Which language model does EntityIdentification use?
The example uses `genai.GenerativeModel("gemini-2.0-flash-thinking-exp")` to generate the final matching result.
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Basic information
MCP (Model Context Protocol) server for identifying whether two sets of data are from the same entity. 识别两组数据是否来自同一主体的MCP服务器
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
EntityIdentification is an MCP (Model Context Protocol) server that determines whether two sets of JSON data originate from the same real-world entity. It compares values using exact matching, text normalization, and a generative language model for semantic similarity.
Install the required dependency with pip install genai. Use the provided functions normalize_text, compare_values, and compare_json to compare two JSON objects. The server integrates a language model to assess semantic similarity and returns a final judgment.
The example uses `genai.GenerativeModel("gemini-2.0-flash-thinking-exp")` to generate the final matching result.
Install the required package with `pip install genai`. No other setup is mentioned.
Yes. When comparing list values, the server ignores element order for semantic comparison.
After comparing JSON objects key-by-key, it uses a language model to produce a textual judgment on whether the two data sets come from the same entity.
The README does not list specific limitations. It only describes the comparison logic and example usage with a single language model.
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