Which Base URL should I use for my account?

If you are using the international version, use `https://api.coze.com`. For the Chinese version, use `https://api.coze.cn`.

Can I automatically list all published bots in a space?

Yes! Use the `list_bots` tool with your Space ID. Your agent will return a list of all bots that have been published and are ready for interaction.

How do I upload a new document to my knowledge base?

Use the `upload_document` tool with the target Dataset ID and the raw text content. Your agent will handle the ingestion process into the Coze RAG engine.

Coze MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Coze
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

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

    result = await agent.run(
        "What tools are available in Coze?"
    )
    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 Coze MCP Server

Connect your AI agents to Coze (扣子), the advanced bot orchestration platform by ByteDance. This MCP provides 11 tools to manage the full lifecycle of your bots, from chat interactions to knowledge base document ingestion.

Pydantic AI validates every Coze tool response against typed schemas, catching data inconsistencies at build time. Connect 11 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

Bot Interaction — Chat with published bots and handle multi-turn conversations with persistent history
Knowledge Engineering — Upload, list, and delete documents in knowledge base datasets for RAG optimization
Workspace Management — List available spaces and published bots to monitor your AI ecosystem
Action Handling — Submit tool outputs when bots require human-in-the-loop or external plugin results

The Coze MCP Server exposes 11 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 Coze to Pydantic AI via MCP

Follow these steps to integrate the Coze 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 11 tools from Coze with type-safe schemas

Why Use Pydantic AI with the Coze MCP Server

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

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

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

Coze + Pydantic AI Use Cases

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

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

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

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

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

Coze MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Coze to Pydantic AI via MCP:

clear_conversation

Clear all messages from a conversation session

create_chat

Send a message to a Coze bot and get a response

delete_document

Delete documents from a dataset by ID

get_conversation_history

Retrieve the message list from a conversation

list_bots

List published bots in a specific Coze Space

list_datasets

List knowledge base datasets in a Coze Space

list_workspaces

List available Coze workspaces/spaces

publish_bot

Publish a Coze Bot draft

submit_tool_outputs

Submit outputs for tools/plugins required by the bot

upload_document

Upload a raw text document to a Knowledge Base

upload_file_url

Upload an external file URL to Coze storage

Example Prompts for Coze in Pydantic AI

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

"Chat with bot 'bot_123' and ask 'Tell me about the history of Tokyo'."

"List all active workspaces in my Coze account."

"Upload the content of 'manual.txt' to dataset 'ds_999'."

Troubleshooting Coze MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Coze + Pydantic AI FAQ

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