Financial Intelligence Server
БесплатноНе проверенA production-grade MCP server that provides financial ML tools including RAG search, anomaly detection, contract summarization, vendor graph analysis, and model
Описание
A production-grade MCP server that provides financial ML tools including RAG search, anomaly detection, contract summarization, vendor graph analysis, and model drift monitoring using entirely free, open-source components.
README
A production-grade Model Context Protocol (MCP) server that exposes financial ML tools to any MCP-compatible AI client (Claude Desktop, Cursor, etc.).
Built entirely with free, open-source tools — no paid APIs required.
🏗️ Architecture
┌─────────────────────────────────────────────────────────────────┐
│ MCP-Compatible Client │
│ (Claude Desktop / Cursor / Any LLM) │
└─────────────────────┬───────────────────────────────────────────┘
│ MCP Protocol (stdio)
▼
┌─────────────────────────────────────────────────────────────────┐
│ financial-mcp-server │
│ server.py │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Tool Router │ │
│ └──┬────────────┬────────────┬───────────┬─────────────────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ ┌───────┐ ┌─────────┐ ┌────────┐ ┌────────┐ ┌─────────┐ │
│ │ RAG │ │Anomaly │ │Contract│ │Vendor │ │ Drift │ │
│ │Search │ │Detect. │ │Summary │ │ Graph │ │Monitor │ │
│ └───┬───┘ └────┬────┘ └───┬────┘ └───┬────┘ └────┬────┘ │
└─────┼───────────┼───────────┼───────────┼────────────┼─────────┘
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
FAISS + sklearn Ollama Neo4j / Evidently
sentence- IsolationF. (local LLM) NetworkX AI / KS
transformers (free) (free) (free) Test (free)
🛠️ Tools
| Tool | Description | Free Stack Used |
|---|---|---|
search_financial_docs |
RAG over financial PDFs/docs | FAISS + sentence-transformers + Ollama |
analyze_expense_pattern |
Anomaly detection on transactions | scikit-learn IsolationForest |
summarize_contract |
Extract key clauses from contracts | Ollama Mistral + regex fallback |
get_vendor_relationships |
Query vendor knowledge graph | Neo4j Community + NetworkX fallback |
monitor_model_drift |
Detect data/feature drift in models | Evidently AI + scipy KS test fallback |
💸 100% Free Stack
| Component | Tool | Cost |
|---|---|---|
| LLM | Ollama + Mistral 7B (local) | FREE |
| Embeddings | sentence-transformers all-MiniLM-L6-v2 | FREE |
| Vector Store | FAISS (Meta) | FREE |
| Anomaly Detection | scikit-learn IsolationForest | FREE |
| Graph Database | Neo4j Community Edition | FREE |
| Drift Monitoring | Evidently AI open-source | FREE |
| MCP Framework | Anthropic MCP Python SDK | FREE |
🚀 Quick Start
1. Clone & Install
git clone https://github.com/YOUR_USERNAME/financial-mcp-server.git
cd financial-mcp-server
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
2. Start Ollama (free local LLM)
# Install Ollama from https://ollama.com (free)
ollama serve
ollama pull mistral # ~4GB download, one time
3. Add Your Documents (optional)
# Drop any .txt, .md, or .pdf files into:
data/sample_docs/
# Index them (auto-happens on first run, or manually):
python -c "from tools.rag_search import _load_or_build_index; _load_or_build_index()"
4. Run the MCP Server
python server.py
5. Connect to Claude Desktop
Edit your Claude Desktop config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"financial-intelligence": {
"command": "python",
"args": ["/absolute/path/to/financial-mcp-server/server.py"],
"env": {
"PYTHONPATH": "/absolute/path/to/financial-mcp-server"
}
}
}
}
Restart Claude Desktop. You'll see the 🔧 tools icon appear.
🐳 Docker Setup (with Neo4j)
# Start Neo4j Community Edition + MCP server
docker-compose up -d
# Check logs
docker-compose logs -f mcp-server
Neo4j browser available at: http://localhost:7474
🧪 Run Tests
pip install pytest
pytest tests/ -v
💬 Example Claude Desktop Conversations
Once connected, ask Claude:
"Search my financial documents for information about payment terms with TechVendor Solutions"
"Analyze these transactions for anomalies: [paste JSON list]"
"Summarize this contract and flag any risky clauses: [paste contract text]"
"What vendors are related to TechVendor Solutions and what's their risk level?"
"Check if the fraud_detector model has drifted and whether it needs retraining"
📁 Project Structure
financial-mcp-server/
├── server.py # MCP server — tool registry & routing
├── tools/
│ ├── rag_search.py # search_financial_docs
│ ├── anomaly_detection.py # analyze_expense_pattern
│ ├── contract_summary.py # summarize_contract
│ ├── vendor_graph.py # get_vendor_relationships
│ └── drift_monitor.py # monitor_model_drift
├── data/
│ └── sample_docs/ # Drop your financial docs here
├── tests/
│ └── test_tools.py # pytest test suite
├── .github/workflows/
│ └── ci.yml # GitHub Actions CI
├── Dockerfile
├── docker-compose.yml # Includes Neo4j Community Edition
├── requirements.txt
└── claude_desktop_config.json # Copy into Claude Desktop config
🔗 Related Projects
- Financial-RAG-Pipeline — Standalone RAG system (precursor to this project)
📄 License
MIT License — free to use, modify, and distribute.
Установка Financial Intelligence Server
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/BharathiDonku7/MCP-ServerFAQ
Financial Intelligence Server MCP бесплатный?
Да, Financial Intelligence Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Financial Intelligence Server?
Нет, Financial Intelligence Server работает без API-ключей и переменных окружения.
Financial Intelligence Server — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Financial Intelligence Server в Claude Desktop, Claude Code или Cursor?
Открой Financial Intelligence Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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