How do I chat with a specific bot?

Use the `send_bot_message` tool and provide the Bot ID and your message content. The AI agent will relay your message to the GPTBots platform and return the bot's response.

Can I check the status of a triggered workflow?

Yes. When you use `trigger_workflow`, it returns a Record ID. You can then pass that Record ID to the `query_workflow` tool to monitor its execution status (e.g., Running, Success, Failed).

GPTBots MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: GPTBots
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect GPTBots 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 GPTBots "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

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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 GPTBots MCP Server

Connect your GPTBots account to your AI agent and manage your enterprise AI infrastructure conversationally. Interact with your deployed bots, trigger complex automated workflows, and upload new documents to your knowledge bases without leaving your development environment.

Pydantic AI validates every GPTBots tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Bot Interaction — List active conversations, review chat histories, and send messages directly to your deployed AI agents
Knowledge Management — Browse available knowledge documents and upload new content to keep your bots' context up to date
Workflow Automation — Trigger configured AI workflows and query their execution status programmatically
Database Queries — List tables and records hosted within the GPTBots platform database

The GPTBots MCP Server exposes 8 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 GPTBots to Pydantic AI via MCP

Follow these steps to integrate the GPTBots 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 8 tools from GPTBots with type-safe schemas

Why Use Pydantic AI with the GPTBots MCP Server

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

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

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

GPTBots + Pydantic AI Use Cases

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

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

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

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

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

GPTBots MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect GPTBots to Pydantic AI via MCP:

create_knowledge_document

Upload or create a document in the Knowledge Base

get_conversation

Get details and history of a specific conversation

list_conversations

List chat conversations with a bot

list_databases

List tables in the platform database

list_knowledge_documents

List documents in a Knowledge Base

query_workflow

Check the execution status of a triggered workflow

send_bot_message

Send a message to a GPTBots Agent

trigger_workflow

Trigger an automated workflow

Example Prompts for GPTBots in Pydantic AI

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

"List all recent conversations for bot ID 'bot_123xyz'."

"Trigger the onboarding workflow (ID: 'wf_456') and pass the parameter email='test@example.com'."

Troubleshooting GPTBots MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

GPTBots + Pydantic AI FAQ

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