Mcp Server Moke Wenshu

Overview

MokeWenShu is a specialized MCP server that bridges AI assistants with China’s public judgment repository, 中国裁判文书网. By exposing the court documents as structured resources and providing a suite of NLP‑driven tools, it transforms raw legal text into an interactive knowledge base that can be queried conversationally. Developers who need to surface judicial precedent, perform comparative case analysis, or track evolving legal trends now have a ready‑made interface that integrates seamlessly with Claude or other MCP‑compatible assistants.

The server addresses the pain point of navigating voluminous, unstructured court opinions. It parses judgments into a graph format where entities such as parties, legal provisions, and factual elements become linked nodes. This structure enables the assistant to answer questions like “Which cases cited Article 123 of the Civil Code?” or “What are the most common outcomes for disputes involving intellectual property in 2023?” without requiring manual indexing or custom search engines. The ability to query the knowledge graph through natural language dramatically reduces development time for legal analytics applications.

Key capabilities include:

• Conversational document analysis – Users can ask open‑ended questions about a specific judgment, and the assistant will retrieve relevant passages, summarize key points, and highlight critical legal arguments.
• Similar‑case pattern mapping – The server compares new or user‑supplied cases against the indexed corpus, returning a ranked list of precedent cases with similarity scores and visual maps of shared legal themes.
• Judgment trend visualization – Built‑in tools aggregate decision data over time, producing charts that show shifts in sentencing patterns, judicial focus areas, or regional variations.
• Legal knowledge graph integration – By exposing the underlying graph through MCP resources, developers can compose complex queries that combine case facts with statutory references and precedent relationships.

In practice, MokeWenShu powers workflows such as a law firm’s due‑diligence assistant, which can quickly surface comparable cases and trend insights during client briefings. A court analytics startup might use the server to build dashboards that monitor how new legislation is interpreted across jurisdictions. Even academic researchers can tap into the graph to study jurisprudential evolution or conduct large‑scale legal NLP experiments.

What sets MokeWenShu apart is its end‑to‑end pipeline that converts raw judgments into a semantically rich graph and exposes that data through the MCP interface. Developers can focus on building conversational experiences rather than wrestling with text extraction, entity recognition, or graph construction. The server’s modular design means additional tools—such as sentiment analysis of judge opinions or automated extraction of key dates—can be added without disrupting existing integrations. For any project that requires deep, actionable insights from China’s judicial corpus, MokeWenShu offers a ready, AI‑friendly gateway that turns static documents into dynamic knowledge.