SEC Intelligence

🔎 SEC Intelligence MCP — Document-Grounded Financial AI

Ask Claude Desktop real questions about SEC filings — 10-Ks, 10-Qs, 8-Ks — and get answers quoted directly from the actual filing text, with a citation to the exact section (and page, where available) on every claim. Not a summary from training data. Not a guess.

Try it now: uvx sec-intelligence-mcp — see Quick install below.

Why this is different

Most finance-related MCP servers are data-API wrappers: they hand back numbers (revenue, EPS, price) from a database. None of the ones we surveyed read the actual filing documents, so none can answer a question that requires understanding what a company's management actually said — e.g. "how did NVIDIA's management explain the datacenter revenue surge?" or "did Amazon's forward guidance tone change between quarters?"

This server retrieves and quotes the real filing text with a citation on every claim, and its answer-generation prompt explicitly refuses to use prior/general knowledge when the retrieved passages don't contain the answer — verified live: asking about NVIDIA's non-existent "Mars operations" correctly returns "not present in the filing" rather than an invented answer. It also ships an automated RAGAS evaluation harness (see Evaluation results) that measures this claim on 50 real questions rather than just asserting it.

🚀 Quick install

Published on PyPI: https://pypi.org/project/sec-intelligence-mcp/. No clone, no build step — uv fetches and runs it on demand:

uvx sec-intelligence-mcp

That's the whole install. Two more things and you're ready to use it in Claude Desktop:

1. Get your free API keys

All four take under 5 minutes total, no credit card anywhere.

Don't have Docker or want to skip signing up for Qdrant? See the note in Getting Qdrant running below.

2. Connect Claude Desktop

Add this to your claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json, macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{  "mcpServers": {    "sec-intelligence-mcp": {      "command": "uvx",      "args": ["sec-intelligence-mcp"],      "env": {        "GEMINI_API_KEY": "your-key",        "QDRANT_URL": "http://localhost:6333",        "LANGFUSE_SECRET_KEY": "your-key",        "LANGFUSE_PUBLIC_KEY": "your-key"      }    }  }}

Restart Claude Desktop, open the tools list (hammer icon), and confirm sec-intelligence-mcp appears with a ping tool. That confirms the connection works before you rely on it for a real question.

3. Try it out

Every session starts by ingesting the company you want to ask about — that's what indexes its filings so they can be searched. Then just ask in plain English:

You: Ingest NVIDIA's last 2 annual filings

Claude (calls ingest_company_filings): Indexed 2 filings for NVIDIA — 312 chunks from FY2024's 10-K, 287 from FY2023's.

You: What did they say about risks from export controls?

Claude (calls analyze_filing): NVIDIA's FY2024 10-K identifies export control regulations as a primary risk: "The U.S. government has imposed, and may in the future impose, controls on the export of our products... restrictions to China, Hong Kong, and Russia have materially impacted our revenue." — [Item 1A — Risk Factors]

You: How does that compare to AMD?

Claude (calls compare_companies): [grounded side-by-side answer, cited to each company's own filing]

No prompt engineering, no special syntax — Claude picks the right tool automatically based on what you ask.

🧰 Available tools

🛠️ Tech stack

Open source and free-tier first — no paid API is required to run this end to end.

🏗️ Architecture

flowchart LR    A[Claude Desktop / MCP client] -->|MCP tool calls| B[sec-intelligence-mcp server]    B --> C[SEC EDGAR API]    B --> D[Qdrant<br/>vector search]    B --> E[Gemini<br/>answer generation]    B --> F[LangFuse<br/>tracing + eval scores]    C -->|filings| B    D -->|cited passages| B    E -->|grounded answer| B

📊 Evaluation results

Measured with RAGAS on 50 hand-verified question/ground-truth pairs across 5 companies (full methodology and raw results in eval/README.md):

CI's eval-gate fails any PR to main that drops faithfulness below 0.75 on a real, live-ingested subset of these questions — see .github/workflows/ci.yml.

LangFuse dashboard

A real trace of analyze_filing answering "What risks does NVIDIA face from export controls?" — the span tree shows retrieval and embedding nested under the tool call, alongside the LLM generation, with a faithfulness: 1.00 score attached automatically:

[LangFuse trace showing analyze_filing's span tree and a 1.00 faithfulness score]

👩‍💻 For developers

Want to run from source, contribute, or self-host instead of using the published package?

Getting Qdrant running

The simplest path is Docker: docker run -p 6333:6333 qdrant/qdrant. No Docker? Use a free Qdrant Cloud cluster instead and set QDRANT_API_KEY too.

Run from a local clone

1. Install uv.
2. Clone the repo and install dependencies:
   git clone https://github.com/jahanv01/sec-intelligence-mcp.gitcd sec-intelligence-mcpuv sync

3. Copy .env.example to .env and fill in the keys from the table above.
4. Start Qdrant locally:
   docker compose up -d qdrant

5. Run the server directly:
   uv run python src/server.py

   Or with the MCP Inspector (dev UI, requires Node.js):
   uv run mcp dev src/server.py

For Claude Desktop, point it at your clone instead of the published package:

{  "mcpServers": {    "sec-intelligence-mcp": {      "command": "uv",      "args": [        "--directory",        "C:\\ABSOLUTE\\PATH\\TO\\sec-intelligence-mcp",        "run",        "python",        "src/server.py"      ]    }  }}

src/config.py fails fast at import time (raises RuntimeError) if any required key is missing.

