redflag-mcp
An MCP server that provides queryable access to Anti-Money Laundering (AML) red flag knowledge from regulatory documents. It enables compliance officers to ask natural-language questions and receive relevant, sourced red flags from a local vector database.
README
redflag-mcp
MCP server exposing AML red flag knowledge as queryable tools. Compliance officers ask natural-language questions; the server returns relevant, sourced red flags from a local vector database.
Overview
Three distinct workflows:
- Extraction — pull AML red flags out of PDFs or web pages using an LLM and save them as YAML
- Ingestion — embed the YAML files and load them into the local vector database
- Query — MCP server answers semantic search queries against that database
Extraction Pipeline
scripts/extract.py takes a regulatory document (PDF file or URL), sends its text to an OpenAI model, and writes a structured YAML file into data/source/. Each extracted entry includes a source_url linking back to the original document.
Prerequisites
uv sync
export OPENAI_API_KEY=sk-...
Adding PDFs in bulk (recommended workflow)
Step-by-step:
- Add the source URL to
red_flag_sources/pdflinks.txt— one URL per line, in serial order. Line 1 → key001, line 2 →002, etc. - Download the PDF and save it to
red_flag_sources/pdf/namedNNN_short_descriptive_name.pdf, whereNNNmatches its line position inpdflinks.txt. - Regenerate the registry:
uv run python scripts/build_sources_registry.py - Run extraction:
uv run python scripts/extract.py --parallel
Key constraint: the
NNN_prefix in the filename must match the line number inpdflinks.txt. Line 1 =001_*.pdf, line 2 =002_*.pdf, etc. This is how the extractor links each PDF to its public source URL.
PDFs are stored in red_flag_sources/pdf/ and must be named with a zero-padded serial prefix:
red_flag_sources/pdf/
001_fincen_alert_russian_sanctions_evasion.pdf
002_ffiec_bsa_aml_examination_manual.pdf
003_fatf_guidance_virtual_assets.pdf
Each serial number maps to a public URL for the source document. Maintain this mapping in red_flag_sources/pdflinks.txt — one URL per line, in serial order:
# FinCEN Russian Sanctions Evasion Alert
https://fincen.gov/sites/default/files/2022-06/Alert%20FIN-2022-Alert001_508C.pdf
# FFIEC BSA/AML Examination Manual
https://bsaaml.ffiec.gov/manual
# FATF Guidance on Virtual Assets
https://www.fatf-gafi.org/...
Blank lines and lines starting with # are ignored. After editing pdflinks.txt, regenerate sources.yaml:
uv run python scripts/build_sources_registry.py
Then run batch extraction:
uv run python scripts/extract.py --parallel
Only new (unprocessed) PDFs are extracted — previously processed sources are skipped automatically.
Batch extraction commands
# Sequential batch
uv run python scripts/extract.py
# Parallel batch (4 workers by default)
uv run python scripts/extract.py --parallel
# Parallel batch with custom worker count
uv run python scripts/extract.py --parallel 8
# Force re-extract everything
uv run python scripts/extract.py --force --parallel
# Process only PDFs in a serial range (e.g. 001 through 005)
uv run python scripts/extract.py --range 001-005
# Range + parallel
uv run python scripts/extract.py --range 001-005 --parallel
# Force re-extract a range
uv run python scripts/extract.py --force --range 001-005 --parallel
Note:
--rangeapplies only to numbered PDFs. Web URLs inWeblinks.mdare excluded when a range is active.
Single source (ad hoc)
# Extract from a local PDF
uv run python scripts/extract.py red_flag_sources/pdf/001_fincen_alert.pdf
# Extract from a URL
uv run python scripts/extract.py https://example.com/regulatory-guidance
# Re-extract a source that was already processed
uv run python scripts/extract.py --force red_flag_sources/pdf/001_fincen_alert.pdf
For single-source PDFs, add the URL to pdflinks.txt and run build_sources_registry.py first so the extractor can populate source_url in the output.
What it does
- Fetches the document — downloads the web page (strips nav/footer/scripts) or reads text from the PDF via pdfplumber
- Sends to OpenAI — prompts
gpt-4o-mini(override withOPENAI_EXTRACTION_MODEL) to extract every distinct AML red flag indicator as structured JSON - Validates — each returned flag is checked against the
RedFlagSourceschema; invalid entries are skipped with a warning - Writes YAML — saves to
data/source/<slug>.yaml, one entry per red flag - Updates the manifest — records the source in
data/source/.extracted_sources.yamlto prevent re-processing
Output schema
Each entry in the YAML file has the following fields:
| Field | Type | Required | Description |
|---|---|---|---|
id |
string | yes | Unique identifier, e.g. 001-fincen-alert-01 |
description |
string | yes | Standalone description of the red flag indicator |
source_url |
string | no | Public URL of the source document |
product_types |
list[string] | no | Financial products this applies to (e.g. depository, crypto, msb) |
regulatory_source |
string | no | Source document name or authority (e.g. FinCEN Alert FIN-2022-Alert001) |
risk_level |
string | no | high, medium, or low |
category |
string | no | AML typology (e.g. structuring, sanctions_evasion, shell_company) |
simulation_type |
string | no | Optional simulation complexity code (e.g. 1A, 2B) |
Deduplication
data/source/.extracted_sources.yaml tracks every processed source by its canonical path or URL. Sources already in the manifest are skipped in both batch and single-source mode. Use --force to re-extract a source regardless.
Ingestion
After extraction, embed the YAML files and load them into the vector database:
uv run python scripts/ingest.py
This reads all YAML files in data/source/, generates embeddings with nomic-embed-text-v1.5, and upserts records into LanceDB at data/vectors/. Run this before connecting the MCP server to Claude Desktop — the ~275 MB embedding model downloads on first use and connection timeouts will occur if it happens at server startup.
MCP Server
# Start server (stdio mode, for Claude Desktop / Claude Code)
uv run python -m redflag_mcp
# Start in MCP inspector
uv run mcp dev src/redflag_mcp/server.py
# Start as HTTP server (for OpenAI agents or other HTTP clients)
MCP_TRANSPORT=http MCP_HOST=0.0.0.0 MCP_PORT=8000 uv run python -m redflag_mcp
The server exposes three tools: search_red_flags, get_red_flag, and list_filters. It is fully offline after ingestion — no API keys required at query time.
Development
uv sync # Install dependencies
uv run pytest tests/ # Run tests
uv run ruff check src/ # Lint
uv run mypy src/ # Type check
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