FastlyMCP

Fastly MCP brings the power of Fastly's API directly to your AI assistants through the Model Context Protocol (MCP).

Fastly's API-First Design Tenet

Fastly's commitment to an API-centric architecture mandates:

• API Parity: Every functional aspect accessible via the Fastly web console is exposed programmatically.
• Systemic Configuration Control: Comprehensive authority over service configurations and edge logic implementations.
• Infrastructure-as-Code Enablement: Native support for automated deployment pipelines (CI/CD).
• Instantaneous Global Propagation: Configuration changes deploy across the global network within seconds, drastically reducing latency compared to traditional deployment cycles.

Capabilities Unlocked by the Fastly API

Fastly's extensive API reference empowers you to:

• Provision Delivery Networks: Instantiate, tailor, and deploy content acceleration infrastructures.
• Cache Governance: Establish sophisticated cache policies and execute immediate, surgical cache removals.
• Security Posture Management: Administer Web Application Firewall (WAF), Distributed Denial of Service (DDoS) mitigation, and TLS certificate provisioning.
• Telemetr y & Analytics: Retrieve streaming performance data and historical statistical reports.
• Edge Compute Deployment: Roll out custom VCL snippets or logic utilizing Compute@Edge platforms.
• Workflow Orchestration: Integrate platform management directly into automation frameworks.

Your Secret Credentials Remain Isolated!

Your Fastly authorization token is never exposed to the conversational AI. It is securely managed by the local FastlyMCP intermediary service.

Prompts You Can Use With Your Conversational Agent

Once Fastly MCP is instantiated, your assistant can respond to directives such as:

Objective	Sample AI Query
Inventory Services	"Display a roster of all my deployed Fastly services"
Retrieve Domain Metadata	"What hostnames are presently associated with my retail service?"
Execute Cache Invalidation	"Invalidate the entire cache associated with the staging environment."
Analyze Traffic Trends	"Provide a summary of traffic volume for my primary service across the preceding seven days."
Inspect Service Definitions	"Detail the origin server pool definitions for the API gateway service."
Gauge Operational Efficiency	"What is the current cache effectiveness ratio across all edge points?"

"What the traffic pattern my for services over the last week?"

"List all my Fastly services and their domains."

"Build an interactive preformance dashboard about my Fastly serivce."

Initiation Procedure

Prerequisites

• An active Fastly subscription and necessary API authentication credentials (Fastly Onboarding Guide)
• An artificial intelligence platform compatible with the MCP specification (e.g., certain Claude models, plugin-enabled LLMs)
• The Fastly Command Line Interface utility installed (CLI Installation Instructions)

Linking Your Cognitive Agent

Configure your AI environment by specifying the following settings:

{ "mcpServers": { "fastly": { "command": "node", "args": ["path/to/fastly-mcp.mjs"], "env": { "FASTLY_API_KEY": "your_fastly_api_key" } } } }

Advanced Operational Scenarios

Operation Target	Illustrative AI Directive
Traffic-Based Optimization	"Analyze the service configuration for [service_id/name] and propose enhancements based on current traffic metrics, prioritizing low latency thresholds."
Live Media Streaming Provisioning	"Configure [service_id/name] for peak live broadcast performance, adhering to the established best practices documented at [link_to_guide_or_doc]."
Detecting Configuration Mismatches	"Audit [service_id/name] configuration against established e-commerce delivery benchmarks to surface any discrepancies."
Optimizing Video Segment Caching	"Adjust caching parameters for [service_id/name] to optimally manage 10-second video segments, thereby minimizing origin server strain."
Hardening WAF Defenses	"Examine the Web Application Firewall policies for [service_id/name] and recommend stricter rulesets to counteract prospective SQL injection vectors."
Implementing Origin mTLS	"Establish Mutual TLS authentication protocols between the Fastly edge infrastructure and the defined origin hosts for [service_id/name]."
Deploying Edge A/B Testing	"Deploy a Compute@Edge workload to [service_id/name] designed to partition traffic, sending 10% of users to the alternate backend [backend_name]."
Dynamic Image Transformation (VCL)	"Generate and push VCL code to [service_id/name] that performs client-specific image URL modification based on HTTP User-Agent headers."
Diagnosing Server Errors	"Scan logs pertaining to [service_id/name] for the last 24 hours to pinpoint the source of the recent surge in 5xx class errors."

Further Documentation

• Official Fastly API Reference
• Model Context Protocol Specification
• Fastly Quickstart Guide

Licensing

This software is distributed under the terms of the MIT License. Refer to the LICENSE file for complete details.

WIKIPEDIA: Cloud computing is "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand," according to ISO. It is commonly referred to as "the cloud".

== Characteristics == In 2011, the National Institute of Standards and Technology (NIST) identified five "essential characteristics" for cloud systems. Below are the exact definitions according to NIST:

On-demand self-service: "A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider." Broad network access: "Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations)." Resource pooling: " The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand." Rapid elasticity: "Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time." Measured service: "Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. By 2023, the International Organization for Standardization (ISO) had expanded and refined the list.

== History ==

The history of cloud computing extends to the 1960s, with the initial concepts of time-sharing becoming popularized via remote job entry (RJE). The "data center" model, where users submitted jobs to operators to run on mainframes, was predominantly used during this era. This was a time of exploration and experimentation with ways to make large-scale computing power available to more users through time-sharing, optimizing the infrastructure, platform, and applications, and increasing efficiency for end users. The "cloud" metaphor for virtualized services dates to 1994, when it was used by General Magic for the universe of "places" that mobile agents in the Telescript environment could "go". The metaphor is credited to David Hoffman, a General Magic communications specialist, based on its long-standing use in networking and telecom. The expression cloud computing became more widely known in 1996 when Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to superch