCP-compatible AI workflows, allowing an assistant to log and classify expenses.
When spending data needs to be stored in a database, this tool uses Postgres to save expense records and can optionally improve organization with LLM-based classification.
It is an MCP server for capturing and classifying daily expenses. The description also mentions optional LLM-based classification.
The provided information says it is built with Node.js and TypeScript and uses Postgres for storage. For more implementation details, see the source repository.
From the description, Postgres is required. If you want to use LLM-based classification, corresponding model configuration may also be needed; see the source repository for specifics.
Track, manage, and summarize personal expenses with MongoDB-backed MCP tools.
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Track, list, and summarize expenses by category with local SQLite storage.
Log expenses locally and review budget summaries through MCP-compatible AI clients.
Track, categorize, and analyze expenses with fast summaries and lookups.