Fly.io MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Fly.io through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Fly.io "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Fly.io?"
)
print(result.data)
asyncio.run(main())
* 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 Fly.io MCP Server
Connect your Fly.io account to any AI agent and take full control of your edge computing and container orchestration through natural conversation.
Pydantic AI validates every Fly.io tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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
- App Space Orchestration — List logical parent containers (Apps) spanning the Fly Edge network, identifying anycast assignments and dedicated IPv4/IPv6 IPs natively
- Machine Runtime Management — Navigate and control individual MicroVM (Machine) endpoints, fetching unique IDs and explicit placement regions flawlessly
- Autonomous Scaling — Provision new highly available Edge Machines to scale horizontal capacities dynamically without waiting on full platform deployments
- Live Health Auditing — Examine exhaustive runtime states, returning dynamic executing statuses (started, stopped, suspended) and docker image digests in real-time
- Remote Command Execution — Inject and run shell commands inside active Machines bypassing SSH by interacting directly with the hypervisor API securely
- Persistent Storage Control — List hardware NVMe Volumes attached to your apps to manage stateful data like PostgreSQL or SQLite independent of compute
- Network DNA Extraction — Retrieve the operational baseline of Fly Apps, identifying Wireguard ranges and cluster master regions synchronously
The Fly.io 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 Fly.io to Pydantic AI via MCP
Follow these steps to integrate the Fly.io 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 Fly.io with type-safe schemas
Why Use Pydantic AI with the Fly.io MCP Server
Pydantic AI provides unique advantages when paired with Fly.io 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 Fly.io integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Fly.io connection logic from agent behavior for testable, maintainable code
Fly.io + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Fly.io MCP Server delivers measurable value.
Type-safe data pipelines: query Fly.io with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Fly.io tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Fly.io and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Fly.io responses and write comprehensive agent tests
Fly.io MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Fly.io to Pydantic AI via MCP:
create_machine
Scales horizontal capacities dynamically without waiting on full platform deployments. Provision a new highly available Edge Machine inside an App
delete_machine
The Firecracker VM is dropped, compute billing ceases immediately, and any ephemeral disk state dissolves. Fails safely if persistent volumes are explicitly attached without the force flag. Terminate and destroy a Fly Machine forever (Scale Down)
exec_machine
Useful for `ls`, `ps aux`, `top`, or running internal database diagnostic migrations. Inject and run a shell/Bash command inside an active Fly Machine
get_app
Identifies the primary Region holding the cluster master, internal Wireguard network ranges assigned, and any active Anycast IPs actively routing inbound user traffic globally. Retrieve the operational baseline state of a distinct Fly App
get_machine
Returns dynamic executing states ("started", "stopped", "suspended"), the precise docker image digest/SHA actively booted into RAM, and any mapped volume points tying persistent SQLite/Postgres logs. Get exhaustive runtime states attached to a single Fly Machine
list_apps
Apps are fundamentally distinct collections of individual microVMs (Machines), dedicated IPv4/IPv6 anycast assignments, and persistent storage volumes. List Fly.io App spaces belonging to an Organization
list_machines
Retrieves unique identifiers and explicit placement Regions (e.g., iad, ams, nrt). List individual MicroVM (Machine) endpoints inside a Fly App
list_volumes
Crucial identifier for managing stateful applications (PostgreSQL, SQLite, persistent cache) safely independent of compute instances. List persistent hardware NVMe Volumes attached to an App
start_machine
Utilized extensively when recovering paused batch processors or restarting crashed worker nodes dynamically across edge points of presence. Boot a previously stopped or suspended Fly Machine
stop_machine
Drastically reduces latency bills during idle cycles outside typical user ingress bands. Gracefully halt a running Fly.io internal Machine
Example Prompts for Fly.io in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Fly.io immediately.
"List all machines in my 'web-api' app"
"Run 'ls -la /app' on machine '918572b0' in app 'web-api'"
"Show me the persistent volumes for 'web-api'"
Troubleshooting Fly.io MCP Server with Pydantic AI
Common issues when connecting Fly.io to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiFly.io + Pydantic AI FAQ
Common questions about integrating Fly.io MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Fly.io with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Fly.io to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.