A
licenseNot graded
qualityD
maintenanceEnables web content retrieval and semantic search capabilities through the Jina AI API. Provides tools to fetch content from URLs and perform intelligent web searches with natural language queries.
3
MIT
MCP JinaAI Search Server
by spences10
mcp-jinaai-搜索
⚠️ 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，用于将 Jina.ai 的搜索 API 与 LLM 集成。该服务器提供高效全面的网络搜索功能，并针对从网络检索简洁、LLM 友好的内容进行了优化。
Related MCP server: Jina AI Remote MCP Server
特征
🔍 通过 Jina.ai Search API 进行高级网页搜索
🚀 快速高效的内容检索
📄 保留结构的干净文本提取
🧠 针对法学硕士 (LLM) 优化的内容
🌐 支持各种内容类型，包括文档
🏗️ 基于模型上下文协议
🔄 可配置缓存以提高性能
🖼️ 可选的图像和链接收集
🌍 通过浏览器语言环境支持本地化
🎯 响应大小的令牌预算控制
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-search": {
"command": "node",
"args": ["-y", "mcp-jinaai-search"],
"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-search": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-search"
]
}
}
}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
服务器实现了具有可配置参数的单个 MCP 工具：
搜索
使用 Jina.ai 阅读器搜索网页，获取干净、适合法学硕士 (LLM) 学习的内容。返回包含 URL 和干净内容的前 5 条结果。
参数：
query（字符串，必需）：搜索查询format（字符串，可选）：响应格式（“json”或“text”）。默认为“text”no_cache（布尔值，可选）：绕过缓存以获取最新结果。默认为 falsetoken_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
创建变更集：
pnpm changeset对包进行版本控制：
pnpm version构建并发布：
pnpm 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
由Jina.ai 搜索 API提供支持
Available Tools
1 toolsearchB
Search the web and get clean, LLM-friendly content using Jina.ai Reader. Returns top 5 results with URLs and clean content.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| format | No | Response format (json or text) | text |
| no_cache | No | Bypass cache for fresh results | |
| token_budget | No | Maximum number of tokens for this request | |
| browser_locale | No | Browser locale for rendering content | |
| stream | No | Enable stream mode for large pages | |
| gather_links | No | Gather all links at the end of the response | |
| gather_images | No | Gather all images at the end of the response | |
| image_caption | No | Caption images in the content | |
| enable_iframe | No | Extract content from iframes | |
| enable_shadow_dom | No | Extract content from shadow DOM | |
| resolve_redirects | No | Follow redirect chains to final URL |
TDQS
B3.1/5.0
Behavior2/5
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. It mentions the tool returns 'clean, LLM-friendly content' and 'top 5 results with URLs and clean content,' which gives some behavioral context. However, it lacks critical information about rate limits, authentication requirements, error conditions, or what constitutes 'clean' content, leaving significant gaps for a tool with 12 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and front-loaded: a single sentence that communicates the core functionality, method, and output format. Every word earns its place with zero redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 12 parameters and no output schema, the description provides basic purpose and output format but lacks sufficient behavioral context. Without annotations covering safety, limits, or authentication, and with no output schema to explain return values, the description should do more to compensate for these gaps, especially given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all 12 parameters. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the web and get clean, LLM-friendly content using Jina.ai Reader.' It specifies the action (search), resource (web content), and processing method (Jina.ai Reader). However, without sibling tools, it cannot demonstrate differentiation from alternatives, preventing a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, prerequisites, or contextual constraints. It mentions returning 'top 5 results' but doesn't explain when this limitation is appropriate or when other search tools might be better suited.
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
search
TDQS
B3.2/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'search' has a clear and singular purpose, making it impossible for an agent to misselect among non-existent alternatives.
Naming Consistency5/5
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'search' follows a simple verb pattern, which is appropriate and unambiguous for its function.
Tool Count2/5
A single tool is too few for a server named 'MCP JinaAI Search Server', which suggests a broader search functionality scope. While the tool covers basic web search, the server lacks additional tools for advanced operations like filtering, pagination, or domain-specific searches, making it feel thin and under-scoped.
Completeness2/5
The server is severely incomplete for a search domain. It only offers a basic search tool without any supporting operations such as refining queries, handling multiple result pages, or accessing search history. This creates significant gaps that could lead to agent failures when more complex search tasks are required.
Maintenance
ActivityInactive
ResponsivenessNo issues
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LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
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The best web search for your AI Agent
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