MCP
MCP servers work fine with one client. The question is whether they hold up when five AI agents are calling them simultaneously, or when a single agent fires off ten parallel tool calls in a complex workflow. Most MCP server developers never test this. They ship, an agent makes concurrent
MCP
Most MCP testing guides focus on the server side: does the server expose correct tools, does it handle errors, does it perform under load. But if you're building an application that consumes MCP servers — an AI assistant, an agent orchestrator, a developer tool — the client side needs tests
MCP
Your MCP tool works in Claude Desktop. You've called it manually a dozen times. It returns results. You're confident. Then a user sends an unexpected input format, or calls two tools in sequence with shared state, or the downstream API your tool depends on returns a
MCP
Unit tests for MCP tool handlers are necessary but not sufficient. They test the function — they don't test whether the server correctly handles the MCP protocol, whether tools are advertised correctly, whether resources are listed as expected, or whether the transport layer survives malformed messages. Integration tests do.
MCP
Most MCP server tutorials skip authentication. The examples use stdio transport with no auth, the server runs locally, and there's nothing to secure. But if you're building an MCP server that exposes real capabilities — file access, database queries, API calls — and if that server is accessible
Testing
Basic MCP server tests cover tool invocation and resource reading. Advanced patterns go further: property-based testing to discover unexpected inputs, fuzz testing for protocol robustness, multi-client concurrency testing, transport-layer validation, and performance benchmarking. This guide covers those advanced patterns. A previous guide covered the basics of testing
Testing
MCP (Model Context Protocol) servers expose tools, resources, and prompts to AI agents. Testing them requires validating JSON-RPC protocol compliance, schema correctness, tool execution behavior, resource content, and error handling — none of which standard unit test frameworks handle out of the box. This guide covers how to test each
MCP
You built an MCP server. You tested it locally. Claude can call your tools. You're ready to ship. Then a user pastes a prompt, your tool gets called with an edge-case input, and it silently fails. No exception. No error. The agent just gets back garbage and
AI Testing
Your AI agent shipped. Traffic is flowing. Everything looks clean in your LLM observability dashboard — latency is fine, token usage is stable, no exceptions in the logs. And somewhere, quietly, your agent is returning the wrong answer to every third user. This is the silent failure problem. It is not
developers
Playwright MCP is a Model Context Protocol server that lets AI agents (Claude, Copilot, Cursor) control a browser using the accessibility tree instead of screenshots. It's useful for AI-driven browser automation — but it still requires local Playwright and doesn't replace a proper test automation strategy.
AI Testing
Your AI agent can read the filesystem, query your database, call external APIs, and write files. It does all of this through MCP tools — and you have no idea if it's doing it correctly. Welcome to the new testing gap. What MCP Is (And Why It Changes Everything)