Dappier MCP Server

What does “Dappier” MCP Server do?

Dappier MCP Server is a tool designed to connect AI assistants with real-time web search capabilities and access to premium data from trusted media brands, covering news, financial markets, sports, entertainment, and weather. By acting as a bridge between AI clients and diverse external data sources or APIs, Dappier MCP enables powerful AI agents to be built for various development workflows. These agents can enhance productivity by automating data retrieval, analysis, and integration tasks, allowing for seamless database queries, real-time information lookups, and rich context augmentation for language models.

List of Prompts

No information about prompt templates was found in the provided repository or documentation.

List of Resources

No explicit resources (as defined by MCP) are listed in the available documentation or repository files.

List of Tools

No explicit tools or tool definitions were found in the repository files or documentation.

Use Cases of this MCP Server

• Real-time Web Search: Instantly retrieve up-to-date information from the internet, enabling AI agents to answer queries with the latest news or data.
• Financial Market Data: Access live market data to empower financial analysis tools and agents.
• News Aggregation: Aggregate and summarize news from multiple premium sources, useful for research assistants and journalism tools.
• Weather Data Access: Provide current weather conditions and forecasts for use in planning or travel assistants.
• Entertainment & Sports Data: Extract real-time scores, schedules, and entertainment news for engagement bots or dashboards.

How to set it up

Windsurf

No setup instructions for Windsurf are provided in the current documentation.

Claude

1. Obtain your Dappier API Key from Dappier Platform.
2. Install uv (Python package manager):
   • 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. Update your Claude configuration file (claude_desktop_config.json) with the following:

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

4. Save the configuration file:
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
5. Restart Claude Desktop and verify the Dappier MCP server is accessible.

Cursor

No setup instructions for Cursor are provided in the current documentation.

Cline

No setup instructions for Cline are provided in the current documentation.

Securing API Keys (Environment Variables)

You can secure your API key using the env field in your configuration:

"env": {
  "DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-name": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Roots support: Not specified
Sampling support: Not specified

Based on the tables above, Dappier MCP provides a clear overview and solid setup instructions for Claude Desktop, but lacks detailed information about prompts, resources, tools, and feature support specifics in the public documentation. It is best suited for users seeking web and data integration but not for those requiring deep MCP extensibility or explicit workflow templates.

MCP Score

Overall, I would rate this MCP a 4/10:
While Dappier MCP is promising for real-time data access and provides basic setup guidance, it currently lacks transparency and documentation about MCP primitives (tools, resources, prompts), which limits its utility for advanced MCP integrations and developer adoption.