Does this work with self-hosted Dify?

Yes, simply replace the Base URL credential with your own domain (e.g. `https://dify.mycompany.com/v1`).

Dify.AI SDK MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: Dify.AI SDK
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dify.AI SDK through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Dify.AI SDK "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Dify.AI SDK?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Dify.AI SDK MCP Server

Connect your Vinkius agents directly to Dify.AI, the leading open-source LLM app development platform. With 10 exposed tools, you can execute complex Dify workflows, send messages to specialized chatbots, retrieve session histories, and submit model feedback for RLHF.

Pydantic AI validates every Dify.AI SDK tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Agent Chat — Send messages to published Dify chatbots and track conversations
Workflows — Trigger background Dify workflows with dynamic JSON parameters
Session Management — Rename, fetch, or delete conversation histories
Audit & Feedback — Programmatically submit 'like/dislike' ratings to improve model tuning

The Dify.AI SDK MCP Server exposes 14 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Dify.AI SDK to Pydantic AI via MCP

Follow these steps to integrate the Dify.AI SDK MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 14 tools from Dify.AI SDK with type-safe schemas

Why Use Pydantic AI with the Dify.AI SDK MCP Server

Pydantic AI provides unique advantages when paired with Dify.AI SDK through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Dify.AI SDK integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Dify.AI SDK connection logic from agent behavior for testable, maintainable code

Dify.AI SDK + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Dify.AI SDK MCP Server delivers measurable value.

Type-safe data pipelines: query Dify.AI SDK with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Dify.AI SDK tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Dify.AI SDK and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Dify.AI SDK responses and write comprehensive agent tests

Dify.AI SDK MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect Dify.AI SDK to Pydantic AI via MCP:

chat_message

Send a chat message to a Dify Application

delete_conversation

Delete a Dify conversation

get_app_meta

Get application meta data configuration

get_conversation_messages

Get historical messages of a specific Dify conversation

get_conversations

List recent conversations for a user

get_suggested_questions

Use after receiving a chat response. Get next suggested questions for a message

get_workflow_info

Get basic App information

get_workflow_parameters

Get required application parameters

rename_conversation

Rename a Dify conversation

run_workflow

Execute a Dify Workflow application

send_completion

Returns the full generated text. Send a text completion request to a Dify completion app

stop_chat_generation

Only supported for streaming mode responses. Stop an in-progress chat message generation

submit_feedback

Submit feedback (like/dislike) for a message

upload_file

Upload a file via URL for multimodal understanding

Example Prompts for Dify.AI SDK in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Dify.AI SDK immediately.

"Check my recent Dify conversations and tell me the name of the last one."

Troubleshooting Dify.AI SDK MCP Server with Pydantic AI

Common issues when connecting Dify.AI SDK to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dify.AI SDK + Pydantic AI FAQ

Common questions about integrating Dify.AI SDK MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Dify.AI SDK MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.