用于访问 entscheidsuche.ch 瑞士法律决策搜索 API 的 MCP 服务器。
该服务器通过模型上下文协议 (MCP) 提供对瑞士法院判决的标准化访问。它允许像 Claude 这样的法学硕士 (LLM) 从 entscheidsuche.ch 数据库中搜索、检索和分析法律文件。
Related MCP server: Legal Court MCP Server
资源：访问瑞士法院判决作为可搜索的资源
工具：搜索法院判决、检索文件、按州列出法院
提示：常见法律研究任务的模板
# Clone the repository
git clone [repository-url]
cd entscheidsuche-mcp-server
# Install dependencies
npm install
# Build the server
npm run build打开 Claude for Desktop 的设置
将以下内容添加到您的claude_desktop_config.json中：
{
"mcpServers": {
"entscheidsuche": {
"command": "node",
"args": ["/absolute/path/to/entscheidsuche-mcp-server/build/index.js"]
}
}
}重启 Claude 桌面版
开始询问法律研究问题！
npx @modelcontextprotocol/inspector node /path/to/entscheidsuche-mcp-server/build/index.jsentscheidsuche://scrapers - 列出所有可用的 scrapers/collections
entscheidsuche://scraper/{scraperId} - 获取特定抓取工具的详细信息
entscheidsuche://document/{documentId} - 访问特定文档的元数据
search-decisions - 使用 Elasticsearch 查询语法搜索法院判决
get-document-content - 检索特定文档的内容
list-courts - 按州列出可用的法院
get-document-urls - 获取文档 PDF 和 HTML 版本的直接 URL
search-legal-precedents - 查找特定法律主题的相关先例
compare-jurisdictions - 比较不同州对特定法律问题的裁决
court-decisions - 检索特定法院的最新判决
Can you find Swiss court decisions about copyright infringement in Zurich from the last 5 years?How do different Swiss cantons approach the legal issue of tenant rights in rental disputes?Can you retrieve and analyze the decision with ID "ZH_VG-VB.2021.00042"?使用 MCP TypeScript SDK 构建
尊重速率限制，以“保护 entscheidsuche.ch 服务器”
处理正确的身份验证和错误处理
使用关键元数据（法院、日期、案件编号）格式化搜索结果
欢迎贡献代码！欢迎提交 Pull 请求。
该项目已获得 MIT 许可。
entscheidsuche.ch提供 API
模型上下文协议团队提供的优秀 SDK
本文档提供了如何通过 Claude 使用 Entscheidsuche MCP 服务器研究瑞士法律判决的示例。
Find Swiss court decisions about intellectual property rights in the technology sector from the last 5 years.What are some important court decisions from the canton of Zurich (ZH) related to landlord-tenant disputes?Can you find Swiss Federal Supreme Court cases discussing the concept of "good faith" (Treu und Glauben) in contract law?Can you retrieve and analyze the Swiss court decision with ID "CH_BGer-4A_283_2021"?I'd like to access the original court decision for case number "ZH_OG-LB190025". Can you provide the PDF and HTML links?How do the cantons of Geneva (GE), Vaud (VD), and Zurich (ZH) differ in their approach to divorce settlements? Please search for relevant cases and compare.Has there been an evolution in how Swiss courts have interpreted data protection rights over the last decade? Search for relevant cases and analyze the trend.I'm researching a case where an employee was terminated while on medical leave. Can you find Swiss court decisions that established precedent for similar situations?Find the most significant Swiss court decisions related to pharmaceutical patent disputes and analyze how they've shaped the legal landscape in this area.Using the compare-jurisdictions prompt, please analyze how different Swiss cantons approach the legal issue of "non-compete clauses" in employment contracts.Using the search-legal-precedents prompt, find relevant Swiss legal precedents about "algorithmic decision making" and data protection, focusing on federal court decisions.Using the court-decisions prompt, retrieve recent decisions from the Swiss Federal Supreme Court (Bundesgericht) within the last 2 years related to cryptocurrency regulation.Retrieve the full content of a specific legal document
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Document format to retrieve | json |
| signature | Yes | Document signature (e.g., CH_BGer_005_5F-23-2025_2025-07-01) | |
| spider | No | Court/spider name (e.g., CH_BGer). If not provided, will be extracted from signature |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Retrieve' implies a read operation, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, response format, or whether this is a simple fetch versus a complex operation. The description is minimal and lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's purpose. There's no wasted language, repetition, or unnecessary elaboration. It's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a valid document signature, how the retrieved content is structured, error handling, or operational constraints. The minimal description leaves too many questions unanswered for effective tool usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents all three parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema - it doesn't explain the relationship between signature and spider parameters, provide examples of valid signatures, or clarify the format parameter's implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve') and resource ('full content of a specific legal document'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search_case_law' or 'list_courts' - it doesn't explain that this retrieves a single document by signature rather than searching or listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance about when to use this tool versus alternatives. There's no mention of prerequisites, when this tool is appropriate versus 'search_case_law', or any context about what constitutes a 'specific legal document' that can be retrieved.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get information about available courts and their document counts
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'Get information' but doesn't disclose behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what format the information is returned in. The description is minimal and lacks essential context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or unnecessary details. It is front-loaded and appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'information' includes (e.g., court names, IDs, document counts), how results are structured, or any limitations. For a tool that returns data, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, and since there are none, it meets the baseline of 4 for not introducing confusion or redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'information about available courts and their document counts', which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'search_case_law', which might also involve court information but with different functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_case_law'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for Swiss court decisions using Entscheidsuche database
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | Starting position for pagination | |
| query | Yes | Search query for legal cases | |
| size | No | Number of results to return (max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the basic function. It lacks details on behavioral traits such as rate limits, authentication needs, error handling, or what the search returns (e.g., result format, metadata). This is inadequate for a search tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without redundancy. It's front-loaded and wastes no words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with no annotations or output schema, the description is insufficient. It doesn't explain what the search returns, how results are structured, or any limitations, leaving gaps for the agent to understand the tool's behavior fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters like 'query' for search terms and 'from/size' for pagination. The description adds no additional meaning beyond implying a legal context, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and target resource ('Swiss court decisions') with the specific database ('Entscheidsuche'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_document' or 'list_courts', which might also retrieve legal information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention scenarios for searching case law compared to getting specific documents or listing courts, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_documentlist_courtssearch_case_lawEach tool has a clearly distinct purpose: get_document retrieves specific document content, list_courts provides metadata about courts, and search_case_law performs searches across the database. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern (get_document, list_courts, search_case_law) with clear, descriptive verbs. There are no deviations in naming conventions, making the set predictable and easy to understand.
With only 3 tools, the server feels somewhat thin for a legal document search domain. While the tools cover core operations (retrieve, list, search), more comprehensive coverage might include tools for filtering, advanced search, or document metadata management, suggesting a borderline appropriateness.
The tools provide basic CRUD-like operations (get, list, search) for legal documents and courts, but there are notable gaps. For example, there is no tool for updating or deleting documents, managing user queries, or handling advanced search parameters, which could limit agent effectiveness in complex workflows.
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