Kagi MCP server
kagi-server MCP 服务器
用于 Kagi API 集成的 MCP 服务器
这是一个基于 TypeScript 的 MCP 服务器,集成了 Kagi Search API。它通过以下方式演示了 MCP 的核心概念:
使用 Kagi 的 API 执行网络搜索和其他操作的工具(目前处于私人测试阶段)
特征
已实施的工具
kagi_search- 使用 Kagi 进行网络搜索采用查询字符串和可选限制作为参数
从 Kagi 的 API 返回搜索结果
计划中的工具(尚未实施)
kagi_summarize- 生成网页或文本的摘要kagi_fastgpt- 使用 Kagi 的 FastGPT 获得快速响应kagi_enrich- 获取特定主题的丰富新闻结果
Related MCP server: Kagi MCP Server
发展
安装依赖项:
npm install构建服务器:
npm run build对于使用自动重建的开发:
npm run watch环境设置
使用您的 Kagi API 密钥在根目录中创建一个.env文件:
KAGI_API_KEY=your_api_key_here确保将.env添加到您的.gitignore文件中以确保您的 API 密钥安全。
安装
通过 Smithery 安装
要通过Smithery自动为 Claude Desktop 安装 Kagi 服务器:
npx @smithery/cli install kagi-server --client claude要与 Claude Desktop 一起使用,请添加服务器配置:
在 MacOS 上: ~/Library/Application Support/Claude/claude_desktop_config.json在 Windows 上: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"kagi-server": {
"command": "/path/to/kagi-server/build/index.js",
"env": {
"KAGI_API_KEY": "your_api_key_here"
}
}
}
}调试
由于 MCP 服务器通过 stdio 进行通信,调试起来可能比较困难。我们推荐使用MCP Inspector ,它以包脚本的形式提供:
npm run inspector检查器将提供一个 URL 来访问浏览器中的调试工具。
用法
一旦服务器运行并连接到 Claude Desktop,您就可以使用它进行网页搜索。例如:
问克劳德:“你能搜索有关量子计算最新进展的信息吗?”
Claude 将使用
kagi_search工具从 Kagi 的 API 中获取结果。然后,Claude 将为您总结或分析搜索结果。
注意:计划中的工具(summarize、fastgpt、enrich)尚未实现,无法使用。
贡献
欢迎贡献代码!请随时提交 Pull 请求。贡献代码的领域包括:
实施计划中的工具(总结、fastgpt、丰富)
改进错误处理和输入验证
增强文档和使用示例
执照
该项目已获得 MIT 许可。
路线图
实现
kagi_summarize工具用于网页和文本摘要实施
kagi_fastgpt工具以实现快速响应实现
kagi_enrich工具来获取丰富的新闻结果改进错误处理并添加更强大的输入验证
添加更全面的使用示例和文档
将软件包发布到 npm,以便轻松安装并使用 Claude Desktop 和 npx
Available Tools
1 toolkagi_searchC
Perform web search using Kagi
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Perform web search' which implies read-only behavior, but doesn't disclose any behavioral traits like rate limits, authentication needs, response format, or potential side effects. This leaves significant gaps for a tool with external dependencies.
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 with zero waste. It's front-loaded with the core purpose and efficiently communicates the essential function without unnecessary details.
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 no annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't address behavioral aspects, parameter usage, or result expectations, making it inadequate for a tool that interacts with an external web search service.
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 0%, so the description must compensate. It adds no meaning beyond the schema—doesn't explain what 'query' should contain, how 'limit' affects results, or any parameter nuances. The schema defines types and constraints, but the description offers no semantic context.
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 ('Perform web search') and the resource/service ('using Kagi'), which is specific and unambiguous. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.
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, prerequisites, or any contextual limitations. It simply states what the tool does without offering usage instructions.
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
kagi_search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as performing web searches using Kagi, leaving no room for misselection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'kagi_search' follows a clear verb_noun pattern, but consistency cannot be assessed across a set of one.
A single tool is too few for a server that appears to be focused on web search functionality, as it lacks complementary operations like summarization, filtering, or handling search results. This minimal scope feels incomplete for the domain.
The tool surface is severely incomplete for a web search domain, as it only provides raw search capability without tools for processing, refining, or managing search results. This creates significant gaps that will limit agent effectiveness.
Maintenance
Related MCP Connectors
Web search, AI agent, and content extraction via You.com APIs
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
Web search and page-reading for AI agents. One-click OAuth connect, or a Caesar API key.
Search your knowledge bases from any AI assistant using hybrid RAG.
Related MCP Servers
- AlicenseCqualityDmaintenanceAllows the use of Kagi's API for web searching and content enrichment through methods like fastgpt, enrich/web, and enrich/news.32MIT
- AlicenseNot gradedqualityDmaintenanceEnables integration with Kagi search engine services including web search, content summarization from URLs, and AI assistant conversations. Uses session tokens to access Kagi's search API, summarizer, and AI models directly within MCP-compatible applications.10 npm3MIT
- AlicenseAqualityDmaintenanceProvides free web search and URL summarization using Kagi session tokens, compatible with any MCP client.284MIT
- AlicenseNot gradedqualityDmaintenanceProvides web search capabilities to AI assistants using the Kagi search API, enabling parallel queries and formatted results.20 npm3MIT