Announcing filemaker-mcp — Connect FileMaker Directly to Claude AI

I'm releasing a tool I built that I think the FileMaker community will find useful.

filemaker-mcp is a Model Context Protocol (MCP) server that gives Claude — Anthropic's AI assistant — direct, structured access to your FileMaker solution. Schema, data, analytics. No copy-pasting field lists, no explaining your data model in every prompt. Claude just has it.

github.com/nietsneuah/filemaker-mcp

Why I built it

I run a FileMaker-based ERP that manages operations for carpet and rug cleaning businesses. I was spending a lot of time giving Claude context about my schema every time I needed help with a script, a calculation, or a layout question. MCP solved that problem — it's a protocol that lets an LLM connect to external tools and data sources in a structured way. Once I connected Claude to my FileMaker Server, it could see every table, every field, every relationship. The development workflow improved immediately.

But then I realized the connection could do a lot more than just answer schema questions.

What it actually does

The server connects to FileMaker Server's OData v4 API — the same REST interface you may already be using for integrations. Once connected, Claude can query your data conversationally. You can ask things like:

• "What were total sales by month last year?"

• "Which driver had the highest average invoice in Q4?"

• "Show me all open orders past their promised date"

Claude forms the OData queries, pulls the data, and gives you the answer in natural language.

The analytics engine

This is where it gets interesting. The server is written in Python, which gave me access to pandas — a data analysis library widely used in finance and data science for time-series analysis and large dataset manipulation. If you've ever built a pivot table in Excel, pandas does the same thing but in memory, in milliseconds, and at scale.

I embedded pandas directly into the MCP server as an analytics sub-process. When Claude pulls data from FileMaker, it doesn't try to analyze millions of rows inside the AI model (which would be slow and expensive). Instead, the raw data gets loaded into a pandas DataFrame — a highly efficient in-memory data structure. The MCP server does the heavy computation locally, and only the summarized result gets sent back to Claude.

What this means in practice:

• A query like "Summarize sales by category by month and product for last year" returns in 10-15 seconds

• The raw data — potentially megabytes — never hits the AI model, so there's no token consumption cost

• DataFrames persist in memory for the entire session, so follow-up queries against the same data don't need to touch FileMaker Server again

• DataFrames can be sliced and saved as new datasets, building up an in-session analytical workspace

Requirements

• FileMaker Server v22+ — the OData v4 API is required. This won't work with earlier versions or FileMaker Cloud legacy.

• Python — the server is written in Python, a language with deep support for data analysis and broad adoption across the AI ecosystem.

• Claude Pro or Team plan — MCP server support requires a paid Anthropic plan.

Who is this for

• FileMaker developers who use Claude for development and want it to understand their solution without constant hand-holding

• Business operators running on FileMaker who want to ask natural language questions about their data

• Anyone exploring AI integration with FileMaker who wants a working, production-tested starting point

It's not a proof of concept — it's a tool I use every day.

The repo is public. Feedback, issues, and contributions are welcome.

— Doug Hauenstein