Create AI Tool

Create AI Tool

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

IMPORTANT: Never modify the crates/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 crates/<crate>/src/inbound/toolset/.

Study an existing tool for patterns:

• crates/documents/src/inbound/toolset/ — tools: read_content.rs, read_metadata.rs, create_document.rs
• crates/email/src/inbound/toolset/ — tools: send_email.rs, get_thread.rs, update_thread_labels.rs
• crates/soup/src/inbound/toolset/ — tool: list_entities.rs
• crates/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 crates/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 crates/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 crates/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 crates/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 the repository root:

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 apps/web/:

bun gen-tools

This builds crates/ai_tools/src/bin/gen_tool_schemas.rs, generates crates/ai_tools/schemas/tools.json, and transpiles the schemas into TypeScript at apps/web/src/lib/service-clients/service-cognition/generated/tools/.