xk6‑mcp – Testing Model Context Protocol Servers with k6

xk6‑mcp is a lightweight extension that brings the Model Context Protocol (MCP) into the k6 performance‑testing ecosystem. It lets developers treat MCP servers as first‑class services in their load tests, enabling end‑to‑end validation of tool discovery, prompt retrieval, and resource handling under realistic traffic patterns. By exposing the same client API that an AI assistant would use, it bridges the gap between protocol specification and practical testing, ensuring that MCP implementations meet both functional and performance expectations before deployment.

The server is invaluable for teams building AI‑powered applications. With MCP, an assistant can query a catalog of tools, fetch prompts, and read resources in a unified way. xk6‑mcp allows you to simulate thousands of concurrent clients performing these operations, measuring latency, error rates, and resource usage. This is critical when scaling AI assistants to handle many users or integrating them into high‑throughput services such as chatbots, recommendation engines, or automated support systems. By running these tests in CI pipelines, teams can detect regressions early and guarantee that the MCP server remains responsive under load.

Key capabilities include:

• Full protocol support: The extension implements all core MCP methods—, , , etc.—so tests can cover every interaction an assistant might perform.
• Multiple transport options: In addition to the simple stdio transport used in examples, it supports Server‑Sent Events (SSE) and Streamable HTTP, mirroring real deployment scenarios.
• Automatic pagination handling: helpers transparently traverse paginated responses, while manual paging remains available for fine‑grained control.
• Built‑in metrics: Every request emits RED‑style metrics (, , ) tagged by method, providing instant visibility into performance health.

Typical use cases span from verifying a new MCP server before rollout to benchmarking an existing implementation against SLA targets. For example, a data‑science team can load‑test the prompt catalog to ensure that retrieval latency stays below 50 ms, or a DevOps group can simulate concurrent tool calls to confirm that the server scales horizontally without resource contention. Because the extension integrates directly with k6’s powerful scripting and reporting features, developers can embed MCP checks into existing performance suites without learning a new tool.

In summary, xk6‑mcp equips AI developers with a focused, protocol‑centric testing solution that aligns closely with production workloads. By exposing MCP operations to k6’s scalable execution engine and detailed metrics, it empowers teams to validate correctness, performance, and resilience of AI assistants’ back‑end services before they reach users.