Features¶
Spring AI Playground is organized around five product surfaces, designed to be understood in this order. The first four are the author / consume surfaces; Observability is the operator surface that watches them.
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Low-code authoring environment for JavaScript-based tools. Deny-first sandbox, Draft state, MCP server preset catalog, per-tool sandbox capability overrides.
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Built-in MCP server over Streamable HTTP, external connections via HTTP / SSE / STDIO / OAuth 2.1, a multi-tab Inspector for tools, resources, prompts, and client primitives, and a preset catalog of 57 vendor-official MCP servers activatable from the sidebar — see the MCP Catalog directory.
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Document ingestion, chunking, embedding, storage, and similarity search across Spring AI vector stores — the RAG validation surface.
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Unified runtime that composes tools and RAG context in one conversational interface — chain workflows and agentic tool-use side by side.
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Twelve in-app dashboards across four groups (AI Usage · AI Stack · Runtime · Overview) covering token economics, tool and MCP behaviour, RAG quality, host runtime, and a live trace tail — the operator surface that watches the other four.
The first four surfaces are intentionally connected. A tool authored in Tool Studio is exposed by the built-in MCP server, verified through the MCP Inspector, and consumed by Agentic Chat together with documents indexed in the Vector Database — without restart or redeploy at any step. Every chat turn, tool call, MCP exchange, and vector query is captured by Observability as it happens.
For a system-level view — runtime layers, data flows, and extension points behind these surfaces — see Architecture.
Further Reading¶
- Overview: return to the main product overview and documentation map
- Getting Started: install the app, configure providers, and choose a runtime
- Architecture: runtime layers, data flows, and extension points
- Tutorials: follow end-to-end workflows for tools, MCP, vector search, and agentic chat