aicoolies logo
mcp.run logo

mcp.run

Hosted MCP server platform

Share
freemium
Visit Website →

Platform for running MCP servers securely in WebAssembly sandboxes. One-click install for Claude Desktop, VS Code, and other MCP-compatible clients with no Docker required. Simplifies MCP server deployment and management by handling isolation, security, and distribution, making it easy to extend AI assistants with custom tools without infrastructure overhead.

MCP.run is a registry and runtime platform for hosting and executing MCP (Model Context Protocol) tools using WebAssembly-based isolation, built by Dylibso to provide secure, portable, and universal tool execution for AI applications. It solves the challenge of safely running third-party MCP servers by executing tools as WebAssembly modules (called servlets) in a sandboxed environment, eliminating the need for heavy containers or virtual machines while providing strong security isolation. MCP.run enables anyone to publish, discover, and execute MCP tools that work across any platform, operating system, processor, or device.

MCP.run differentiates itself with its WebAssembly-first architecture where all servlets are compiled to Wasm modules, ensuring portability and security through strict sandboxing. The platform requires developers to explicitly declare domain allowlists, environment variable access, and file system paths ahead of time, preventing unauthorized network access or data exfiltration. MCP.run provides client connectors including mcpx for native integration, mcpx4j for Java and Android support, and web-based interfaces for browsing and testing available tools. The registry allows anyone to publish servlets with transparent security policies that users can review before execution.

MCP.run is designed for AI developers, platform teams, and organizations that need to integrate third-party MCP tools into their AI applications with production-grade security and minimal operational overhead. It integrates with any MCP-compatible AI client including Claude, ChatGPT, Cursor, and custom agent frameworks, providing a centralized hub for discovering and executing tools without managing individual server deployments. The platform is particularly valuable for enterprise environments where security policies require strict control over what external tools can access, offering auditable, sandboxed execution that meets compliance requirements while maintaining the flexibility of the MCP ecosystem.

Pricing

Free tier available

Platforms

Web, CLI

Categories

Tags

Use Cases

Alternatives

Related Tools

Hermes Agent logo

Hermes Agent

Top Pick

Open-source AI agent framework with persistent memory, reusable skills, tools, and messaging gateways

Hermes Agent is an open-source AI agent framework with persistent memory, reusable skills, 40+ tools, cron jobs, and messaging gateways.

open-sourceOpen Source

Safari MCP Server

Apple's Safari-native MCP server for web debugging agents

Safari MCP Server is Apple's safaridriver-based MCP server in Safari Technology Preview, giving compatible coding agents local access to Safari page content, console logs, network requests, screenshots, JavaScript evaluation, interactions, viewport controls, and accessibility/performance checks.

freeTelemetry

Headroom

Context compression for LLM apps and coding agents

Headroom is an Apache-2.0 context compression layer for LLM apps and coding agents. It compresses tool output, logs, files, RAG chunks, and agent history through a local library, proxy, wrapper, or MCP server, with retrieval hooks for bringing originals back when needed. Treat its savings numbers as Headroom-reported benchmarks, not independent aicoolies measurements.

open-sourceOpen SourceTelemetry

Codebase Memory MCP

Codebase knowledge graph MCP server for AI coding agents

Codebase Memory MCP is an MIT-licensed MCP server that turns a repository into a persistent code knowledge graph for AI coding agents. It gives Claude Code, Cursor, Codex-style agents, and other MCP clients structural queries for functions, classes, call chains, routes, and architecture, helping them explore large projects without repeatedly rereading files or relying only on broad search.

open-sourceOpen SourceTelemetry
BeeAI Framework logo

BeeAI Framework

Python and TypeScript framework for production multi-agent systems

BeeAI Framework is an Apache-2.0 toolkit for building production-ready AI agents and multi-agent systems in Python and TypeScript. Its docs cover agents, tools, RAG, memory, workflows, backend providers, serving, and A2A/MCP integration surfaces, making it a vendor-neutral option for teams comparing LangGraph, CrewAI, Mastra, and related agent runtimes.

open-sourceOpen SourceTelemetry

Supabase MCP

MCP server for connecting AI assistants to Supabase projects

Supabase MCP is Supabase's Apache-2.0 server for connecting AI assistants to Supabase projects. It can expose database, configuration, and project-management workflows to MCP clients such as Cursor, Claude, and Windsurf, while the official docs emphasize permission and security review before production use, SQL changes, or high-privilege database access.

open-sourceOpen SourceTelemetry