Senro
Senro provides evaluation and observability infrastructure for WebMCP, the emerging standard that lets AI agents discover and invoke tools on websites. The platform lets development teams run thousands of cross-LLM tests before production and monitor real-world agent interactions, including tool arguments, results, errors, and execution duration.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
WebMCP introduces an interface designed for AI agents, allowing them to discover and invoke tools directly instead of navigating websites like humans do. As agent interfaces become part of the web, reliability becomes a new problem—a WebMCP implementation might work with one LLM or language and fail with another, select the wrong tool, pass incorrect parameters, or behave differently in production, and teams lack dedicated infrastructure to catch these issues.
Solution
Senro provides infrastructure to evaluate WebMCP implementations before production and observe how they perform in the real world. Teams can run thousands of evaluations across LLMs, languages, and test cases without building reliability infrastructure themselves. The platform captures real agent interactions in production, surfaces failures, and helps teams understand how their WebMCP implementation performs, including goal-oriented evaluation that tests whether an agent actually accomplishes the intended outcome and trajectory evaluation that verifies expected tool calls, arguments, and ordering. Senro also monitors agent activity to show when AI agents interact with a website and which WebMCP tools they use, while inspecting tool arguments, results, errors, and execution duration for every observed call.
Target Audience
Development teams building WebMCP implementations and AI-agent-accessible websites who need to evaluate their tools across multiple LLMs and monitor real-world agent interactions in production.
Features
- Goal-oriented evaluation that tests whether an agent accomplishes the intended outcome, not just whether it calls the right tools
- Trajectory evaluation that verifies expected WebMCP tool calls, arguments, and ordering when the agent's path matters
- Cross-LLM testing across 100+ LLMs with 1,000+ evaluations supported
- Production observability capturing agent activity, including when AI agents interact with the website and which WebMCP tools they use
- Tool call inspection showing arguments, results, errors, and execution duration for every observed WebMCP call
- No reliability infrastructure required—the platform handles evaluation and monitoring end-to-end