What Sets Them Apart
Requestly and Chrome DevTools MCP both live in the browser-debugging neighborhood, but they are aimed at entirely different jobs. Requestly is a multi-surface suite for humans — frontend engineers and QA testers who want to intercept, mock, and replay HTTP traffic collaboratively across a team. Chrome DevTools MCP is a Google-maintained Model Context Protocol server that exposes Chrome’s DevTools capabilities to AI coding agents. One is a rule-based debugging workspace; the other is a bridge between an agent and a browser. Confusion is easy because both touch network traffic; the right choice depends on whether a human or an agent is driving.
Requestly and Chrome DevTools MCP at a Glance
Requestly is a decade-old, BrowserStack-acquired product used by 200,000+ developers. It ships a Chrome, Firefox, and Edge extension plus a desktop app, and it bundles an HTTP interceptor, an API client, a mock server, and session replay behind one collaborative workspace. The core is MIT-licensed, the commercial tier adds SSO and team features, and the primary user is a human clicking through a rule builder to debug a flaky integration.
Chrome DevTools MCP is a much younger, narrower product. It implements the Model Context Protocol so AI coding agents (Claude, Cursor, Windsurf, and others) can drive a headless Chrome instance — navigate, inspect the DOM, read console output, capture network requests, and run DevTools commands — all through the MCP standard. There is no rule builder, no mock server, and no human-facing UI beyond the agent’s own transcript.
The shape of the choice is driver, not feature list. Requestly helps a human debug the network stack; Chrome DevTools MCP helps an agent operate a browser. Trying to use either for the other job ends badly.
Breadth of Human-Facing Features
Requestly carries years of product depth that a focused agent bridge cannot match. The interceptor rule builder alone is a mature tool — header rewrites, URL redirects, response modifications, script injection, request delays — and every rule can be shared, versioned, and collaboratively edited. The API client is a credible Postman alternative, the mock server stands up fake endpoints in seconds, and session replay captures network, console, and DOM together so engineers can reproduce bugs instead of guessing.
Chrome DevTools MCP does almost none of this. It is a programmable interface; the features that matter are what an agent can ask Chrome to do, not what a human can configure through a UI. That is not a failure — it is the entire point of the product — but it means a QA team adopting MCP as its debugging tool would be building the equivalent of Requestly themselves on top of it. That is not a small project.
Pricing reinforces the split. Requestly charges per-seat for team plans after a generous free tier; Chrome DevTools MCP is free and open-source (MIT), because its audience is developers wiring it into agent loops rather than organizations buying team seats.
Integration with AI Agents
Chrome DevTools MCP wins the agent-integration axis by design. Claude Code, Cursor, Windsurf, and any MCP-compatible agent can list tools, call them, stream responses, and chain DevTools commands into longer workflows without writing custom glue code. For teams building AI coding products where the agent needs to inspect a running web app, this is the standard path in 2026 and is actively being refined.
Requestly does not expose an MCP server and is not positioned as an agent-driven tool. Teams that want AI agents to modify traffic still need to build that integration themselves. For a subset of users — those explicitly blending agents with network debugging — this is a real gap, but it reflects the fact that Requestly is aimed at human workflows, not autonomous ones.
