EntityIdentification
@u3588064
About EntityIdentification
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
Config
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Overview
What is EntityIdentification?
EntityIdentification is an MCP (Model Context Protocol) server that compares two sets of data (typically JSON objects) to determine whether they originate from the same real-world entity. It performs text normalization, value comparison (exact and semantic), and uses a generative language model to assess semantic similarity and provide a final judgment.
How to use EntityIdentification?
Install the required dependency with pip install genai, then call the compare_json function with two JSON objects. The function iterates through keys, compares values using normalize_text and compare_values, and finally uses a language model (e.g., gemini-2.0-flash-thinking-exp) to generate a matching result.
Key features of EntityIdentification
- Text normalization (lowercase, remove punctuation, normalize whitespace)
- Exact and semantic value comparison
- Order‑ignoring comparison for lists
- JSON key‑by‑key traversal
- Language model integration for final similarity judgment
Use cases of EntityIdentification
- Deduplicating customer records across different systems
- Matching user profiles from multiple data sources
- Detecting duplicate entries in contact lists or databases
- Verifying identity information during data merging
FAQ from EntityIdentification
What dependencies does EntityIdentification require?
The server requires Python with the genai library installed (pip install genai). It also uses the standard json and re modules. The language model specified in the example is gemini-2.0-flash-thinking-exp.
How does the language model help in entity identification?
The language model assesses the semantic similarity of the compared values and provides a final judgment on whether the two sets of data come from the same entity.
Does EntityIdentification store or share my data externally?
No. The README does not mention any external storage or data sharing. The comparison is performed on the provided JSON objects, and the language model is used only for inference.
Can EntityIdentification handle lists with items in different order?
Yes. The compare_values function ignores the order of elements when comparing lists, allowing for semantic matching even if the order differs.
What transports or authentication mechanisms does EntityIdentification use?
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Frequently asked questions
What dependencies does EntityIdentification require?
The server requires Python with the `genai` library installed (`pip install genai`). It also uses the standard `json` and `re` modules. The language model specified in the example is `gemini-2.0-flash-thinking-exp`.
How does the language model help in entity identification?
The language model assesses the semantic similarity of the compared values and provides a final judgment on whether the two sets of data come from the same entity.
Does EntityIdentification store or share my data externally?
No. The README does not mention any external storage or data sharing. The comparison is performed on the provided JSON objects, and the language model is used only for inference.
Can EntityIdentification handle lists with items in different order?
Yes. The `compare_values` function ignores the order of elements when comparing lists, allowing for semantic matching even if the order differs.
Basic information
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