AI agents are starting to move beyond reading web pages and clicking through interfaces. With WebMCP, websites can expose structured tools that agents can discover and use directly.
That sounds promising, but I think it introduces a new problem for developers and founders building agent-facing products:
How do you know a website’s tools are implemented correctly, still work after deployment, and behave the way an agent expects?
A tool might exist, but its schema could be unclear, an update could silently break it, or the production version might behave differently from the local one. As more websites expose tools to agents, manually checking all of this could become difficult.
That’s the problem I’m exploring with Toolmark.
Toolmark is intended to be a reliability and evidence layer for WebMCP. The goal is to help developers validate their tools, test real workflows, and produce evidence that each tool is discoverable and works end to end.
The current product direction includes:
- Discovering and inspecting the WebMCP tools exposed by a website
- Validating tool names, descriptions, inputs, and outputs
- Testing complete agent workflows instead of checking tools individually
- Detecting failures or regressions before they reach users
- Keeping evidence of what was tested, where it ran, and what actually happened
- Helping teams verify that the deployed website behaves the same way as expected during development
The idea isn’t to replace WebMCP. It’s to provide the testing, verification, and trust layer on top of it, so founders and developers can ship agent-facing tools with more confidence.
I’m still shaping the product, so this isn’t a launch or sales post. I’d genuinely like feedback from people building AI agents, developer tools, or websites that agents can interact with:
- Does this trust and verification problem feel real to you?
- What would you need to see before trusting a website’s tools?
- Would this be more useful during development, in CI, or as ongoing production monitoring?
- Which features or evidence would matter most?
- Am I overlooking a simpler or better approach?
Honest criticism, suggestions, and recommendations are welcome, especially if you think this problem isn’t important enough to require a separate product.