Jina.ai Grounding MCP Server
mcp-jinaai-接地
⚠️ 通知
此存储库不再维护。
该工具的功能现已在mcp-omnisearch中提供,它将多个 MCP 工具组合在一个统一的包中。
请改用mcp-omnisearch 。
一个模型上下文协议 (MCP) 服务器,用于集成 Jina.ai 的 Grounding API 与 LLM。该服务器提供高效全面的 Web 内容接地功能,并经过优化,能够通过真实、实时的 Web 内容增强 LLM 响应。
Related MCP server: MCP JinaAI Search Server
特征
🌐 通过 Jina.ai Grounding API 实现高级 Web 内容接地
🚀 实时内容验证和事实核查
📚 全面的网络内容分析
🔄 针对法学硕士 (LLM) 优化的简洁格式
🎯 精准的内容相关性评分
🏗️ 基于模型上下文协议
配置
此服务器需要通过您的 MCP 客户端进行配置。以下是不同环境的示例:
克莱恩配置
将其添加到您的 Cline MCP 设置中:
{
"mcpServers": {
"jinaai-grounding": {
"command": "node",
"args": ["-y", "mcp-jinaai-grounding"],
"env": {
"JINAAI_API_KEY": "your-jinaai-api-key"
}
}
}
}带有 WSL 配置的 Claude 桌面
对于 WSL 环境,将其添加到您的 Claude Desktop 配置中:
{
"mcpServers": {
"jinaai-grounding": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-grounding"
]
}
}
}环境变量
服务器需要以下环境变量:
JINAAI_API_KEY:您的 Jina.ai API 密钥(必需)
API
服务器实现了 MCP 工具,用于将 LLM 响应与 Web 内容结合起来:
ground_content
使用 Jina.ai Grounding 将 LLM 响应与实时网络内容相结合。
参数:
query(字符串,必需):包含网络内容的文本no_cache(布尔值,可选):绕过缓存以获取最新结果。默认为 falseformat(字符串,可选):响应格式(“json”或“text”)。默认为“text”token_budget(数字,可选):此请求的最大令牌数量browser_locale(字符串,可选):用于呈现内容的浏览器语言环境stream(布尔值,可选):为大页面启用流模式。默认为 falsegather_links(布尔值,可选):收集响应末尾的所有链接。默认为 false。gather_images(布尔值,可选):在响应结束时收集所有图像。默认为 false。image_caption(布尔值,可选):内容中的图片标题。默认为 falseenable_iframe(布尔值,可选):从 iframe 中提取内容。默认为 falseenable_shadow_dom(boolean,可选):从影子 DOM 中提取内容。默认为 falseresolve_redirects(布尔值,可选):遵循重定向链到达最终 URL。默认为 true
发展
设置
克隆存储库
安装依赖项:
pnpm install构建项目:
pnpm run build以开发模式运行:
pnpm run dev出版
更新 package.json 中的版本
构建项目:
pnpm run build发布到 npm:
pnpm run release贡献
欢迎贡献代码!欢迎提交 Pull 请求。
执照
MIT 许可证 - 有关详细信息,请参阅LICENSE文件。
致谢
基于模型上下文协议
Available Tools
1 toolground_statementA
Ground a statement using real-time web search results to check factuality. When providing URLs via the references parameter, ensure they are publicly accessible and contain relevant information about the statement. If the URLs do not contain the necessary information, try removing the URL restrictions to search the entire web.
| Name | Required | Description | Default |
|---|---|---|---|
| statement | Yes | Statement to be grounded | |
| references | No | Optional list of URLs to restrict search to. Only provide URLs that are publicly accessible and contain information relevant to the statement. If the URLs do not contain the necessary information, the grounding will fail. For best results, either provide URLs you are certain contain the information, or omit this parameter to search the entire web. | |
| no_cache | No | Whether to bypass cache for fresh results |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key traits like using real-time web search, the impact of URL restrictions (grounding may fail if URLs lack info), and the option to bypass cache. However, it omits details such as rate limits, authentication needs, or specific error handling, leaving some behavioral aspects unclear.
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 appropriately sized and front-loaded, starting with the core purpose. Both sentences earn their place by adding useful context about URL handling, though it could be slightly more streamlined by avoiding minor redundancy with the schema (e.g., repeating URL accessibility advice).
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 tool's complexity (fact-checking with web search) and no annotations or output schema, the description is moderately complete. It covers the main purpose and parameter usage but lacks details on output format, error cases, or performance expectations, which are important for an agent to use it effectively without structured output guidance.
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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by reiterating guidance on the references parameter (e.g., ensuring URLs are accessible and relevant), but it doesn't provide additional semantic context or examples not covered in the schema, warranting a baseline score.
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 tool's purpose with a specific verb ('ground') and resource ('statement'), explaining it uses real-time web search to check factuality. It distinguishes the action from generic search by specifying the grounding objective, and with no sibling tools, this level of specificity is excellent.
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 clear context on when to use the tool (for fact-checking statements) and includes guidance on the references parameter (e.g., ensure URLs are publicly accessible and relevant, or omit to search the entire web). However, it lacks explicit alternatives or exclusions, as there are no sibling tools, so it doesn't fully address when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
ground_statement
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'ground_statement' has a clearly defined and distinct purpose: fact-checking statements using web search.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'ground_statement' follows a clear verb_noun pattern, which would be consistent if more tools were added.
A single tool is too few for a server that appears to handle grounding/verification tasks, as it suggests an incomplete or minimal surface. Typically, such a domain might include tools for different grounding methods, batch processing, or related operations.
The server is severely incomplete for its apparent grounding/fact-checking domain. It lacks essential operations like grounding multiple statements, verifying against specific sources, or handling different input formats, which limits agent workflows.
Maintenance
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