Can my agent run a prediction against a specific Flowise chatflow?

Yes. Use the 'predict' tool. Provide the 'chatflow_id' and your question. The agent will command the Flowise backend to process the logic chain and return the AI-generated response directly in your chat.

How do I see the past conversational logs for a chatflow via chat?

Use the 'get_history' tool with the 'chatflow_id'. Your agent will retrieve the past execution traces and logs, helping you understand how users have interacted with that specific logic chain natively.

Can I list all custom tools configured in my Flowise instance through the agent?

Absolutely. Use the 'list_tools' tool. Your agent will retrieve custom tools and integrations configured in your environment, allowing you to audit available capabilities through natural conversation.

Flowise MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Flowise
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Flowise through the 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 Flowise "
            "(7 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Flowise MCP Server

Connect your FlowiseAI instance to any AI agent and take full control of your low-code generative AI application development through natural conversation.

Pydantic AI validates every Flowise tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through the 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

Chatflow Orchestration — List and retrieve detailed architectural nodes and edges for all deployed Chatflows within your Flowise instance natively
Agentic Workflow Control — Access compound Agentflows defining complex AI tasks and multi-step reasoning logic synchronously
Live AI Prediction — Commands the backend to submit user questions to specific Chatflows and retrieve generated AI responses in real-time
Execution History Auditing — Pull precise past execution traces and conversational logs to debug logic chains and monitor agent performance limitlessly
Tool & Integration Discovery — Retrieve custom tools and third-party integrations configured in your Flowise environment to verify available capabilities
Credential Oversight — Enumerate stored credential components used to authenticate your AI logic chains securely within the platform
System Health Monitoring — Verify instance status and available base endpoints to ensure your AI orchestration layer is operational

The Flowise MCP Server exposes 7 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 Flowise to Pydantic AI via MCP

Follow these steps to integrate the Flowise 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 7 tools from Flowise with type-safe schemas

Why Use Pydantic AI with the Flowise MCP Server

Pydantic AI provides unique advantages when paired with Flowise 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 Flowise integration code

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

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

Flowise + Pydantic AI Use Cases

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

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

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

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

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

Flowise MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Flowise to Pydantic AI via MCP:

get_chatflow

Get chatflow details

get_history

Get chat execution history

list_agentflows

List agentflows

list_chatflows

List chatflows

list_credentials

List credentials

list_tools

List available tools

predict

Run prediction on chatflow

Example Prompts for Flowise in Pydantic AI

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

"Ask chatflow 'abc-123': 'Summarize this document: [Context]'"

"List all active chatflows in my instance"

"Show me the execution history for chatflow 'Legal-Assistant'"

Troubleshooting Flowise MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Flowise + Pydantic AI FAQ

Common questions about integrating Flowise 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 Flowise MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.