What are the prerequisites for using mcp-server-dify?
You need a Dify AI API endpoint URL and an API key. Either Docker (to run the container) or Node.js (to use npx) is required.
@yuru-sha
Basic information
Sample MCP Server for Dify AI
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-dify": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"mcp/dify",
"https://your-dify-api-endpoint",
"your-dify-api-key"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
mcp-server-dify is a Model Context Protocol server that enables LLMs to interact with Dify AI’s chat completion API. Written in TypeScript, it provides a standardized interface for chat, streaming responses, and conversation context.
Install via Docker (make docker then docker run -i --rm mcp/dify <endpoint> <key>) or run directly with npx. Configure it in Claude Desktop by adding the endpoint and API key to your claude_desktop_config.json. Invoke its tools, such as the meshi-doko tool for restaurant recommendations, by supplying the required parameters (LOCATION, BUDGET, query, optional conversation_id).
You need a Dify AI API endpoint URL and an API key. Either Docker (to run the container) or Node.js (to use npx) is required.
Build the image with `make docker`, then run: `docker run -i --rm mcp/dify <your-dify-api-endpoint> <your-dify-api-key>`.
It is a restaurant recommendation tool that interfaces with Dify AI. It accepts a location, budget, query, and optional conversation ID to maintain context across calls.
Yes, streaming response support is listed as a feature.
Keep your API key private, use HTTPS for the endpoint, and never commit the key to version control.

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