MCP Search Server

# MCP Financial Analyzer with Google Search This example demonstrates a financial analysis Agent application that uses an orchestrator with smart data verification to coordinate specialized agents for generating comprehensive financial reports on companies. https://github.com/user-attachments/assets/d6049e1b-1afc-4f5d-bebf-ed9aece9acfc ## How It Works 1. **Orchestrator**: Coordinates the entire workflow, managing the flow of data between agents and ensuring each step completes successfully 2. **Research Agent & Research Evaluator**: Work together in a feedback loop where the Research Agent collects data and the Research Evaluator assesses its quality 3. **EvaluatorOptimizer** (Research Quality Controller): Manages the feedback loop, evaluating outputs and directing the Research Agent to improve data until reaching EXCELLENT quality rating 4. **Analyst Agent**: Analyzes the verified data to identify key financial insights 5. **Report Writer**: Creates a professional markdown report saved to the filesystem This approach ensures high-quality reports by focusing on data verification before proceeding with analysis. The Research Agent and Research Evaluator iterate until the EvaluatorOptimizer determines the data meets quality requirements. ```plaintext ┌─────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │ │ │ │ │ │ Orchestrator│─────▶ Research Quality │──────▶ Research Agent ◀── ┐ │ │ │ Controller │ │ │ │ └─────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ │ │ │ │ │ ▼ │ │ ┌──────────────────┐ │ │ │ │ │ │ │Research Evaluator ─┘ │ │ Agent │ │ └──────────────────┘ │ ┌─────────────────┐ │ │ │ └────────────▶│ Analyst Agent │ │ │ │ │ └─────────────────┘ │ ┌─────────────────┐ │ │ │ └────────────▶│ Report Writer │ │ Agent │ └─────────────────┘ ``` ## `1` App set up First, clone the repo and navigate to the financial analyzer example: ```bash git clone https://github.com/lastmile-ai/mcp-agent.git cd mcp-agent/examples/usecases/mcp_financial_analyzer ``` Install `uv` (if you don’t have it): ```bash pip install uv ``` Sync `mcp-agent` project dependencies: ```bash uv sync ``` Install requirements specific to this example: ```bash uv pip install -r requirements.txt ``` Install the g-search-mcp server (from https://github.com/jae-jae/g-search-mcp): ```bash npm install -g g-search-mcp ``` ## `2` Set up secrets and environment variables Copy and configure your secrets: ```bash cp mcp_agent.secrets.yaml.example mcp_agent.secrets.yaml ``` Then open `mcp_agent.secrets.yaml` and add your API key for your preferred LLM (OpenAI): ```yaml openai: api_key: "YOUR_OPENAI_API_KEY" ``` ## `3` Run locally Run your MCP Agent app with a company name: ```bash uv run main.py "Apple" ``` Or run with a different company: ```bash uv run main.py "Microsoft" ```