kiwi
Overview
The kiwi MCP server is a Model Context Protocol (MCP) server that enables AI assistants and agents to interact directly with Kiwi, a lightweight experimentation and workflow platform designed for iterative analysis, data exploration, and decision support. It allows AI-driven workflows to create, inspect, and reason about experiments, inputs, and results without switching tools or embedding platform-specific logic.
This server is well suited for experimentation, analysis workflows, and iterative decision-making where AI needs structured access to evolving data and results.
Transport
streamable-http
Tools
Key Capabilities
- Experiment lifecycle management — Create, update, and inspect experiments programmatically.
- Iterative analysis — Enable AI assistants to run, compare, and reason over multiple iterations or scenarios.
- Result inspection — Retrieve structured outputs and metadata for downstream reasoning or summarization.
- Workflow support — Integrate experimentation directly into AI-driven analysis pipelines.
- Decision support — Help assistants explain outcomes, tradeoffs, and trends across runs.
How It Works
The kiwi MCP server runs as an MCP service and connects AI clients to the Kiwi platform using configured credentials. AI clients communicate with the server over the MCP protocol to request experiment context or submit new inputs as part of broader reasoning workflows.
The server mediates access to Kiwi’s APIs, handling authentication and response normalization before returning structured results that AI assistants can reason over directly. This abstraction allows AI workflows to focus on interpreting results and guiding next steps rather than managing platform-specific details.
By exposing Kiwi through MCP, the server enables conversational and automated experimentation workflows — such as exploring scenarios, comparing outcomes, and iterating toward better decisions — all within a single AI-driven environment.