Dappier MCP Server

Dappier MCP Server

By DappierAI GitHub

Dappier MCP server connects any AI to proprietary, real-time data — including web search, news, sports, stock market data, and premium publisher content.

Overview

What is Dappier MCP Server?

Dappier MCP Server is a Model Context Protocol server that connects any AI to real-time, rights-cleared, proprietary data from trusted sources, enabling AI to access specialized models for various data types including web search, news, sports, and financial data.

How to use Dappier MCP Server?

To use Dappier MCP Server, sign up for an API key at Dappier, install the server using pip, and configure it with your API key in the Claude Desktop configuration file.

Key features of Dappier MCP Server?

  • Real-Time Web Search for the latest information.
  • Access to Stock Market Data with AI insights.
  • AI-Powered Recommendations for personalized content.
  • Structured JSON Responses for rich article metadata.
  • Flexible customization options for data models and search algorithms.

Use cases of Dappier MCP Server?

  1. Retrieving real-time weather updates.
  2. Accessing the latest financial news and stock prices.
  3. Getting personalized content recommendations in various domains.

FAQ from Dappier MCP Server?

  • Can I use Dappier MCP Server for any AI model?

Yes! Dappier MCP Server is designed to connect with any LLM or Agentic AI.

  • Is there a cost associated with using Dappier MCP Server?

You can sign up for an API key, and usage may depend on the data accessed.

  • How do I debug the Dappier MCP Server?

You can run the MCP inspector to debug the server.

Content

Dappier MCP Server

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A Model Context Protocol (MCP) server that connects any LLM or Agentic AI to real-time, rights-cleared, proprietary data from trusted sources. Dappier enables your AI to become an expert in anything by providing access to specialized models, including Real-Time Web Search, News, Sports, Financial Stock Market Data, Crypto Data, and exclusive content from premium publishers. Explore a wide range of data models in our marketplace at marketplace.dappier.com.

Features

  • Real-Time Web Search: Access real-time Google web search results, including the latest news, weather, stock prices, travel, deals, and more.
  • Stock Market Data: Get real-time financial news, stock prices, and trades from Polygon.io, with AI-powered insights and up-to-the-minute updates.
  • AI-Powered Recommendations: Personalized content discovery across Sports, Lifestyle News, and niche favorites like I Heart Dogs, I Heart Cats, Green Monster, WishTV, and many more.
  • Structured JSON Responses: Rich metadata for articles, including titles, summaries, images, and source URLs.
  • Flexible Customization: Choose from predefined data models, similarity filtering, reference domain filtering, and search algorithms.

Tools

  • Name: dappier_real_time_search
  • Description: Retrieves direct answers to real-time queries using AI-powered search. This includes web search results, financial information, news, weather, stock market updates, and more.
  • Parameters:
    • query (string, required): The user-provided input string for retrieving real-time data.
    • ai_model_id (string, optional): The AI model ID to use for the query. Defaults to am_01j06ytn18ejftedz6dyhz2b15 (Real-Time Data).

2. AI Recommendations

  • Name: dappier_ai_recommendations
  • Description: Provides AI-powered content recommendations based on structured data models. Returns a list of articles with titles, summaries, images, and source URLs.
  • Parameters:
    • query (string, required): The user-provided input string for AI recommendations.
    • data_model_id (string, optional): The data model ID to use for recommendations. Defaults to dm_01j0pb465keqmatq9k83dthx34 (Sports News).
    • similarity_top_k (integer, optional): The number of top documents to retrieve based on similarity. Defaults to 9.
    • ref (string, optional): The site domain where AI recommendations should be displayed. Defaults to None.
    • num_articles_ref (integer, optional): The minimum number of articles to return from the specified reference domain (ref). Defaults to 0.
    • search_algorithm (string, optional): The search algorithm to use for retrieving articles. Options: most_recent, semantic, most_recent_semantic, trending. Defaults to most_recent.

Setup Instructions

Installing via Smithery

To install dappier-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @DappierAI/dappier-mcp --client claude

1. Get Dappier API Key

Head to Dappier to sign up and generate an API key.

2. Install Dependencies

Install uv first.

MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Install Dappier MCP Server

pip install dappier-mcp

Or if you have uv installed:

uv pip install dappier-mcp

4. Configure Claude Desktop

Update your Claude configuration file (claude_desktop_config.json) with the following content:

{
  "mcpServers": {
    "dappier": {
      "command": "uvx",
      "args": ["dappier-mcp"],
      "env": {
        "DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Configuration file location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Examples

  • Query: "How is the weather today in Austin, TX?"
  • Query: "What is the latest news for Meta?"
  • Query: "What is the stock price for AAPL?"

AI Recommendations

  • Query: "Show me the latest sports news."
  • Query: "Find trending articles on sustainable living."
  • Query: "Get pet care recommendations from IHeartDogs AI."

Debugging

Run the MCP inspector to debug the server:

npx @modelcontextprotocol/inspector uvx dappier-mcp

Contributing

We welcome contributions to expand and improve the Dappier MCP Server. Whether you want to add new search capabilities, enhance existing functionality, or improve documentation, your input is valuable.

For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers

Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements.

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