SEC Filings and Earnings Call

Le serveur MCP fournit des workflows complets pour les dépôts SEC et les transcriptions d'appels de résultats, incluant la résolution de tickers, la récupération de documents, l'OCR, l'embedding, la découverte de ressources sur disque et la recherche sémantique, exposés via MCP et alimentés par les mêmes backends olmOCR et d'embedding que les backends vLLM.

GitHub
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Documentation

Finance Data MCP

A Python-first toolkit for SEC filing ingestion, OCR-to-Markdown conversion, transcript collection, and retrieval across hybrid retrieval (dense + BM25) with reranking.

What this project does

  • Downloads SEC filings and stores filing metadata.
  • Converts filing PDFs to Markdown via olmOCR.
  • Chunks and indexes filings/transcripts in Chroma.
  • Supports:
    • Hybrid search (dense + BM25 reciprocal-rank-fusion + reranker).
  • Exposes workflows through:
    • FastAPI (server.py).
    • MCP server (mcp_server.py).

Repository layout

  • finance_data/filings/: SEC download + helpers.
  • finance_data/ocr/: olmOCR pipeline.
  • finance_data/dataloader/: chunking, Chroma indexing, semantic + BM25 retrieval.
  • finance_data/earnings_transcripts/: transcript fetch + persistence.
  • finance_data/server_api/: API request/response models + batch helpers.
  • server.py: FastAPI app.
  • mcp_server.py: MCP entrypoint.
  • docs/: setup and operations docs.

Quick start

1) Install dependencies

uv sync

For OCR/embedding flows:

uv sync --group ocr-md

For MCP workflows:

uv sync --group ocr-md --group mcp

2) Configure environment

Use .env or environment variables. Common settings:

  • SEC_API_ORGANIZATION, SEC_API_EMAIL
  • OLMOCR_SERVER, OLMOCR_MODEL, OLMOCR_WORKSPACE
  • EMBEDDING_SERVER, EMBEDDING_MODEL
  • CHROMA_PERSIST_DIR
  • MCP_HOST, MCP_PORT, MCP_NGROK_ALLOWED_HOSTS

See finance_data/settings.py for defaults.

3) Run services

Start model servers:

make vllm-olmocr-serve
make vllm-embd-serve
make vllm-reranker-serve

Start API:

make start-server

Start MCP:

uv run --group ocr-md --group mcp python mcp_server.py

Search capabilities

SEC filings API

  • Hybrid (dense + BM25 + reranker): POST /vector_store/search_sec_filings

Transcript API

  • Hybrid (dense + BM25 + reranker): POST /vector_store/search_transcripts

MCP tools

  • Hybrid: search_sec_filings_tool, search_transcripts_tool

Core workflows

SEC filing → Markdown

uv run python -m finance_data.filings.sec_data --ticker AMZN --year 2025
uv run python -m finance_data.ocr.olmocr_pipeline --pdf-dir sec_data/AMZN-2025

Embed and search filings (API)

curl -s -X POST "http://127.0.0.1:8081/vector_store/embed_sec_filings" \
  -H "Content-Type: application/json" \
  -d '{"ticker":"AMZN","year":"2025","filing_type":"10-K","force":false}'

curl -s -X POST "http://127.0.0.1:8081/vector_store/search_sec_filings" \
  -H "Content-Type: application/json" \
  -d '{"ticker":"AMZN","year":"2025","filing_type":"10-K","query":"operating income margin","top_k":5}'

Earnings transcripts

Fetch quarterly transcripts:

uv run python -m finance_data.earnings_transcripts.transcripts AMZN 2025

Embed + hybrid search transcripts:

curl -s -X POST "http://127.0.0.1:8081/vector_store/embed_transcripts" \
  -H "Content-Type: application/json" \
  -d '{"ticker":"AMZN","year":"2025","force":false}'

curl -s -X POST "http://127.0.0.1:8081/vector_store/search_transcripts" \
  -H "Content-Type: application/json" \
  -d '{"ticker":"AMZN","year":"2025","query":"AWS revenue growth","top_k":5}'

Docker

Use Makefile wrappers:

make docker-build
make docker-start

Stop/remove by API port:

make docker-stop
make docker-remove

Documentation

  • docs/README.md
  • docs/setup-and-operations.md