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@dre1144

Repository

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#finance#portfolio-analysis#recommendation-agent

Basic information

Category

Finance & Commerce

Transports

stdio

Publisher

dre1144

Submitted by

Andrey

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "github": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SUPABASE_URL",
        "-e",
        "SUPABASE_SERVICE_KEY",
        "-e",
        "ENCRYPTION_KEY",
        "-e",
        "MCP_API_KEY",
        "-e",
        "MCP_API_URL",
        "-e",
        "GITHUB_PERSONAL_ACCESS_TOKEN",
        "mcp/finance-portfolio-agent"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_TOKEN>"
      }
    }
  }
}

Tools

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Overview

What is MCP Finance Agent?

MCP Finance Agent is a Model Context Protocol agent for analyzing financial portfolios and generating recommendations. It integrates with the Tinkoff Invest API to provide portfolio analysis, risk assessment, and optimization suggestions.

How to use MCP Finance Agent?

Clone the repository, install dependencies with Poetry, configure environment variables in a .env file, then run the agent using poetry run python -m src.main. The API becomes available at http://localhost:8000.

Key features of MCP Finance Agent

  • Integration with Tinkoff Invest API
  • Portfolio and risk analysis
  • Recommendation generation for portfolio optimization
  • Real-time market data monitoring
  • Efficiency metric calculations

Use cases of MCP Finance Agent

  • Automated portfolio risk assessment
  • Personalized portfolio rebalancing recommendations
  • Continuous monitoring of financial positions

FAQ from MCP Finance Agent

What runtime does MCP Finance Agent require?

Python 3.11+ and Poetry for dependency management are required.

How do I configure MCP Finance Agent?

Create a .env file based on .env.example and set the required environment variables (e.g., API keys for Tinkoff Invest).

How is the agent tested?

Run pytest to execute all tests with coverage reporting. Specific tests can be run individually, and tests can be filtered by marker (e.g., -m "not integration").

What is the structure of the project?

Source code lives in src/ with subdirectories for the agent core, external services, and data models. Tests are in tests/ and documentation in docs/.

How do I start development?

Activate the virtual environment with poetry shell, install pre-commit hooks with pre-commit install, and run tests with pytest.

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