How do I securely obtain my API Key?

Installation in Pumble is unique. In your workspace, click **+ Add apps** in the left sidebar, find the **API** addon, and install it. After doing so, go to any chat input box in Pumble and type `/api-keys generate`. A private, ephemeral message will appear containing your secret token. Paste that token here.

Can my AI automatically reply to unread issues?

Yes. You can instruct your agent to regularly fetch messages from a specific channel (`chat_history_messages`). The agent can evaluate questions, generate answers based on your knowledge base, and then use `chat_post_message` to post the solution back to the team instantly.

Is it possible for the AI to react with emojis?

Absolutely. It uses the `chat_add_reaction` functionality. A common use case is having the agent process a batch of tickets reported in Pumble, and instruct it to leave a '✅' checkmark reaction on the original message to signify that work is complete.

What happens if a bot message has a typo?

Unlike standard webhooks, this integration provides full bidirectional control. If your AI posted something incorrect, simply ask it to update its previous message. It will fetch its message ID and push a new payload using the `chat_update_message` tool instantly without leaving double messages.

Pumble MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Pumble
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

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

Connect your Pumble workspace to any AI agent and bring powerful automation directly to your team's communication hub.

Pydantic AI validates every Pumble tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Read & Manage Channels — List all public and private channels, fetch detailed metadata, and dynamically create new discussion channels on the fly
Message Operations — Retrieve conversation histories, post new messages, update typos, or delete outdated announcements seamlessly
Interactive Reactions — Add emoji reactions to messages automatically to acknowledge requests without cluttering the chat
User Directory — List all workspace users and pull detailed profiles (including emails and time zones) to ensure accurate tagging

The Pumble MCP Server exposes 10 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 Pumble to Pydantic AI via MCP

Follow these steps to integrate the Pumble 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 10 tools from Pumble with type-safe schemas

Why Use Pydantic AI with the Pumble MCP Server

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

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

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

Pumble + Pydantic AI Use Cases

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

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

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

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

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

Pumble MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Pumble to Pydantic AI via MCP:

chat_add_reaction

Adds an emoji reaction to a message

chat_delete_message

This action is irreversible. Deletes a message from a Pumble channel

chat_history_messages

Retrieves recent messages from a channel

chat_post_message

Specify the channel ID and the message text. Sends a message to a Pumble channel

chat_update_message

Updates a pre-existing message

create_chat_channel

Specify name and whether it should be private. Creates a new communication channel

get_channel_info

Retrieves detailed information about a specific channel

get_user_info

Retrieves detailed information for a specific user

list_all_channels

Lists all public and private channels available in the workspace

list_workspace_users

Lists all users in the Pumble workspace

Example Prompts for Pumble in Pydantic AI

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

"List all our active channels in Pumble."

"Post a message in the #dev-updates channel stating that 'Deployment 2.1 is completed'."

"Read the last 3 messages from #marketing-q4 and react to the last one with a 'thumbsup'."

Troubleshooting Pumble MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Pumble + Pydantic AI FAQ

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