About developing-genkit-tooling
developing-genkit-tooling is a practical guide for developers building Genkit tooling, covering CLI commands and MCP tools. It defines naming conventions, runtime interaction patterns, and error handling.
Key features
- 01CLI subcommand naming conventions
- 02MCP tool naming conventions
- 03Static and runtime tool architecture
- 04Genkit project runtime interaction patterns
- 05Structured error responses
Use cases
- 01Add CLI commands to Genkit
- 02Register MCP tools for a project runtime
- 03Implement static MCP tools for documentation
MCP support
The guide describes building Genkit MCP tools, including static documentation tools and tools that require a project runtime.
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ genkit-ai/ genkit --skill developing-genkit-toolingAfter installing, type/developing-genkit-toolingin Claude Code to confirm it loaded.
Details
From the project READMEDeveloping Genkit Tooling
Naming Conventions
Consistency in naming helps users and agents navigate the tooling.
CLI Commands
Use kebab-case with colon separators for subcommands.
- Format:
noun:verborcategory:action - Examples:
flow:run,eval:run,init - Arguments: Use camelCase in code (
flowName) but standard format in help text (<flowName>).
MCP Tools
Use snake_case for tool names to align with MCP standards.
- Format:
verb_noun - Examples:
list_flows,run_flow,list_genkit_docs,read_genkit_docs
CLI Command Architecture
Commands are implemented in cli/src/commands/ using commander.
Runtime Interaction
Most commands require interacting with the user's project runtime. Use the runWithManager utility to handle the lifecycle of the runtime process.
import { runWithManager } from '../utils/manager-utils';
// ... command definition ...
.action(async (arg, options) => {
await runWithManager(await findProjectRoot(), async (manager) => {
// Interact with manager here
const result = await manager.runAction({ key: arg });
});
});
Output Formatting
- Logging: Use
loggerfrom@genkit-ai/tools-common/utils. - Machine Readable: Provide options for JSON output or file writing when the command produces data.
- Streaming: If the operation supports streaming (like
flow:run), provide a--streamflag and pipe output to stdout.
MCP Tool Architecture
MCP tools in cli/src/mcp/ follow two distinct patterns: Static and Runtime.
Static Tools (e.g., Docs)
These tools do not require a running Genkit project context.
- Registration:
defineDocsTool(server: McpServer) - Dependencies: Only the
serverinstance. - Use Case: Documentation, usage guides, global configuration.
Runtime Tools (e.g., Flows, Runtime Control)
These tools interact with a specific Genkit project's runtime.
- Registration:
defineRuntimeTools(server: McpServer, options: McpToolOptions) - Dependencies: Requires
optionscontainingmanager(process manager) andprojectRoot. - Schema: MUST use
getCommonSchema(options.explicitProjectRoot, ...)to ensure the tool can accept aprojectRootargument when required (e.g., in multi-project environments).
// Runtime tool definition pattern
server.registerTool(
'my_runtime_tool',
{
inputSchema: getCommonSchema(options.explicitProjectRoot, {
myArg: z.string(),
}),
},
async (opts) => {
// Resolve project root before action
const rootOrError = resolveProjectRoot(
options.explicitProjectRoot,
opts,
options.projectRoot
);
if (typeof rootOrError !== 'string') return rootOrError;
// access manager via options.manager
}
);
Error Handling
MCP tools should generally catch errors and return them as content blocks with isError: true rather than throwing exceptions, which ensures the client receives a structured error response.
try {
// operation
} catch (err) {
const message = err instanceof Error ? err.message : String(err);
return {
isError: true,
content: [{ type: 'text', text: `Error: ${message}` }],
};
}