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FlowiseAI MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Execute Chatflow Prediction, Get Chatflow Details, Get Server Version, and more

Built by Vinkius GDPR 12 Tools SDK

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

Ask AI about this App Connector for Pydantic AI

The FlowiseAI app connector for Pydantic AI is a standout in the Friends Mcp category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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 FlowiseAI "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())
FlowiseAI
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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 FlowiseAI MCP Server

Connect your FlowiseAI (self-hosted) instance to any AI agent and take full control of your LLM orchestration and RAG workflows through natural conversation.

Pydantic AI validates every FlowiseAI tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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

  • Prediction Orchestration — Trigger specific chatflows and retrieve LLM-generated responses programmatically using natural language inputs
  • Chatflow Management — List all orchestration flows and retrieve detailed technical structures and metadata to monitor your AI agents
  • Vector Intelligence — Programmatically upsert documents or raw data into the vector stores linked to your chatflows to ensure high-fidelity context
  • Component Oversight — Access server-wide credentials, custom tools, and global variables to manage your complete Flowise ecosystem
  • Operational Visibility — Monitor user feedback, leads, and assistant profiles directly through your agent for instant reporting

The FlowiseAI MCP Server exposes 12 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.

All 12 FlowiseAI tools available for Pydantic AI

When Pydantic AI connects to FlowiseAI through Vinkius, your AI agent gets direct access to every tool listed below — spanning llm-workflows, rag-pipelines, chatbot-development, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

execute_chatflow_prediction

Trigger an LLM flow prediction

get_chatflow_details

Get details for a specific chatflow

get_server_version

Get Flowise server version

list_ai_assistants

List OpenAI-style assistants

list_chat_feedback

List user feedback for a chatflow

list_chatflows

List all LLM orchestration flows

list_external_tools

List custom tools

list_flow_leads

List captured leads

list_flow_variables

List global variables

list_flowise_credentials

List configured credentials

list_marketplace_templates

List chatflow templates

upsert_vector_data

Push data into a vector store

Connect FlowiseAI to Pydantic AI via MCP

Follow these steps to wire FlowiseAI into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 12 tools from FlowiseAI with type-safe schemas

Why Use Pydantic AI with the FlowiseAI MCP Server

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

01

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

02

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

03

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

04

Dependency injection system cleanly separates your FlowiseAI connection logic from agent behavior for testable, maintainable code

FlowiseAI + Pydantic AI Use Cases

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

01

Type-safe data pipelines: query FlowiseAI with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple FlowiseAI tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query FlowiseAI and output structured, schema-compliant notifications

04

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

Example Prompts for FlowiseAI in Pydantic AI

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

01

"List all my chatflows in Flowise."

02

"Execute chatflow 'cf_1' with question: 'How do I reset my password?'"

03

"Upsert this data into vector store for chatflow 'cf_2': [data]"

Troubleshooting FlowiseAI MCP Server with Pydantic AI

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

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

FlowiseAI + Pydantic AI FAQ

Common questions about integrating FlowiseAI MCP Server with Pydantic AI.

01

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.
02

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.
03

Can I switch LLM providers without changing MCP code?

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