filemaker-mcp
Enables AI assistants to query, analyze, and discover FileMaker database schemas via OData v4, with pandas-powered analytics for efficient data summaries.
README
filemaker-mcp
Connect Claude (or any MCP client) to a FileMaker database — read-only queries, schema discovery, and pandas-powered analytics.
What It Does
filemaker-mcp is an MCP server that gives AI assistants live access to your FileMaker data via OData v4. Load it in Claude Desktop or Claude Code and ask questions about your data in plain English.
Tools provided:
fm_query_records— Search and filter records with OData expressionsfm_get_record— Fetch a single record by primary keyfm_count_records— Count records with optional filtersfm_list_tables— List available tablesfm_get_schema— Discover field names, types, and keysfm_load_dataset— Pull records into memory for analyticsfm_analyze— Run groupby/sum/count/mean/min/max on loaded datafm_list_datasets— See what datasets are loaded
Quick Start
Prerequisites
- Python 3.12+
- uv package manager
- FileMaker Server with OData v4 enabled
- An FM account with
fmodataextended privilege
Install
git clone https://github.com/nietsneuah/filemaker-mcp.git
cd filemaker-mcp
cp .env.example .env
# Edit .env with your FileMaker server details
uv sync
Configure Claude Desktop
Add to your Claude Desktop MCP config
(~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"filemaker": {
"command": "uv",
"args": ["run", "--directory", "/path/to/filemaker-mcp", "filemaker-mcp"],
"env": {
"FM_HOST": "your-server.example.com",
"FM_DATABASE": "your_database",
"FM_USERNAME": "mcp_agent",
"FM_PASSWORD": "your_password"
}
}
}
}
Run
uv run filemaker-mcp
Schema Discovery
On startup, the server auto-discovers your tables from the OData service
document. For richer schema (field types, primary keys, tiers), install the
optional GetTableDDL FileMaker script — see docs/FM_ACCOUNT_SETUP.md.
Analytics
For reports and summaries, use the analytics tools instead of raw queries:
fm_load_dataset— Fetch records into a pandas DataFrame (auto-paginates)fm_analyze— Run aggregations instantly (no additional FM round trips)
This returns ~200 tokens instead of ~400K for raw records — much more efficient for dashboards and trend analysis.
Documentation
- Architecture — System design and security model
- FM Account Setup — FileMaker privilege configuration
- Tool Reference — Detailed parameter docs
License
GPL-3.0 — see LICENSE
Author
Doug Hauenstein / FM Rug Software
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