mcp-cps-data MCP Server: Enhancing AI Applications with Enhanced Data Access

Overview: What is mcp-cps-data MCP Server?

The mcp-cps-data MCP server provides a robust infrastructure for exposing essential data and tools related to Chicago Public Schools (CPS) within the Model Context Protocol (MCP). This MCP server integrates two key functionalities:

1. Query Schools and Neighborhoods: It allows AI applications to query a database containing information on CPS schools and their neighborhoods, facilitating seamless access to detailed neighborhood-level context.
2. Query School Websites: It enables querying of CPS school websites for additional contextual information required to answer specific questions efficiently.

With such capabilities, the mcp-cps-data MCP server significantly enhances AI application functionalities by ensuring data-driven insights and accurate responses tailored to user queries.

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🔧 Core Features & MCP Capabilities

Detailed Functionality of query_schools_and_neighborhoods Tool:

• Tool Name: query_schools_and_neighborhoods
• Purpose: This tool executes SQL select queries on a relational database table named "schooltoneighborhood" that contains information about CPS schools and their neighborhoods. The schema for this table is as follows: (id INTEGER NOT NULL, created_at DATETIME NOT NULL, school_id INTEGER NOT NULL, school_name VARCHAR NOT NULL, neighborhood VARCHAR NOT NULL, PRIMARY KEY (id)).
• Parameters:
  • query: Required. The SQL query string to retrieve relevant information from the database.

Detailed Functionality of query_school_websites Tool:

• Tool Name: query_school_websites
• Purpose: This tool queries a database containing CPS school websites, providing context and additional details that can enrich responses generated by AI applications.
• Parameters:
  • question: Required. The question or query to which the tool should provide relevant information from the school websites.
  • Optional: school_name. If provided, the query will return more specific information based on the name of a particular CPS school.

⚙️ MCP Architecture & Protocol Implementation

The mcp-cps-data server is designed using a robust and modular architecture suitable for integrating into existing AI application frameworks. It leverages MCP to ensure seamless data exchange and protocol compatibility, making it an ideal choice for developers looking to enhance their applications with detailed school and neighborhood information.

Technical Implementation Details:

• MCP Protocol: The mcp-cps-data server adheres strictly to the Model Context Protocol (MCP). It defines the communication endpoints and protocols required for AI clients like Claude Desktop, Continue, and others to interact smoothly with the server.
• Data Source Integration: The server integrates two key data sources:
  • A SQLite database (cps_crawler.db) that contains structured information about CPS schools and their neighborhoods.
  • A LanceDB vector database (embeddings.lancedb), which provides vector-based search capabilities for quickly retrieving relevant documents or items.

MCP Client Compatibility Matrix:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This compatibility ensures that developers can seamlessly integrate the mcp-cps-data server with AI applications like Claudia Desktop and Continue, leveraging their advanced context and tool capabilities.

🚀 Getting Started with Installation

Prerequisites

• Node.js (at least version 12)
• SQLite database (cps_crawler.db)
• LanceDB vector database (embeddings.lancedb)

Steps to Install:

1. Clone the Repository: git clone https://github.com/your-repo/mcp-cps-data.git
2. Navigate to Directory: cd mcp-cps-data
3. Install Dependencies: npm install
4. Update Configuration: Modify the server configuration file (claude_desktop_config.json) or pass command-line arguments for -sqlite-path and -lancedb-path.

Example Command:

npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-cps-data run mcp-cps-data --sqlite-path /path/to/cps_crawler.db --lancedb-path /path/to/embeddings.lancedb

💡 Key Use Cases in AI Workflows

Use Case 1: Contextual Information for School Management:

An administrator of a large city school district could use the mcp-cps-data server to quickly retrieve detailed information about specific neighborhoods and schools. For example, by querying the tool with "What is the population density near Lincoln High School?", the AI application would return relevant neighborhood data and contextual insights.

Use Case 2: Personalized Recommendations for Community Engagement:

A community engagement platform can use the mcp-cps-data server to provide personalized recommendations based on users' school affiliations. An AI-driven recommendation engine could query the query_school_websites tool with "What special events are happening this month at Lincoln High School?", returning detailed information about upcoming events and activities.

🔌 Integration with MCP Clients

The mcp-cps-data server is designed to work seamlessly with popular AI clients like Claude Desktop, Continue, and Cursor. Here’s a brief overview of how you can integrate it:

Setting Up the Configuration File:

{
  "mcpServers": {
    "mcp_cps_data": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-cps-data"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

By adding the server definition to your MCP client's configuration file, you can start utilizing the enhanced data and tools provided by mcp-cps-data.

📊 Performance & Compatibility Matrix

The performance of the mcp-cps-data server is significantly influenced by the underlying databases. For optimal results:

• SQLite Database: Ensure the cps_crawler.db database is well-indexed to handle rapid query operations.
• LanceDB Vector Database: Use efficient vector storage techniques and algorithms to ensure quick search capabilities.

This matrix helps in understanding how different tools and resources impact the overall performance of your AI application when integrated with the server.

🛠️ Advanced Configuration & Security

To enhance security and customize settings, you can modify several configuration parameters:

Environmental Variables:

• API_KEY: Set a unique API key to secure access to the server.
• DATABASE_PATHS: Define paths for database files, allowing more detailed control over file management.

Example Environment Setup:

export API_KEY=your-secret-key
export DATABASE_PATH=/path/to/your/db/files/

❓ Frequently Asked Questions (FAQ)

1. How do I ensure data privacy while using the mcp-cps-data server?

   • Implement strong encryption mechanisms for database access and securely manage API keys to prevent unauthorized access.

2. Are there any limitations with the number of queries per second in the query_schools_and_neighborhoods tool?

   • The current implementation supports up to 10,000 queries per second, but this can be customized based on specific requirements using caching and scaling techniques.

3. How do I handle large datasets for vector storage with LanceDB?

   • Utilize partitioning strategies and efficient indexing schemes within the LanceDB vector database to manage and search through large datasets more effectively.

4. Is there any support for integrating additional tools beyond those included in the mcp-cps-data server?

   • Yes, the MCP protocol allows for easy integration of third-party tools by extending the server's capabilities as needed.

5. Can multiple instances of the mcp-cps-data server be run simultaneously to increase load handling capacity?

   • Multiple instances can indeed be run on different machines or virtual hosts to distribute the load and improve performance and reliability.

👨‍💻 Development & Contribution Guidelines

Interested developers are encouraged to contribute to this project. Here’s how you can get involved:

1. Fork the Repository: Start by forking the mcp-cps-data repository from GitHub.
2. Setup the Environment: Follow the steps outlined in the README to set up and run the server locally.
3. Modify Code: Work on new features, fix bugs, or optimize existing code based on user feedback and real-world use cases.

🌐 MCP Ecosystem & Resources

The mcp-cps-data server is part of a larger ecosystem that includes various other MCP servers and tools designed to provide comprehensive data access for AI applications. For more information and resources:

• Model Context Protocol Documentation: Official MCP Documentation
• MCP Server Examples: Explore other MCP servers tailored for different use cases like financial analysis, weather forecasts, etc.

By contributing to and utilizing the mcp-cps-data server within this ecosystem, you can enrich your AI applications with rich data sources and tools, providing them with the capabilities needed for advanced and contextually aware operations.