About EntityIdentification
EntityIdentification is an MCP server for deciding whether two data sets refer to the same entity. It combines text normalization, key-by-key JSON comparison, and generative language-model judgments of semantic similarity.
Key features
- 01Normalize case, punctuation, and whitespace
- 02Compare JSON objects key by key
- 03Compare values exactly and semantically
- 04Ignore list order in semantic comparison
- 05Use a generative language model for entity judgments
Use cases
- 01Check whether two records refer to the same entity
- 02Compare JSON data containing list values
MCP support
The project identifies itself as an MCP server and provides a tool for comparing two data sets; input and MCP interface details are not specified.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEEntityIdentification
Identify whether two sets of data are from the same entity. 识别两组数据是否来自同一主体
This is a MCP (Model Context Protocol) server. 这是一个支持MCP协议的服务器。
Data Comparison Tool
This tool provides a comprehensive way to compare two sets of data, evaluating both exact and semantic equality of their values. It leverages text normalization and a language model to determine if the data originates from the same entity.
Features
- Text Normalization: Converts text to lowercase, removes punctuation, and normalizes whitespace.
- Value Comparison: Compares values directly and semantically (ignoring order for lists).
- JSON Traversal: Iterates through each key in the JSON objects and compares corresponding values.
- Language Model Integration: Uses a generative language model to assess semantic similarity and provide a final judgment on whether the data comes from the same entity.
Installation
To use this tool, ensure you have the necessary dependencies installed. You can install them using pip:
pip install genai
Usage
Functions
-
normalize_text(text):
- Normalizes the input text by converting it to lowercase, removing punctuation, and normalizing whitespace.
-
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.
-
compare_json(json1, json2):
- Compares two JSON objects key by key.
- Uses
compare_valuesto evaluate each key's values. - Integrates a language model to assess semantic similarity and provides a final judgment.
Example
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("综合这些信息,你认为可以判断两个数据来自同一主体吗?"+json.dumps(comparison_results, ensure_ascii=False, indent=4))
print(result_matching.text)
Contributing
Contributions are welcome! Please open an issue or submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Contact
If you have any questions or suggestions, please contact me:
- Email: [email protected]
- GitHub: [email protected]。
Wechat
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.