Running via Docker

docker compose up -d builds the server image and starts it alongside Qdrant. The app service reads secrets from your local .env via env_file, and QDRANT_URL is overridden to http://qdrant:6333 (the in-network service name) since localhost inside the container would not reach the qdrant container.

Testing locally

uv run python -c "import mcp"                    # SDK installed correctlyuv run python scripts/test_server_stdio.py        # server responds over stdio (ping -> pong)docker compose up -d qdrantuv run python scripts/test_qdrant.py               # Qdrant round-trip works
# EDGAR data layer (each hits the real EDGAR API)uv run python scripts/test_edgar_lookup.py         # ticker -> CIK, DuckDB-cacheduv run python scripts/test_edgar_filings.py        # recent 10-K filings for a tickeruv run python scripts/test_edgar_parser.py         # download + clean a real filinguv run python scripts/test_edgar_sections.py       # section detection + metadata
# Embedding & retrieval pipeline (real model + real Qdrant)uv run python scripts/test_chunker.py              # section/paragraph chunkinguv run python scripts/test_encoder.py              # E5 embedding shape/latencyuv run python scripts/test_ingest.py               # chunk -> embed -> upsert to Qdrantuv run python scripts/test_search.py               # semantic search with citations
# MCP tools (real pipeline + real Gemini calls)uv run python scripts/test_tool_ingest_company_filings.pyuv run python scripts/test_tool_search_filings.pyuv run python scripts/test_tool_analyze_filing.pyuv run python scripts/test_tool_get_filing_summary.py
# Full mocked suite (no network calls)uv run pytest tests/ -m "not integration"

Project structure

src/├── server.py         # MCP server entrypoint├── tools/             # One file per MCP tool├── edgar/             # SEC EDGAR fetching + parsing├── embeddings/        # Chunking + embedding pipeline├── retrieval/         # Qdrant client + search + hybrid + rerank├── evaluation/         # RAGAS eval pipeline└── config.py          # Env var loading (fail-fast)tests/                  # Unit/integration testsprompts/                # Prompt templates (.txt)data/                   # Gitignored local cache (DuckDB, filing PDFs, Qdrant storage)eval/                   # Test questions + ground truth answersscripts/                # One-off dev/test scripts

Deploy your own instance

The published PyPI package is enough for personal use via Claude Desktop — you only need this if you want a standalone, publicly reachable server (e.g. for a remote MCP client).

Deployed on an Oracle Cloud "Always Free" compute VM (Ampere A1, ARM) rather than Render or Hugging Face Spaces: both of those give the container an ephemeral filesystem (wiped on every restart/redeploy) and cap free-tier RAM at 512MB, which doesn't comfortably fit the embedding model (e5-base-v2, CPU-only, ~440MB loaded) alongside the rest of the process. A real Always Free VM has neither constraint — genuine persistent disk and up to 24GB RAM — so Qdrant runs locally via the same docker-compose.yml used for local dev, with no separate Qdrant Cloud account needed.

Setup (one-time):

1. Create an Always Free Ampere A1 compute instance (Ubuntu image) in the Oracle Cloud console, and note its public IP.
2. In the VCN's Security List (not just the instance's own firewall — both must allow it), add an ingress rule for TCP port 8000 (and 22 for SSH, usually already open).
3. SSH in, install Docker + the Compose plugin, then:
   git clone https://github.com/jahanv01/sec-intelligence-mcp.gitcd sec-intelligence-mcpcp .env.example .env   # fill in GEMINI_API_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEYecho "MCP_TRANSPORT=sse" >> .envsudo docker compose up -d --build

   QDRANT_URL doesn't need to be set in .env here — docker-compose.yml already overrides it to http://qdrant:6333, the in-network service name, for the app service.
4. Also open the instance's own firewall for the port (Ubuntu ships iptables/ufw rules that block it even after the Security List allows it):
   sudo iptables -I INPUT -p tcp --dport 8000 -j ACCEPTsudo netfilter-persistent save   # or: sudo ufw allow 8000/tcp

5. Confirm: curl http://<instance-public-ip>:8000/health returns ok.

Both services have restart: unless-stopped, so a VM reboot brings the whole stack back up without manual intervention. Plain HTTP (no TLS/domain) is used for now — fine for a demo, but a real production deployment would put Caddy or Nginx in front for HTTPS.

Alternative: Hugging Face Spaces. Also possible via the Docker SDK, using Qdrant Cloud instead of a local container (Spaces storage is ephemeral on restart, unlike a real VM). huggingface.co → New Space → SDK: Docker → add GEMINI_API_KEY, QDRANT_URL, QDRANT_API_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY as secrets and MCP_TRANSPORT=sse as a variable in Space Settings, then git push this repo to the Space's git remote.

🤝 Contributing

Issues and PRs welcome. See docs/edgar-api.md for EDGAR API quirks (rate limits, required User-Agent header) and eval/README.md before changing anything in the retrieval pipeline — a PR that regresses RAGAS faithfulness below 0.75 will fail CI's eval-gate job.