Fastly MCP Server for Pydantic AI 12 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Fastly through 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 Fastly "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in Fastly?"
)
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 Fastly MCP Server
Connect your Fastly account to any AI agent and take full control of your edge cloud delivery and CDN configurations through natural conversation.
Pydantic AI validates every Fastly tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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
- Service Orchestration — Identify bounded underlying Edge Cloud Delivery mappings and extract CDN service IDs aggregating global payload instances natively
- Version Management — Enumerate strictly immutable configuration drafts and promover promoted versions seamlessly to distribute instant security patches
- Live Traffic Auditing — Target specific configuration identities evaluating precise Active Version pointers to validate which architectural instance controls live traffic today
- Edge Deployment — Deploy drafted VCL or Compute@Edge logic instantly to production by promoting Promoted Drafts to Active states synchronously
- Cache Purging — Vaporize the complete Surrogate Cache storing static endpoints globally by issuing absolute HTTP PURGE instructions via chat
- Backend & Origin Control — Locate physical upstream Origins (AWS/GCP) mapped inside configurations and verify port constraints shielding original load-balancers
- Domain Auditing — Extract precise FQDN apex domains terminated at the Fastly Edge to manage routing configurations for specific headers flawlessly
The Fastly MCP Server exposes 12 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 Fastly to Pydantic AI via MCP
Follow these steps to integrate the Fastly 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 12 tools from Fastly with type-safe schemas
Why Use Pydantic AI with the Fastly MCP Server
Pydantic AI provides unique advantages when paired with Fastly 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 Fastly integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Fastly connection logic from agent behavior for testable, maintainable code
Fastly + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Fastly MCP Server delivers measurable value.
Type-safe data pipelines: query Fastly with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Fastly tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Fastly and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Fastly responses and write comprehensive agent tests
Fastly MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect Fastly to Pydantic AI via MCP:
activate_service_version
Activate a specific configuration version for a service
create_service
Create a new Fastly service
delete_service
Delete a specific Fastly service
get_me
Get current API token identity info
get_service
Get details for a specific Fastly service
get_service_stats
Get usage statistics for a specific service
get_service_version
Get details for a specific service version
list_service_versions
List all configuration versions for a service
list_services
List all Fastly services
list_version_backends
List all backend origins for a specific service version
list_version_domains
List all domains for a specific service version
purge_all_cache
Purge all cached content for a specific service
Example Prompts for Fastly in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Fastly immediately.
"List all active Fastly services"
"Activate version 15 for service 'Prod-Main-CDN'"
"Purge all cache for service '1a2b'"
Troubleshooting Fastly MCP Server with Pydantic AI
Common issues when connecting Fastly to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiFastly + Pydantic AI FAQ
Common questions about integrating Fastly 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 Fastly 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 Fastly to Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.