MCP Test Suite

A professional, modular testbed for Model Context Protocol (MCP) servers and CrewAI-powered agents. This repository enables you to run, test, and interact with a variety of MCPs for data analysis, web search, financial analytics, database management, and more—all from a unified Python/Streamlit interface.

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Table of Contents

• Repository Structure
• Overview
• Supported MCP Servers
• Quick Start
• Configuration
• Running MCP Servers
• Streamlit Apps
• Usage Examples
• Contributing
• License

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Repository Structure

• src/: Standalone MCP server modules (each file = one MCP server)
• app/: Streamlit apps
  • multi_mcp_app.py: Launches all MCP servers at once and lets the user select which LLM to use per query
  • single_mcp_app.py: Example of a single MCP/LLM chat app
• llm/: LLM provider definitions and utilities
• project/: Main project logic, including a multi-agent server that combines two MCPs in a single agent
• example-env.env: Example environment file showing required variables for .env
• my_mcp/: Contains your custom MCP server and related modules.
  • etl_mcp_server.py: Example MCP server implementation using FastMCP's @mcp.tool decorator to expose functions as tools.
  • etl_agent.py, etl_app.py, test.py: Additional logic, agent definitions, and tests for your custom server.

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Overview

This project demonstrates how to orchestrate and interact with multiple MCP servers using CrewAI agents. Each MCP server exposes a set of tools (APIs) for a specific domain, such as:

• Financial data (YFinance)
• Database analytics (Supabase)
• Web search (Brave Search)
• GitHub repository analysis
• Docker-based tool orchestration
• Documentation Q&A (Context7)
• Browser automation (Selenium)

You can run each MCP server independently, or use the provided Streamlit apps for a unified chat interface.

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Supported MCP Servers

Each server is implemented as a Python module in src/:

| Server | File | Purpose | Default Port | |-----------------------|-------------------------------|----------------------------------------------|--------------| | Supabase Analyst | src/supabase_mcp_server.py | SQL/CRUD on Supabase DB | 8000 | | YFinance Analyst | src/yfinance_mcp_server.py | Financial data analytics | 8005 | | Brave Web Search | src/brave_mcp_server.py | Web search and scraping | 8003 | | GitHub Analyst | src/github_mcp_server.py | GitHub repo insights | 8001 | | Docker MCP | src/docker_mcp_server.py | Run tools in Docker containers | 8002 | | Context7 Analyst | src/context7_mcp_server.py | Documentation/codebase Q&A | 8004 | | Selenium Scraper | src/selenium_mcp_server.py | Browser automation and scraping | 8003 |

Multi-Agent Analyst:

• project/mcp_server.py: Unified access to YFinance & Supabase in a single agent (port 8000)

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Quick Start

1. Clone the repository

git clone <your-repo-url>
cd mcp-test

2. Install dependencies (using UV)

uv pip install -r requirements.txt
# or, for editable mode:
uv pip install -e .

3. Set up environment variables

• Copy example-env.env to .env:

  cp example-env.env .env
  

• Fill in your API keys and tokens as shown in the example file.

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Configuration

• All sensitive credentials (API keys, tokens) are managed via the .env file.
• Required variables include:
  • OPENAI_API_KEY, SUPABASE_ACCESS_TOKEN, BRAVE_API_KEY, GITHUB_PERSONAL_ACCESS_TOKEN, etc.
• See example-env.env for a full list and format.

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Running MCP Servers

Each MCP server can be started individually. For example:

# Start Supabase MCP server
python src/supabase_mcp_server.py

# Start YFinance MCP server
python src/yfinance_mcp_server.py

# Start Brave Web Search MCP server
python src/brave_mcp_server.py

# Start Multi-Agent Analyst (unified)
python project/mcp_server.py

• By default, servers run on localhost with different ports (see table above).
• You can run multiple servers in parallel (in separate terminals).

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Streamlit Apps

Two Streamlit apps are provided for interactive chat with MCP agents:

• app/multi_mcp_app.py: Launches all MCP servers and lets you select the LLM and MCP for each query.
• app/single_mcp_app.py: Example of a single MCP/LLM chat app.

Run with:

streamlit run app/multi_mcp_app.py
# or
streamlit run app/single_mcp_app.py

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LLMs

• The llm/ folder contains all available LLM provider definitions.
• You can select which LLM to use in the multi-agent Streamlit app.

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Usage Examples

• Ask financial questions:

  "Show me the revenue growth of Apple over the last 5 years."

• Query your Supabase DB:

  "List all users who signed up in the last month."

• Analyze a GitHub repo:

  "Summarize the top contributors and open issues for repo X."

• Perform web search:

  "Find the latest news about AI regulation."

All actions are performed securely via CrewAI-powered agents and MCP tools.

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Using MCP Servers with Desktop Claude

You can connect any MCP server from this repository to Desktop Claude for seamless local tool integration.

1. Start an MCP Server for Desktop Claude

Use the fastmcp CLI to launch the desired MCP server. For example, to run the YFinance MCP server:

fastmcp run src/yfinance_mcp_server.py:mcp

Replace yfinance_mcp_server.py with any other MCP server file in src/ as needed.

2. Configure Desktop Claude

Edit your claude_desktop_config.json file to add the MCP server. Example configuration for YFinance:

{
  "mcpServers": {
    "yfinance-agent-server": {
      "command": "<repository/path>/.venv/Scripts/python",
      "args": [
        "<repository/path>/src/yfinance_mcp_server.py"
      ]
    }
  }
}

• Replace <repository/path> with the absolute path to your local repository.
• Ensure the Python executable path matches your environment (e.g., .venv/Scripts/python on Windows).
• You can add multiple servers by duplicating the block under mcpServers and changing the server name and script.

After saving the config, restart Desktop Claude. The MCP server will be available as a local tool.

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Custom MCP Server: my_mcp

You can extend this test suite by adding your own MCP servers. This repository includes a sample custom MCP server in the my_mcp/ directory. This is a great starting point for building and testing your own tools using the FastMCP framework.

Structure

• my_mcp/: Contains your custom MCP server and related modules.
  • etl_mcp_server.py: Example MCP server implementation using FastMCP's @mcp.tool decorator to expose functions as tools.
  • etl_agent.py, etl_app.py, test.py: Additional logic, agent definitions, and tests for your custom server.

How to Use Your Custom MCP Server

To use your custom MCP server and integrate it as a tool in your own Streamlit application, follow these steps:

1. Start the Custom MCP Server

   This launches the FastMCP server that exposes your ETL functions as tools:

   python my_mcp/etl_mcp_server.py
   

2. Start the Agent

   This script connects to the running MCP server, wraps it in an agent, and exposes the agent as a tool:

   python my_mcp/etl_agent.py
   

3. Launch the Streamlit Application

   This app uses the agent (now a tool) to provide an interactive UI for your ETL workflows:

   streamlit run my_mcp/etl_app.py
   

Summary:

• The MCP server (etl_mcp_server.py) exposes your functions as tools.
• The agent (etl_agent.py) connects to the MCP server and turns the agent into a tool for your app.
• The Streamlit app (etl_app.py) provides a user interface to interact with your agent/tool.

You can further customize each step to fit your workflow and add more tools or agents as needed.

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Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements, bug fixes, or new MCP integrations.

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