Product Case
Build production MCP servers in minutes — without touching infrastructure.
MCPCore is a browser-based development and hosting platform specifically designed for the Model Context Protocol. Developers write tool logic in JavaScript, configure authentication and parameter schemas through a visual interface, and deploy to a live endpoint in a single click — without leaving the browser, without managing servers, and without configuring runtimes or dependencies.
The platform is built for teams and individual developers building AI-augmented applications who need to expose structured capabilities to MCP-compatible clients — including Claude Desktop, Cursor, VS Code, Windsurf, and Cline — without the operational burden of self-hosting MCP infrastructure.
Context
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open standard developed to enable AI models to interact with external tools, data sources, and systems in a structured, interoperable way. Rather than building bespoke integrations for every AI assistant or model, MCP provides a unified protocol that any MCP-compatible client can use to invoke tools, access resources, and exchange structured data with external systems.
An MCP server exposes one or more tools — defined functions with typed inputs and outputs — that AI clients can discover and call during a conversation. This enables AI assistants to interact with databases, APIs, file systems, web services, and custom business logic through a standardized interface.
MCPCore eliminates the infrastructure complexity of building and hosting MCP servers. Instead of managing runtimes, configuring server environments, handling deployment pipelines, and maintaining authentication layers, developers focus entirely on writing tool logic — and MCPCore handles the rest.
Compatible Clients
Platform
Full Platform Capabilities
Browser-Based Code Editor
Write, test, and iterate on MCP tool logic directly in your browser using a full-featured IDE with syntax highlighting, autocomplete, and real-time error reporting. No local environment setup, no package installation, no configuration files.
AI-Assisted Tool Generation
The built-in AI assistant helps you generate tool definitions, write parameter schemas, and scaffold MCP server logic. Describe what your tool should do, and the assistant produces a working implementation you can review, adjust, and deploy.
One-Click Deployment
Deploy an MCP server from the editor with a single click. MCPCore automatically provisions a subdomain, configures the runtime environment, and makes your server immediately accessible — no DevOps, no cloud console, no waiting.
Real-Time Analytics
Monitor every deployed server with a live dashboard tracking request volume, response latency, error rates, and estimated cost per server. Analytics are available per server and per tool, enabling precise optimization of both performance and spend.
Flexible Authentication
Secure your MCP endpoints with four distinct authentication modes: Public (no authentication for open tools), API Key (simple bearer authentication), OAuth 2.0 (federated identity for enterprise integrations), and Bearer Token. Switch modes without redeployment.
Encrypted Secrets Storage
Store API keys, database credentials, and configuration values as encrypted secrets using AES-256 encryption. Reference secrets in your tool code without embedding credentials in source or exposing them through logs.
Error Reporting and Debugging
Full stack traces and structured runtime logs are captured for every failed request. Error reports include request payloads, execution context, and timing data — giving you the information needed to debug and fix issues without additional instrumentation.
Visual Parameter Builder
Define typed input parameters for your MCP tools using a visual interface. Specify types, validation rules, optional and required fields, and default values — the platform generates the JSON Schema automatically.
Built by HAFETECH
Engineering and product ownership
MCPCore is a HAFETECH in-house product. Our team owns the full engineering stack — from the browser-based IDE and deployment infrastructure to the analytics engine and authentication layer. Product decisions are made based on direct feedback from developers building real MCP integrations.
As the MCP ecosystem matures and adoption grows across AI development toolchains, we continue to extend MCPCore's capabilities to serve increasingly complex production use cases — including enterprise-grade security requirements, high-volume workloads, and multi-team management needs.
Explore MCPCore →