Raycast AI Extensions

Raycast AI Extensions

AI extensions let Raycast AI call your extension's tools to answer questions and take actions ("@your-extension create an issue titled …"). You provide the tools; the model decides when to call them. Build on raycast-core (manifest + command model); this spoke covers the AI surface. Confirm current signatures at developers.raycast.com/ai.

Mental model

• A tool is a typed function the AI may invoke: it receives validated input and returns data the model uses to continue. Tools are the AI analog of commands.
• Declare tools in package.json → tools[] (each with a name, title, and description the model reads to decide relevance), with a matching implementation file that default-exports the tool function.
• The extension's description and any AI instructions steer when/how the AI uses your tools — write them for the model, not the user.

Designing good tools

• One clear job per tool. Prefer getIssues + createIssue over a mega-tool with a mode arg — the model routes better to focused tools.
• Strong descriptions. The tool/param descriptions are the model's only routing signal; say what it does, when to use it, and what each argument means.
• Typed inputs. Validate every argument; never trust the model's input blindly (same boundary discipline as user input).
• Read vs write. Mark/structure side-effecting tools so they require confirmation before mutating (create/delete/send). Read tools can run freely.

Confirmations & safety

• Any tool that changes state, spends money, or sends something should request explicit user confirmation before executing (Raycast surfaces a confirmation UI).
• Return concise, structured results; don't dump secrets or huge payloads into the model context.

Evals

• Raycast supports evals (in package.json) that assert the AI calls the right tool with the right args for representative prompts. Add evals for your core flows so model/prompt changes don't silently regress routing.

Build & ship

• Develop with ray develop and test via the AI Chat / @extension mentions.
• Ships through the same Store flow → raycast-store-publishing.

Guardrails

• Tool/param descriptions are the routing brain — invest in them.
• Validate inputs; gate every side-effecting tool behind confirmation.
• Keep tools small and single-purpose; cover them with evals.
• Never leak preference secrets into tool results or the model context.
• Verify exact tools[] schema, function signature, and eval format against developers.raycast.com/ai (this is the model, not a frozen API).