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 CrewAI 14 tools — connect in under 2 minutes

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

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 14 Tools Framework

Connect your CrewAI agents to Dify.AI SDK through Vinkius, pass the Edge URL in the `mcps` parameter and every Dify.AI SDK tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Dify.AI SDK Specialist",
    goal="Help users interact with Dify.AI SDK effectively",
    backstory=(
        "You are an expert at leveraging Dify.AI SDK tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Dify.AI SDK "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 14 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Dify.AI SDK becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Dify.AI SDK tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 14 tools from Dify.AI SDK

Why Use CrewAI with the Dify.AI SDK MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Dify.AI SDK through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Dify.AI SDK + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Dify.AI SDK for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Dify.AI SDK, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Dify.AI SDK tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Dify.AI SDK against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Dify.AI SDK MCP Tools for CrewAI (14)

These 14 tools become available when you connect Dify.AI SDK to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Dify.AI SDK + CrewAI FAQ

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

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.