MCPInstall

developing-genkit-tooling

by genkit-aiBackend & APIs

Best practices for authoring Genkit tooling, including CLI commands and MCP server tools. Covers naming conventions, architectural patterns, and consistency guidelines.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/genkit-ai/genkit --skill developing-genkit-tooling
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads developing-genkit-tooling automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Developing 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:verb or category: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 logger from @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 --stream flag 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 server instance.
  • 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 options containing manager (process manager) and projectRoot.
  • Schema: MUST use getCommonSchema(options.explicitProjectRoot, ...) to ensure the tool can accept a projectRoot argument 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}` }],
  };
}

FAQ

What does the developing-genkit-tooling skill do?

Best practices for authoring Genkit tooling, including CLI commands and MCP server tools. Covers naming conventions, architectural patterns, and consistency guidelines.

How do I install developing-genkit-tooling?

Run npx skills add https://github.com/genkit-ai/genkit --skill developing-genkit-tooling in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/developing-genkit-tooling/ (personal) or .claude/skills/developing-genkit-tooling/ (project).

Which AI agents can use developing-genkit-tooling?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is developing-genkit-tooling safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains developing-genkit-tooling?

It is published by genkit-ai in genkit-ai/genkit. It has 54 recorded installs.

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