description: End-to-end Spring AI Playground tutorials - Tool Studio authoring, MCP servers, vector RAG, default-tool recipes, and Agentic Chat.

Tutorials

These fifteen tutorials walk you from creating a single tool to composing chains over the bundled default catalog, with optional deep-dives off the main path - the MCP protocol surface, re-publishing tools through the MCP Server Proxy, gating tool calls behind human approval, turning a Prompt Library template into a reusable preset, uploading a file into chat for an agent to analyze, attaching an image for a vision model to see, and investigating documents with a filesystem pipeline. They follow the natural product workflow: build → validate → ground → compose.

The shipped chat default is qwen3.5:4b - fast, vision-capable, fine for the early tutorials. Switch to qwen3.5:9b or gemma4:e4b when you reach tutorials 4-7, where tool-calling reliability matters. Embeddings use qwen3-embedding:0.6b throughout. See Picking a model for the tradeoffs.

How these tutorials connect

flowchart LR
  T1["1. Author a Tool<br/>Tool Studio"]
  T2["2. Connect MCP<br/>MCP Server"]
  T3["3. Index a Doc<br/>Vector Database"]
  T4["4. Chat + Tools<br/>Agentic Chat"]
  T5["5. Chat + RAG<br/>Agentic Chat"]
  T6["6. Tools + RAG<br/>Agentic Chat"]
  T7["7. Tool Chain<br/>Weather → Slack"]
  T8["8. Default Tool Recipes<br/>JS-action chains"]
  T9["9. MCP Everything<br/>All 8 Primitives"]
  T10["10. Server Proxy<br/>Re-publish a tool"]
  T11["11. Approve in Chat<br/>Human-in-the-Loop"]
  T12["12. Template to Preset<br/>Prompt Library"]
  T13["13. Upload & Analyze<br/>requestFileUpload"]
  T14["14. Attach an Image<br/>native vision input"]
  T15["15. Investigate Documents<br/>Unix-pipeline tools"]
  T1 --> T2
  T2 --> T4
  T3 --> T5
  T4 --> T6
  T5 --> T6
  T6 --> T7
  T7 --> T8
  T2 -. deep dive .-> T9
  T2 -. proxy .-> T10
  T4 -. approve .-> T11
  T4 -. preset .-> T12
  T4 -. upload .-> T13
  T4 -. vision .-> T14
  T13 -. pipeline .-> T15
  classDef build fill:#eef2ff,stroke:#3F51B5,color:#1e1b4b
  classDef validate fill:#ecfdf5,stroke:#10b981,color:#064e3b
  classDef ground fill:#fff7ed,stroke:#f59e0b,color:#7c2d12
  classDef compose fill:#fdf2f8,stroke:#e11d48,color:#831843
  classDef bonus fill:#f5f3ff,stroke:#7c3aed,color:#4c1d95
  class T1 build
  class T2 validate
  class T3 ground
  class T4,T5,T6,T7,T8 compose
  class T9,T10,T11,T12,T13,T14,T15 bonus

Tutorials 1-3 produce reusable assets (a tool, an MCP connection, an indexed document). Tutorials 4-8 compose those assets in chat (4-6) and as code-level chains over the bundled default catalog (7-8). Tutorial 9 is an optional deep dive off Tutorial 2 - activate MCP Everything from the Default MCP Servers and exercise every Inspector primitive (Tools / Resources / Prompts / Ping / Notifications / Roots / Sampling / Elicitation) end-to-end. Tutorial 10 is a second deep dive off Tutorial 2 - re-publish a connected server's tool through the built-in MCP Server Proxy so chat and external clients can call it. Each tutorial is independently runnable in 3-20 minutes; the full main sequence takes about 50 minutes, plus ~44 minutes for the deep dives (MCP Everything, Server Proxy, Human-in-the-Loop approval, the file and image attachments, and the document pipeline).

!!! abstract "What you'll need" - Spring AI Playground running on http://localhost:8282. Follow Getting Started first if you haven't. - Ollama running, with qwen3.5:9b and qwen3-embedding:0.6b pulled. bash ollama pull qwen3.5:9b ollama pull gemma4:e4b ollama pull qwen3-embedding:0.6b - For Tutorial 7 only: SLACK_WEBHOOK_URL set in the launcher's Environment Variables.

Tutorial list

Picking a model

Tool calling and tool chaining quality depend heavily on the model. The selectable list in Agentic Chat → Settings → Model is driven by application.yaml's playground.chat.models. The shipped default is the small one - start there, and only upgrade if a tool turn comes back empty.

=== "qwen3.5:4b (default)" The shipped default. 3.4 GB. Fast on Apple Silicon, and the smallest build that ships vision tensors, so image attachments work out of the box. Use this for the chat sanity check before wiring up tools or RAG. Tool calling is best-effort - if a tool turn comes back empty, that is the signal to upgrade, not to rewrite the prompt.

=== "qwen3.5:9b" 6.6 GB. Stronger tool calling and multi-turn reasoning. The first upgrade target when qwen3.5:4b skips a tool call.

=== "gemma4:e4b" 9.6 GB. Strongest natural-language quality. Pick this for tutorial 7 (multi-step tool chains) where the model has to reason about a tool result rather than just call it.

=== "gpt-oss:20b" 13 GB. OpenAI's open-weights reasoning model. A good cross-check when you suspect a result depends heavily on which model family you picked.

!!! tip "Where the model selector lives" Open Agentic Chat, click the gear icon at the top right, change Model, then click Apply & New Chat. The chat header reflects the change.

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
• Features: the main product areas and what they do