Modern AI agents are probabilistic - they use language models that can produce different outputs. To be useful, these agents need to interact with external tools and services through the Model Context Protocol (MCP). But the response from the MCP servers can change!
Your agent calls multiple MCP servers to accomplish tasks. Because the agent's behavior is non-deterministic and the MCP servers can return different responses, thorough testing becomes critical.
To properly test a probabilistic agent, you need to run it hundreds or thousands of times to understand its behavior patterns, edge cases, and failure modes. But hitting real external MCP servers 1000x creates massive problems:
External services return different data over time (weather changes, database records update), making it impossible to reproduce bugs.
Testing with real services can send actual emails, modify production databases, or trigger unwanted actions that affect real users.
Network latency, rate limits, and API throttling mean your test suite could take hours to complete instead of seconds.
External services can be down, slow, or have network issues, causing your tests to fail for reasons unrelated to your agent's code.
The question is: How do you test your probabilistic agent 1000x without these problems?
Simvasia solves the testing problem by introducing Mocks with remote MCP servers - a powerful combination that gives you control to test your agents thoroughly.
Test your AI agents 1000x safely and reliably with MCPs using Mocks (deterministic responses) or Staging Environments (meant for testing).
Upload your MCP server to Simvasia and increase adoption by making it easier for AI developers to test and integrate with your tools.
Build your agent, then connect to Simvasia to test against Mocks or staging environments. Run 1000x tests and better understand how your agent performs.
Central platform that hosts a repository of remote MCP servers (Mocks and Staging) and provides authentication for testing & debugging.
Upload your MCP server to Simvasia. Make it easy for AI developers to test at scale and integrate, driving adoption.