MCP Server
The MCP Server is an AI service proxy built on Cloudflare Workers that routes requests to multiple AI providers through a unified API.
Core Capabilities
Multi-provider AI integration: Proxy requests to Anthropic Claude and OpenAI through a single endpoint
Edge deployment: Global, low-latency responses via Cloudflare Workers
Type-safe: Implemented in TypeScript with the Hono framework
Cross-origin support: Built-in CORS for web application integration
API Endpoints
GET /health— Check server status and configurationGET /api/provider— Retrieve current AI provider and model configurationPOST /api/mcp— Send AI service requests with context, query, and options
Available Tools (accessible via /api/mcp)
api-client— Handle interactions with external APIsdata-processor— Process and transform data through the AI pipelinefile-handler— Manage file-related operationsmy-tool— Flexible custom message processingexample_tool— General-purpose message processing
Provides global edge deployment for the MCP server, offering low-latency proxying of AI requests through Cloudflare's distributed network
Enables routing requests to OpenAI's models through the MCP server, providing access to OpenAI's AI capabilities via a unified proxy interface
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server ask Claude to summarize this document about AI ethics"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server
A modern AI service proxy built with Cloudflare Workers and Hono framework, supporting multiple AI providers including Anthropic Claude and OpenAI.
Features
Multi-provider AI service integration (Anthropic Claude, OpenAI)
Built on Cloudflare Workers for global edge deployment
Fast and efficient request handling with Hono framework
Type-safe implementation with TypeScript
CORS support for cross-origin requests
Health check and provider info endpoints
Related MCP server: Remote MCP Server Authless
Prerequisites
Node.js (LTS version recommended)
npm or pnpm package manager
Cloudflare account for deployment
API keys for supported AI providers
Installation
Clone the repository
Install dependencies:
pnpm installEnvironment Setup
Copy the example environment file:
cp .env.example .envConfigure your environment variables in
.envwith your API keys and preferences
Development
Start the development server:
pnpm run devThe server will start in development mode with hot reloading enabled.
Deployment
Deploy to Cloudflare Workers:
pnpm run deployAPI Endpoints
Health Check
GET /healthReturns server status and configuration
Provider Info
GET /api/providerReturns current AI provider and model configuration
MCP API
POST /api/mcpMain endpoint for AI service requests
Accepts JSON payload with context, query, and options
Project Structure
├── src/
│ ├── controllers/ # Request handlers
│ ├── models/ # Type definitions
│ ├── services/ # AI service implementations
│ └── index.ts # Main application entry
├── public/ # Static assets
└── wrangler.jsonc # Cloudflare Workers configurationLicense
MIT
Available Tools
5 toolsapi-clientD
ApiClient tool description
Inputs: message.
data-processorD
DataProcessor tool description
Inputs: message.
example_toolC
An example tool that processes messages
Inputs: message.
file-handlerD
FileHandler tool description
Inputs: message.
my-toolD
MyTool tool description
Inputs: message.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 5 tools
The tools have distinct names like api-client, data-processor, and file-handler, which suggest different purposes, but descriptions are generic (e.g., 'ApiClient tool description'), leaving some ambiguity about their specific functions. Without detailed descriptions, it's unclear if there's overlap in processing or handling tasks.
Naming is inconsistent with mixed conventions: hyphen-case (api-client, data-processor, my-tool), snake_case (example_tool), and camelCase (FileHandler). This lack of a predictable pattern makes the tool set harder to navigate and could confuse agents.
With 5 tools, the count is reasonable for a general-purpose MCP server, suggesting a moderate scope. It's not too thin or heavy, though the generic nature of the tools makes it hard to assess if this number fits a specific domain perfectly.
Given the generic tool names and descriptions, it's impossible to infer a clear domain, making completeness assessment difficult. However, the set lacks obvious CRUD or lifecycle coverage, and tools like 'example_tool' and 'my-tool' seem vague, indicating significant gaps for any specific workflow.
Maintenance
Related MCP Connectors
Cloudflare Workers MCP server: ai-model-router
Cloudflare Workers MCP server: ai-gateway
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
Cloudflare Workers MCP server: ai-guardrails
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