英語 | 中国語の説明
軽量で、依存関係のない Model Context Protocol (MCP) サーバー。リアルタイムのウェブ検索とウェブページのコンテンツ抽出を提供します。
既存のローカル CLIProxyAPI (CPA) インスタンス（http://127.0.0.1:8317）と、承認済みの Google Gemini、xAI Grok、Zhipu GLM アカウントをシームレスに再利用して、AI コーディングエージェント（Claude Code、Cursor、Codex、Windsurf、Trae）向けの構造化されたウェブリサーチを実行できます。
⚡ セットアップ不要 & 追コストなし: 既存のローカル CPA と Google/Grok/GLM 認証情報を直接そのまま使用します。
🎯 マルチエンジン対応:
gemini: Google のディープウェブ検索とファクトチェック（デフォルト & 推奨）。
glm: Zhipu GLM による中国語・技技術系ファクト検索。
grok: xAI によるリアルタイムのウェブ & X/Twitter 検索。
duckduckgo: API キーなしで利用できる無料の公開検索エンジンへのフォールバック。
auto: エンジンを英知的に自動ルーティングし、フェイルオーバするモード。
🌐 ウェブページコンテンツ抽出: 内蔵の fetch_webpage ツールで生 HTML を取得し、余分なタグを除去して読めるテキストを抽出します。
🔌 ユニバーサル互換性: Claude Code、Cursor、Codex、Windsurf、Trae をはじめ、あらゆる標準 MCP クライアントで動作します。
🚀 依存関係なし: 純粋な Node.js ESM 標準ライブラリによる実装。起動が即時、メモリ専有量も最小限です。
Related MCP server: DuckDuckGo MCP Server
ターミナルで以下のコマンドを実行します:
claude mcp add --scope user cpa-search node "C:/Users/aa182/Documents/Codex/2026-08-22/new-chat-2/repositories/cpa-search-mcp/index.mjs"状態を確認します:
claude mcp listMCP 設定ファイル（例: claude_desktop_config.json や Cursor MCP 設定）に以下を追加します:
{
"mcpServers": {
"cpa-search": {
"command": "node",
"args": [
"C:/Users/aa182/Documents/Codex/2026-08-22/new-chat-2/repositories/cpa-search-mcp/index.mjs"
]
}
}
}cpa_search最新の事実、ドキュメント、技術ニュースをウェブで検索します。
パラメータ:
query (string, 必須): 検索クエリまたは調査トピック。
engine (string, 任意): gemini | glm | grok | duckduckgo | auto。
fetch_webpage任意のURLからHTMLを取得し、クリーンなテキストを抽出します。
パラメタ:
url (string, 必須): ウェブページのURL (http/https)。
変数 | デフォルト | 説明 |
|
| ローカル CPA プロキンのアドレス |
| (Built-in のローカルキー) | CPA 認証キー |
|
| 検索に使用するデフォルト AIモデル |
|
| 外行きネットワークプロキシ |
自動化されたエンドツーエンドのテストスイートを実行します:
node test.mjsMIT License © 2026 ffffhx
使用本地已登录的 Gemini / Grok / GLM 账号进行实时全网搜索，获取最新技术动态、文档与事实总结，并附带权威来源。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 搜索关键词或想要查询的最新信息 | |
| engine | No | 指定搜索引擎后端：gemini (Google 检索，默认推荐)、glm (智谱中文检索)、grok (xAI实时检索)、duckduckgo (公共搜索)、auto (默认自动智能选择) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses a key prerequisite (using locally logged-in accounts) and notes real-time retrieval with authoritative sources. However, it does not mention potential failures (e.g., no logged-in accounts), rate limits, or result formatting, leaving gaps.
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, concise sentence that front-loads the core function (real-time web search) and adds relevant context (accounts, sources) without extraneous words. It is optimally sized.
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?
Despite lacking an output schema, the description hints at return content (tech trends, documents, fact summaries, sources) and the prerequisite of logged-in accounts. It does not explain error handling or pagination, but these are less critical for a simple search tool. Overall, it provides sufficient context for an agent to call it correctly.
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%, and the description does not add significant meaning beyond the schema. It mentions using logged-in accounts, which links to the 'engine' parameter, but the schema already explains each backend option. The description adds minimal value for parameters.
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 explicitly states the tool performs real-time web-wide search ('实时全网搜索'), retrieves latest tech trends, documents, and fact summaries with authoritative sources. This clearly distinguishes it from the sibling 'fetch_webpage', which fetches a specific page.
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 implies usage for searching rather than fetching specific pages, but it does not explicitly state when to use this tool over 'fetch_webpage' or provide exclusion criteria. The context of 'search' vs 'fetch' is implicit but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
直接抓取指定 URL 网页的完整正文内容并转换为易读的文本
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | 需要抓取的完整网页网址（例如 https://docs.example.com） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavior. It mentions fetching the 'complete main content' and converting to text, but omits any mention of handling dynamic content, authentication, timeouts, or error cases. This is a significant gap for a web-fetching tool.
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 sentence with no unnecessary words. It is concise and gets to the point, though it could slightly benefit from structuring key constraints more explicitly.
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 a single parameter and no output schema, the description should cover potential failure modes and limitations. It lacks any mention of what happens on failure, unsupported URL types, or rate limits. Given the lack of annotations, this is incomplete for safe agent use.
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 schema description coverage is 100%, so the url parameter is already well-documented. The description does not add extra semantic meaning beyond what the schema provides, matching 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 verb 'fetch' and the resource 'webpage', and adds that it converts content to readable text. It is specific enough, though it doesn't explicitly contrast with sibling tool cpa_search, which appears to be a different operation.
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 given on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description simply states what it does, leaving the agent to infer when it is appropriate.
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.
v1.0.0cpa_searchfetch_webpageScored across 2 tools
两个工具功能明确分离：cpa_search用于搜索并返回结果，fetch_webpage用于抓取特定URL的内容。没有重叠或模糊边界，代理可以轻松区分何时使用哪个工具。
两个工具均使用snake_case，且都包含动作词（search, fetch），但cpa_search以名词cpa开头，而fetch_webpage以动词开头，存在轻微不一致。不过整体模式简单且可预测，不影响理解。
仅2个工具对于搜索服务器而言显得单薄，但结合其功能（搜索+网页抓取）已覆盖核心需求。未超出合理范围，但处于评分的下限。
搜索和网页抓取共同构成完成查询-获取详情的基本流程，没有明显缺失的关键操作。但缺少如历史记录、批量处理等扩展功能，仍有小幅度提升空间。
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Web search, browser automation, scraping, crawling and CAPTCHA solving for AI agents.
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
Fetch pages as markdown, search web and news, extract structured data. For AI agents.
