vibe-tests
Integration test framework for MCP servers with LLM-powered tool calling.
AI models test your MCP tools automatically — they decide which tool to call and how. Isolated Docker environment, structured tracing, and JSON reports out of the box.
Quick Start
1. Add dependency
[]
= "0.0.1"
# Required by engine_config! macro for pre-test initialization
= "1.0.1"
2. Configure test engine
engine_config!
3. Write tests
async
Features
- Agentic testing — AI models call MCP tools based on natural language queries
- Isolated environment — Docker compose with automatic up/down, temp directories
- Structured tracing — file + real-time callback, parseable log format
- JSON reports — per-query details: model, tool, args, response, duration, error codes
- Multi-model — test same queries against multiple Ollama models
- Zero-config defaults — sensible defaults, minimal setup
How it works
engine_config!— one-time setup before all testson_start— launch your MCP server (Docker, process, whatever)engine.test("query")— LLM receives query + available tools → calls one → returns resulton_stop— cleanup, save logs, parse JSON report
Why
Testing MCP servers manually is slow and brittle. Vibe-tests lets AI models test your tools automatically — they understand natural language queries, decide which tool to call, and verify responses.
Catch regressions, validate tool schemas, and ensure your MCP server works before users notice.
Part of Vibe tools for Agentic RAG — Vibe Analyzer.