Fastly MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fastly as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Fastly. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Fastly?"
)
print(response)
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.
LlamaIndex agents combine Fastly tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Fastly MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Fastly MCP Server
LlamaIndex provides unique advantages when paired with Fastly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fastly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fastly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fastly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fastly tools were called, what data was returned, and how it influenced the final answer
Fastly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fastly MCP Server delivers measurable value.
Hybrid search: combine Fastly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fastly to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Fastly for fresh data
Analytical workflows: chain Fastly queries with LlamaIndex's data connectors to build multi-source analytical reports
Fastly MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Fastly to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Fastly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFastly + LlamaIndex FAQ
Common questions about integrating Fastly MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.