create-ai-tool

name	create-ai-tool
description	Build a new AI tool end-to-end — Rust implementation, toolset wiring, infra, schema generation, and frontend UI.

Create AI Tool

This skill walks through building a new AI tool from scratch. Before writing any code, read the design guide at rust/cloud-storage/ai_toolset/TOOL_DESIGN.md and the framework docs/examples in rust/cloud-storage/ai_toolset/src/lib.rs.

IMPORTANT: Never modify the rust/cloud-storage/ai_toolset/ crate. It is the framework — you build tools that use it.

Step 1: Write the tool

Decide which domain crate the tool belongs in. Tools live at rust/cloud-storage/<crate>/src/inbound/toolset/.

Study an existing tool for patterns:

• rust/cloud-storage/documents/src/inbound/toolset/ — tools: read_content.rs, read_metadata.rs, create_document.rs
• rust/cloud-storage/email/src/inbound/toolset/ — tools: send_email.rs, get_thread.rs, update_thread_labels.rs
• rust/cloud-storage/soup/src/inbound/toolset/ — tool: list_entities.rs
• rust/cloud-storage/call/src/inbound/toolset/ — call-related tools

Each tool is a struct that derives JsonSchema and Deserialize, with #[schemars(title = "...", description = "...")] on the struct and #[schemars(description = "...")] on each field. The struct implements AsyncTool<Context> from the ai_toolset crate.

Create a new file for your tool (e.g. my_tool.rs), add it as a mod in the toolset's mod.rs, and wire it into the toolset's AsyncToolSet::new().add_tool::<...>() chain.

Step 2: Create the tool context

If your tool needs dependencies (DB connections, service clients) that aren't already in an existing context, define a new context struct in the toolset's mod.rs. See rust/cloud-storage/documents/src/inbound/toolset/mod.rs for the DocumentToolContext pattern.

The context must be Clone and derivable from the parent ToolServiceContext via FromRef.

If the tool's dependencies are already available in an existing context (e.g. it only needs Arc<ToolScribe>), you can use that context directly — no new struct needed.

Step 3: Add the toolset to all_tools in the ai_tools crate

Edit rust/cloud-storage/ai_tools/src/lib.rs:

• Import your toolset function and context type
• Add .add_tool::<YourTool, YourContext>() or .add_subtoolset::<YourToolContext>(your_toolset()) to the all_tools() function

Step 4: Add the context to ToolServiceContext

Edit rust/cloud-storage/ai_tools/src/tool_context.rs:

• Add any new type aliases for your service implementations (follow the Tool* naming pattern)
• Add your tool context field to the ToolServiceContext struct
• Implement FromRef<ToolServiceContext> for your context if needed (or derive it — the struct uses #[derive(FromRef)])

Step 5: Wire up env vars and service construction

Edit rust/cloud-storage/ai_tools/src/build_context.rs:

• Add any new env vars to the env_var! or maybe_env_var! blocks
• Construct your service/context in build_tool_service_context_from_env
• Add it to the returned ToolServiceContext

Step 6: Update infra (if new env vars or AWS resources are needed)

Edit infra/packages/shared/src/ai_tools.ts:

• Add new env vars to the envVars array in getAiToolsInfra()
• Add any new secret ARNs, queue ARNs, or bucket ARNs to the respective arrays
• Add any new Pulumi stack references needed to resolve the values

Step 7: Rust checks

Run from rust/cloud-storage/:

cargo fmt
cargo clippy -p ai_tools
cargo test -p <your_domain_crate>

Fix any warnings or errors before proceeding.

Step 8: Generate frontend types

Run from js/app/:

bun gen-tools

This builds rust/cloud-storage/ai_tools/src/bin/gen_tool_schemas.rs, generates rust/cloud-storage/ai_tools/schemas/tools.json, and transpiles the schemas into TypeScript at js/app/packages/service-clients/service-cognition/generated/tools/.

Step 9: Check what frontend UI is needed

Run from js/app/:

bun check

This runs tsc --noEmit and will report type errors — specifically, the toolHandlers map in js/app/packages/core/component/AI/component/tool/handler.tsx will be missing your new tool name. The errors tell you exactly what to implement.

Step 10: Read existing tool UI for patterns

The tool UI components live at js/app/packages/core/component/AI/component/tool/. Study existing renderers:

• Search.tsx — search results rendering
• ReadContent.tsx / ReadMetadata.tsx — document tool UI
• SendEmail.tsx — email tool UI
• ListEntities.tsx — list display
• Properties.tsx — property get/set tools
• ListCallRecords.tsx / ReadCallRecord.tsx — call tools
• BaseTool.tsx — shared base component

Each tool needs a handler object implementing ToolHandler (from ToolRenderer.tsx) with at minimum a render component. Use createToolRenderer to create it.

Step 11: Write the tool UI

1. Create a new component file at js/app/packages/core/component/AI/component/tool/YourTool.tsx
2. Export a handler using createToolRenderer
3. Register it in js/app/packages/core/component/AI/component/tool/handler.tsx:
   • Import your handler
   • Add it to the toolHandlers map with the key matching your tool's schema title

Every tool renderer must show results. Use the expandable dropdown pattern from Search.tsx / ListEntities.tsx:

• Use BaseTool (from BaseTool.tsx) as the wrapper. It accepts a response prop for expandable content.
• Create const [isExpanded, setIsExpanded] = createSignal(false) to track open/closed state.
• Access the response via ctx.response?.data (typed from the generated schema).
• Show a status summary (e.g. hit count, "No Results") on the right side of the tool row.
• Render a CaretRight toggle button (@icon/regular/caret-right.svg?component-solid) that rotates 90deg when expanded.
• Pass the expanded content into BaseTool's response prop, gated on isExpanded().

For tools that return text/string results (not entity lists), render the response string in a <pre> or similar block inside the response prop. The key point: the response data must always be surfaced in the UI via the dropdown — never silently swallowed.

Step 12: Frontend checks

Run from js/app/:

bun format
bun check

Fix any remaining type or formatting errors.