regintel-mcp

Regulatory intelligence from FDA regulations.gov dockets: who commented, what they asked for, and what changed between the draft and final guidance.

Black Swan Causal Labs · blackswancausallabs.com

Retrieval lives in the MCP server. Judgment lives in skills. Humans sit at the checkpoints.

Install

Python 3.11 or later.

git clone https://github.com/Black-Swan-Causal-Labs/regintel-mcp.git
cd regintel-mcp
python3 -m venv .venv && .venv/bin/pip install -e .
cp .env.example .env    # then paste your key into .env

API key: get a free key at https://open.gsa.gov/api/regulationsgov/ and put it in .env as REGULATIONS_GOV_API_KEY=.... .env is gitignored. The key is sent only as the X-Api-Key header and never appears in logs, the cache or any output. DEMO_KEY is fine for single lookups but too rate-limited for a full corpus pull. Personal keys allow 1,000 requests/hour.

Register with an MCP client. MCP is an open standard, so the server works with any compatible assistant (Claude, OpenAI Codex, others). For Claude Code, from the repo directory:

claude mcp add regintel-mcp -- "$(pwd)/.venv/bin/python" -m regintel_mcp

When installed as a Claude plugin, .mcp.json starts the server from the plugin root.

Tools

Every tool reads the local SQLite cache (data/cache.sqlite) first. It only touches the network on a cache miss or when called with refresh=true.

For long pulls outside a chat: .venv/bin/python -m regintel_mcp build-corpus FDA-2020-D-2307.

How the diff works: the text layer is extracted with pdfplumber. Running headers, draft line numbers, footnotes and the table of contents are removed. Sections are split on the guidance's own Roman, letter and number headings, then matched draft→final by heading (parent-qualified first) and then by heading plus text similarity. Paragraphs are diffed within each matched section, and paragraphs that moved to a different section are labelled. Everything is deterministic: there are no model calls in the server.

API behaviour verified against the live service is recorded in docs/api-notes.md.

Docket Lens (the explorer)

Docket Lens (explorer/template.html) is a single page that reads one analysis.json and shows three linked views: who spoke, what they asked for (a themes × submitter-type heatmap

... View full README on GitHub