EntityIdentification MCP Server: Comprehensive Data Matching for AI Applications

Overview: What is EntityIdentification MCP Server?

The EntityIdentification MCP (Model Context Protocol) server is designed to facilitate seamless data matching and entity identification between AI applications and various data sources and tools through a standardized protocol. This server leverages advanced text normalization, value comparison, JSON traversal, and language model integration techniques to determine whether two sets of data originate from the same entity.

🔧 Core Features & MCP Capabilities

The EntityIdentification MCP server offers several key features and capabilities:

• Text Normalization: Converting input text to lowercase, removing punctuation, and normalizing whitespace ensures consistent data processing.
• Value Comparison: Directly comparing values while also considering semantic similarity, which is particularly useful for ignoring order in lists.
• JSON Traversal: Iterating through each key in JSON objects to compare corresponding values, integrating a generative language model for assessing semantic similarity.
• Language Model Integration: Utilizing a generative language model to assess the overall semantic similarity and make the final judgment on whether data comes from the same entity.

These features enable accurate and efficient data matching across diverse AI applications. The server supports integration with various MCP clients, ensuring broad compatibility and flexibility in use cases.

⚙️ MCP Architecture & Protocol Implementation

The EntityIdentification MCP server implements a robust protocol that aligns with Model Context Protocol (MCP) standards. It utilizes a JSON-based configuration to define connections between AI applications and data sources or tools. The server interacts with the client through well-defined APIs, ensuring seamless communication.

Below is an example of the MCP server's architecture diagram:

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

MCP Client Compatibility Matrix

The EntityIdentification MCP server supports the following MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

This matrix indicates that the server fully supports various resources and prompts for Claude Desktop and Continue, making them ideal partners for comprehensive data matching. However, applications like Cursor are limited in terms of prompt generation.

🚀 Getting Started with Installation

To begin using the EntityIdentification MCP server, ensure you have Python environment setup and install the required dependencies:

pip install genai

Functions Overview

The server provides several functions to handle different aspects of data comparison:

1. normalize_text(text):

   • Normalizes the input text by converting it to lowercase, removing punctuation, and normalizing whitespace.

2. compare_values(val1, val2):

   • Compares two values both exactly and semantically.
   • If the values are lists, it ignores the order of elements for semantic comparison.

3. compare_json(json1, json2):

   • Compares two JSON objects key by key using compare_values.
   • Utilizes a language model to assess semantic similarity and provide final judgment.

Example Code Usage

Below is an example usage scenario:

import json
import genai
import re

# Define your JSON objects
json1 = {
    "name": "John Doe",
    "address": "123 Main St, Anytown, USA",
    "hobbies": ["reading", "hiking", "coding"]
}

json2 = {
    "name": "john doe",
    "address": "123 Main Street, Anytown, USA",
    "hobbies": ["coding", "hiking", "reading"]
}

# Compare the JSON objects
comparison_results = compare_json(json1, json2)

# Generate final matching result
model1 = genai.GenerativeModel("gemini-2.0-flash-thinking-exp")
result_matching = model1.generate_content("Based on these comparisons, can you determine if the two sets of data likely come from the same entity? " + json.dumps(comparison_results, ensure_ascii=False, indent=4))
print(result_matching.text)

💡 Key Use Cases in AI Workflows

Scenario 1: Identifying Duplicate Records

One common use case is identifying duplicate records in customer databases. The EntityIdentification MCP server can compare customer information like names, addresses, and hobbies to determine if two records represent the same person.

Scenario 2: Ensuring Data Consistency Across Applications

Another scenario involves ensuring data consistency across different AI applications. For instance, if multiple tools are pulling from a central database, the EntityIdentification server can validate whether the data retrieved from these sources matches expected patterns and values.

🔌 Integration with MCP Clients

The EntityIdentification MCP server supports integration with various clients that adhere to Model Context Protocol (MCP). This ensures seamless communication between AI applications like Claude Desktop, Continue, Cursor, and other tools. The protocol flow diagram highlights this interaction:

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

📊 Performance & Compatibility Matrix

While the EntityIdentification MCP server supports multiple clients, it has specific compatibility levels. The following matrix outlines the current status of support for various tools:

Tool	Compatibility
Data Source 1	✅
Data Source 2	❌ (In Beta)
Tool X	❌ (Deprecated)

🛠️ Advanced Configuration & Security

The server can be configured with advanced settings to fine-tune its performance and security. Here's an example of the JSON configuration:

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

❓ Frequently Asked Questions (FAQ)

1. Q: How does the EntityIdentification server handle data normalization?

   • A: The server normalizes text by converting it to lowercase, removing punctuation, and normalizing whitespace.

2. Q: Can multiple AI applications use this server simultaneously?

   • A: Yes, the server can be configured to support simultaneous connections from multiple clients.

3. Q: What happens if a client is not compatible with the protocol?

   • A: The server will provide error messages indicating incompatibility and suggest using a compatible MCP client.

4. Q: Can this server handle large-scale data comparisons efficiently?

   • A: Yes, the built-in language model optimizes semantic comparison for efficiency even with large datasets.

5. Q: How can I contribute to improving the EntityIdentification server?

   • A: Contributions are welcome! Open issues or submit pull requests through GitHub.

👨‍💻 Development & Contribution Guidelines

If you're interested in contributing to the EntityIdentification MCP server, please follow these guidelines:

1. Fork the repository from GitHub.
2. Create a new branch for your feature or bug fix.
3. Ensure all tests pass and add any necessary documentation.
4. Submit a pull request detailing your changes.

🌐 MCP Ecosystem & Resources

For developers building AI applications and MCP integrations, exploring the broader MCP ecosystem is crucial. The EntityIdentification server is a key component in ensuring seamless data matching across various tools and resources.

MCP Client Compatibility Matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

By leveraging the EntityIdentification MCP server, developers can enhance their AI applications with robust data matching and entity identification capabilities.