English | 中文说明
一个轻量级、零依赖的模型上下文协议（MCP）服务器，用于实时网络搜索和网页内容提取。
它能够无缝复用你本地的 CLIProxyAPI (CPA) 实例（http://127.0.0.1:8317）以及已授权的 Google Gemini、xAI Grok、Zhipu GLM 账户，为 AI 编程智能体（Claude Code、Cursor、Codex、Windsurf、Trae）执行结构化的网络研究。
⚡ 零配置、零额外成本：直接使用你现有的本地 CPA 和 Google/Grok/GLM 凭据。
🎯 多引擎支持：
gemini：Google 深度网络搜索与事实核查（默认且推荐）。
glm：Zhipu GLM 中文及技术事实搜索。
grok：xAI 实时网络与 X/Twitter 搜索。
duckduckgo：无需任何 API 密钥的免费公共搜索引擎回退方案。
auto：自动选择请求引擎并支持故障转移。
🌐 网页内容提取：内置 fetch_webpage 工具，可获取网页并去除原始 HTML，转换为清晰可读的文本。
🔌 通用兼容性：适用于 Claude Code、Cursor、Codex、Windsurf、Trae 以及任何标准 MCP 客户端。
🚀 零依赖：纯 Node.js ESM 标准库实现，启动快、内存占用极小。
Related MCP server: DuckDuckGo MCP Server
在终端中运行以下命令：
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"验证状态：
claude mcp list在 MCP 配置文件（例如 claude_desktop_config.json 或 Cursor MCP 设置）中添加以下内容：
{
"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在网络上搜索最新的事实、文档和技术资讯。
参数：
query（字符串，必填）：搜索查询或研究主题。
engine（字符串，可选）：gemini | glm | grok | duckduckgo | auto
fetch_webpage从任意 URL 获取并提取干净的文本内容。
参数：
url（字符串，必填）：网页 URL（http/https）。
Variable | Default | Description |
|
| 本地 CPA 代理地址 |
| (内置本地密钥) | CPA 认证密钥 |
|
| 搜索使用的默认 AI 模型 |
|
| 出站网络代理 |
运行自动化端到端测试套件：
node test.mjsMIT License © 2026 ffffh5
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。
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