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Search Stock News MCP Server

by Cognitive-Stack
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🔌与 Cline、Cursor、Claude Desktop 和任何其他 MCP 客户端兼容！

搜索股票新闻 MCP 与任何 MCP 客户端无缝协作

模型上下文协议 (MCP) 是一种开放标准，使 AI 系统能够与各种数据源和工具无缝交互，促进安全的双向连接。

搜索股票新闻 MCP 服务器提供：

  • 通过 Tavily API 提供实时股票新闻搜索功能

  • 多个可定制的搜索查询模板

  • 可配置的搜索参数和过滤

  • 特定域的内容过滤

  • 使用 TypeScript 进行类型安全操作

先决条件🔧

在开始之前，请确保您已：

  • Tavily API 密钥

  • Claude Desktop、Cursor 或任何兼容 MCP 的客户端

  • Node.js（v16 或更高版本）

  • 已安装 Git（仅在使用 Git 安装方法时才需要）

Related MCP server: Tavily News Search MCP Server

搜索股票新闻 MCP 服务器安装⚡

使用 NPX 运行

npx -y search-stock-news-mcp@latest

通过 Smithery 安装

要通过 Smithery 自动为 Claude Desktop 安装 Search Stock News MCP Server：

npx -y @smithery/cli install search-stock-news-mcp --client claude

配置 MCP 客户端 ⚙️

配置 Cline 🤖

在 Cline 中设置 Search Stock News MCP 服务器的最简单方法是通过市场：

  1. 在 VS Code 中打开 Cline

  2. 点击侧边栏中的 Cline 图标

  3. 导航至“MCP 服务器”选项卡

  4. 搜索“搜索股票新闻”并点击“安装”

  5. 出现提示时，输入您的 Tavily API 密钥

或者，在 Cline 中手动配置服务器：

  1. 打开 Cline MCP 设置文件：

# For macOS:
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

# For Windows:
code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

  1. 添加搜索股票新闻服务器配置：

{
  "mcpServers": {
    "search-stock-news-mcp": {
      "command": "npx",
      "args": ["-y", "search-stock-news-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

配置光标🖥️

要在 Cursor 中设置搜索股票新闻 MCP 服务器：

  1. 打开游标设置

  2. 导航至“功能”>“MCP 服务器”

  3. 点击“+ 添加新的 MCP 服务器”按钮

  4. 填写以下信息：

    • 名称：“search-stock-news-mcp”

    • 类型：“命令”

    • 指令：GXP5

配置 Claude 桌面🖥️

对于 macOS：

touch "$HOME/Library/Application Support/Claude/claude_desktop_config.json"
open -e "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

对于 Windows：

code %APPDATA%\Claude\claude_desktop_config.json

添加服务器配置：

{
  "mcpServers": {
    "search-stock-news-mcp": {
      "command": "npx",
      "args": ["-y", "search-stock-news-mcp@latest"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

使用示例🎯

  1. 基本股票新闻搜索：

{
  "symbol": "AAPL",
  "companyName": "Apple Inc.",
  "maxResults": 10
}

  1. 使用过滤器的高级搜索：

{
  "symbol": "TSLA",
  "companyName": "Tesla Inc.",
  "maxResults": 20,
  "searchDepth": "advanced",
  "minScore": 0.6
}

  1. 自定义域名搜索：

{
  "symbol": "MSFT",
  "companyName": "Microsoft Corporation",
  "includeDomains": ["reuters.com", "bloomberg.com"]
}

故障排除🛠️

常见问题

  1. 未找到服务器

    • 验证 npm 安装

    • 检查配置语法

    • 确保 Node.js 已正确安装

  2. API 密钥问题

    • 验证您的 Tavily API 密钥是否有效

    • 检查配置中的 API 密钥是否正确设置

    • 确保 API 密钥周围没有空格或引号

  3. 搜索结果问题

    • 检查搜索参数是否在有效范围内

    • 验证域名过滤器的格式是否正确

    • 确保公司名称和符号准确

致谢✨

  • MCP 规范的模型上下文协议

  • 克劳德桌面版的 Anthropic

  • Tavily 用于新闻搜索 API

执照

麻省理工学院

Available Tools

2 tools
search-stock-newsC

Search for stock-related news using Tavily API

ParametersJSON Schema
NameRequiredDescriptionDefault
companyNameYesCompany name (e.g., Apple Inc.)
maxResultsNoMaximum number of results to return
minScoreNoMinimum relevance score threshold
searchDepthNoSearch depth level
symbolYesStock symbol (e.g., AAPL)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the Tavily API but doesn't describe key behaviors such as rate limits, authentication needs, error handling, or what the search results include (e.g., headlines, summaries, sources). For a search tool with external API dependencies, this is a significant gap.

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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes the API name for context. Every word earns its place, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of articles with fields), how results are ordered, or any behavioral traits like pagination or API constraints. For a tool with rich input schema but missing output and behavioral context, it should do more.

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 already documents all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples of how parameters interact or typical values). Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('Search') and resource ('stock-related news'), and specifies the API used ('Tavily API'). It distinguishes from the sibling 'general-search' by focusing on stock-related content, though it doesn't explicitly mention this differentiation. The purpose is specific and actionable.

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 like 'general-search', nor does it mention any prerequisites, exclusions, or contextual triggers. It simply states what the tool does without indicating appropriate scenarios or limitations.

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. 2 tool updatesv1.0.0
    • First observedgeneral-search
    • First observedsearch-stock-news
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: 'general-search' is for broad web searches, while 'search-stock-news' is specifically for stock-related news. There is no overlap or ambiguity between them, making it easy for an agent to select the appropriate tool based on the query context.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern with hyphens: 'general-search' and 'search-stock-news'. They use the same naming convention throughout, making the set predictable and readable without any deviations or mixed styles.

Tool Count2/5

With only two tools, the server feels under-scoped for a 'Search Stock News MCP Server'. While the tools cover general and stock-specific searches, the domain suggests potential for more operations like filtering, sorting, or retrieving detailed news, making the count too low for the apparent purpose.

Completeness2/5

The tool surface is significantly incomplete for a stock news server. It lacks essential operations such as filtering news by date, source, or stock ticker, retrieving trending news, or accessing detailed article content. This will likely cause agent failures when trying to perform comprehensive stock news analysis.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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