[MCP-001] Template architecture for an MCP server that gives any LLM structured access to your database. Layered design, schema mapping, read only guardrails, predefined queries. Part of AI for Fintec
View on GitHub ↗Fintech Data MCP Server AI for Fintech [MCP-001]
A template architecture for building an MCP server that gives any LLM structured access to your database. Point it at your tables, connect it to a client, and the model can query your data through well defined tools instead of guessing or hallucinating.
This is not a finished product. It is a starting point, built in layers so you can replace the database, the tables, and the tools without rewriting the server.
Swapping SQLite for Postgres touches only the connection. Adding a table touches only the mapping, the repository and the schemas. Replacing MCP with another protocol touches only the tools and the server. Nothing cascades.
An LLM has no access to your internal data. You can paste records into a prompt, but that does not scale, goes stale immediately, and puts sensitive information into a context window with no control.
MCP solves this by letting the model call tools you define. The model decides when it needs data, calls the tool, and receives a structured response. You control exactly which queries exist and what they return.
This project implements that pattern for a common fintech scenario: looking up a customer across three separate systems (credit score, preapproved limit, and risk profile) and returning a consolidated view.
The synthetic data is here so you can run it immediately. The architecture is what you keep.
Five steps from clone to a working LLM connection.
From the project README.
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