A Model Context Protocol (MCP) server that provides LLM access to Cucumber Studio's testing platform, enabling AI assistants to retrieve test scenarios, action words, test runs, and project information.
From the registry: MCP server for Cucumber Studio - test scenarios, action words, and execution data
Please install the `cucumberstudio` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Cucumber Studio API credentials** — Get access token, client ID, and user ID from your Cucumber Studio account. (https://cucumberstudio.com)
Canonical MCP server config (stdio transport):
- command: `npx`
- args: ["-y","cucumberstudio-mcp"]
- required environment variables:
- `CUCUMBERSTUDIO_ACCESS_TOKEN`: Cucumber Studio API access token. (example: `<your-cucumberstudio-access-token>`)
- `CUCUMBERSTUDIO_CLIENT_ID`: Cucumber Studio client ID. (example: `<your-client-id>`)
- `CUCUMBERSTUDIO_UID`: Cucumber Studio user ID. (example: `<your-uid>`)
Note: Docker image also available: herosizy/cucumberstudio-mcp. Supports dual transport (stdio + streamable HTTP).
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.CUCUMBERSTUDIO_ACCESS_TOKENrequiredYour API access tokenCUCUMBERSTUDIO_CLIENT_IDrequiredYour client IDCUCUMBERSTUDIO_UIDrequiredYour user IDReal-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Validate oh-my-posh configurations and segment snippets against the official schema.
Real-time Kubernetes network traffic visibility and API analysis for HTTP, gRPC, Redis, Kafka, DNS.