toolhive VS playwright-mcp

With the new network isolation features in ToolHive, you don’t have to trust. You can verify – and enforce.

GitHub

At Stacklok, we created ToolHive because we saw a gap in running MCP servers easily but also securely. As security engineers, we knew there had to be a better way.

This looks interesting, but anytime security is offloaded to an LLM I am extremely skeptical. IMO the right way to do this is to enforce permissions explicitly through a AuthZ policy. Something like what Toolhive [0] is doing is the right way I think.

All MCP comms from client to server go through an SSE proxy which has AuthN and AuthZ enabled. You can create custom policies for AuthZ using Cedar [1].

[0] https://github.com/stacklok/toolhive, https://github.com/stacklok/toolhive/blob/main/docs/authz.md

Building on our earlier discussion about enterprises needing dedicated hosting for MCP servers and ToolHive's Kubernetes-based solution, we're excited to announce our new Kubernetes Operator for ToolHive. This specialised tool streamlines the secure deployment of MCP servers to Kubernetes environments for enterprise and engineers.

For more details, check out the README .

The team at Stacklok, empowered by our CEO, Craig McLuckie (and co-creator of Kubernetes), recently released ToolHive, an open source project that offers a convenient way to run MCP servers with familiar technologies with authentication, authorization and network isolation. Let’s take a closer look at how ToolHive and Kubernetes come together to support MCP in an enterprise environment.

As more teams start deploying MCP servers to power tool-calling agents, one question comes up fast: how do you control who can call what? It’s not just about verifying identity, it’s about enforcing the right permissions, without bloating every server with bespoke auth logic. ToolHive was built to solve exactly this problem. It separates authentication from authorization, integrates cleanly with existing identity providers, and uses Amazon’s Cedar policy language to define clear, auditable access rules. The result is a simple but powerful way to lock down tool access without embedding auth logic into every server you run.

We are excited about the potential of Model Context Protocol (MCP) servers and inspired to make it more consistent to set up, easier to configure and overall secure. So, we're releasing ToolHive as an open-source project that uses existing technology (e.g. Docker and Kubernetes) for better packaging, security utilities, and more. Let's build on MCP together!

Playwright MCP Set up Playwright MCP

Playwright MCP -Provides browser automation capabilities using Playwright.

One of them is Playwright MCP. This project is a browser, that provides our agent with "eyes". After generating a component it's able to immediately see the outcome and make the adjustments. You don't need to insert error codes or explain - you could just ask it to check the dev tools and fix the code to avoid those errors, thus creating a powerful feedback loop that helps you to focus on more architectural tasks rather than explaining why you don't like the result. Now, let's dive into how to use it, and let's make a simple news website to test the setup.

In the evolving landscape of web development, ensuring the reliability and performance of web applications across different browsers is paramount. This is where the Playwright Model Context Protocol (MCP) Server can be helpful, enabling LLMs to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models.

Cline with Gemini 2.5 (https://cline.bot/)

Framelink MCP (https://github.com/GLips/Figma-Context-MCP)

Playwright MCP (https://github.com/microsoft/playwright-mcp)

Pull down designs via Framelink, optionally enrich PNG exports of nodes added as images to the prompt, write out the components, test/verify via Playwright MCP.

Gemini has a 1M context size now, so this applies to large mature codebases as well as greenfield. The key thing here is the coding agent being really clever about maintaining its' context; you don't need to fit an entire codebase into a single prompt in the same way that you don't need to fit the entire codebase into your head to make a change, you just need enough context on the structure and form to maintain the correct patterns.

Construí o Testronaut, um agente autônomo de testes com IA, em apenas algumas semanas. Ele transforma descrições em linguagem natural como "Faça login no app e verifique a página inicial" em testes web estruturados e automatizados usando o Playwright MCP.

I built Testronaut, an autonomous testing agent powered by AI, in just a few weeks. It turns natural language descriptions like "Login to the app and check the homepage" into structured, automated web tests using Playwright MCP.

E.g. If you’re interested in running the Playwright MCP, simply do:

Or just use Playwright MCP: https://github.com/microsoft/playwright-mcp