Financial Intelligence MCP Server
Query vendor knowledge graph, explore vendor relationships and risk levels using Neo4j Community Edition.
Leverage local LLMs for contract summarization and RAG search, enabling private and cost-effective AI processing.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Financial Intelligence MCP Serversearch my financial documents for payment terms with TechVendor Solutions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
π¦ Financial Intelligence MCP Server
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 β no paid APIs required.
ποΈ Architecture
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β MCP-Compatible Client β
β (Claude Desktop / Cursor / Any LLM) β
βββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββββββββ
β MCP Protocol (stdio)
βΌ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β financial-mcp-server β
β server.py β
β ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β Tool Router β β
β ββββ¬βββββββββββββ¬βββββββββββββ¬ββββββββββββ¬ββββββββββββββββββ β
β β β β β β β
β βΌ βΌ βΌ βΌ βΌ β
β βββββββββ βββββββββββ ββββββββββ ββββββββββ βββββββββββ β
β β RAG β βAnomaly β βContractβ βVendor β β Drift β β
β βSearch β βDetect. β βSummary β β Graph β βMonitor β β
β βββββ¬ββββ ββββββ¬βββββ βββββ¬βββββ βββββ¬βββββ ββββββ¬βββββ β
βββββββΌββββββββββββΌββββββββββββΌββββββββββββΌβββββββββββββΌββββββββββ
β β β β β
βΌ βΌ βΌ βΌ βΌ
FAISS + sklearn Ollama Neo4j / Evidently
sentence- IsolationF. (local LLM) NetworkX AI / KS
transformers (free) (free) (free) Test (free)π οΈ Tools
Tool | Description | Free Stack Used |
| RAG over financial PDFs/docs | FAISS + sentence-transformers + Ollama |
| Anomaly detection on transactions | scikit-learn IsolationForest |
| Extract key clauses from contracts | Ollama Mistral + regex fallback |
| Query vendor knowledge graph | Neo4j Community + NetworkX fallback |
| Detect data/feature drift in models | Evidently AI + scipy KS test fallback |
πΈ 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 |
π 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.txt2. Start Ollama (free local LLM)
# Install Ollama from https://ollama.com (free)
ollama serve
ollama pull mistral # ~4GB download, one time3. 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.py5. Connect to Claude Desktop
Edit your Claude Desktop config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%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 π§ tools icon appear.
π³ Docker Setup (with Neo4j)
# Start Neo4j Community Edition + MCP server
docker-compose up -d
# Check logs
docker-compose logs -f mcp-serverNeo4j browser available at: http://localhost:7474
π§ͺ Run Tests
pip install pytest
pytest tests/ -v㪠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"
π Project Structure
financial-mcp-server/
βββ server.py # MCP server β tool registry & routing
βββ tools/
β βββ rag_search.py # search_financial_docs
β βββ anomaly_detection.py # analyze_expense_pattern
β βββ contract_summary.py # summarize_contract
β βββ vendor_graph.py # get_vendor_relationships
β βββ drift_monitor.py # monitor_model_drift
βββ data/
β βββ sample_docs/ # Drop your financial docs here
βββ tests/
β βββ test_tools.py # pytest test suite
βββ .github/workflows/
β βββ ci.yml # GitHub Actions CI
βββ Dockerfile
βββ docker-compose.yml # Includes Neo4j Community Edition
βββ requirements.txt
βββ claude_desktop_config.json # Copy into Claude Desktop configπ Related Projects
Financial-RAG-Pipeline β Standalone RAG system (precursor to this project)
π License
MIT License β free to use, modify, and distribute.
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