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gustavo-sacchi

Financial Data MCP Server

by gustavo-sacchi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Tutorial MCP + LangGraph

Este projeto demonstra como integrar o Model Context Protocol (MCP) com LangGraph para criar um agente ReAct inteligente que pode acessar dados financeiros em tempo real.

Objetivo

  • Como criar um servidor MCP com ferramentas personalizadas

  • Como conectar um agente LangGraph ao servidor MCP

  • Como implementar memoria de conversacao persistente

  • Como usar ferramentas de analise financeira via MCP

Related MCP server: Agentic AI with MCP

Pre-requisitos

  • Python 3.13+

  • Chaves de API do OpenAI e/ou Groq

  • FastMCP

  • LangGraph MCP Adapter

Instalacao

1. Instalar UV (Gerenciador de Pacotes Python)

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clonar e Configurar o Projeto

# Clone o repositorio
git clone https://github.com/gustavo-sacchi/tutorial_mcp_langgraph.git
cd tutorial_mcp_langgraph

# Instale as dependencias e crie o ambiente virtual
uv sync

# Configure as variaveis de ambiente
cp .env.exemple .env

3. Configurar Chaves de API

Edite o arquivo .env e adicione suas chaves:

OPENAI_API_KEY=sua-chave-openai-aqui
GROQ_API_KEY=sua-chave-groq-aqui

Como Usar

1. Iniciar o Servidor MCP

Em um terminal:

uv run server_mcp.py

O servidor estara disponivel em http://localhost:4200

2. Executar o Cliente (Agente)

Em outro terminal:

uv run client_mcp.py

3. Interagir com o Agente

Agora voce pode fazer perguntas sobre financas:

User: Qual e o preco atual da acao da Apple?
User: Me de um resumo completo da VALE3.SA
User: Pesquise noticias recentes sobre investimentos em IA

Testando o MCP

Para testar a interface MCP diretamente:

npx @modelcontextprotocol/inspector uv run server_mcp.py

Arquitetura

┌─────────────────┐    HTTP     ┌─────────────────┐
│  Client         │◄──────────►│  MCP Server     │
│  (LangGraph)    │             │  (FastMCP)      │
│                 │             │                 │
│ • ChatOpenAI    │             │ • YFinance      │
│ • ReAct Agent   │             │ • DuckDuckGo    │
│ • Memory        │             │ • Web Scraping  │
└─────────────────┘             └─────────────────┘

Ferramentas Disponiveis

O servidor MCP fornece estas ferramentas financeiras:

  • Resumo de Acoes: Preco, volume e data

  • Estimativas de Analistas: Precos-alvo

  • Recomendacoes: Buy/Hold/Sell

  • Dividendos: Historico de pagamentos

  • Setor/Industria: Classificacao da empresa

  • Demonstracoes Financeiras: Balanco, DRE, Fluxo de Caixa

  • Pesquisa Web: Noticias e conteudo relevante

Video Tutorial

Este projeto foi criado para acompanhar o tutorial no YouTube. Assista ao video completo para entender todos os detalhes da implementacao:

📺 Assistir no YouTube

Personalizacao

  • Modelos: Altere entre OpenAI e Groq no client_mcp.py

  • Transporte: Configure HTTP ou stdio no server_mcp.py

  • Ferramentas: Adicione novas ferramentas MCP no servidor

  • Prompts: Customize os prompts disponiveis

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

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