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Developing Genkit Tooling

  • 4 installs
  • 6.3k repo stars
  • Updated August 5, 2026
  • genkit-ai/genkit

Follow best practices for authoring Genkit tooling, including CLI command and MCP server tool naming conventions, architectural patterns, and consistency guidelines.

About

Documents naming and architecture conventions for building Genkit CLI commands and MCP server tools, such as kebab-case noun:verb commands and snake_case verb_noun MCP tools. A developer uses it when authoring or extending Genkit tooling to keep commands and tools consistent.

  • CLI commands use kebab-case noun:verb; MCP tools use snake_case verb_noun
  • Covers naming conventions, architectural patterns, and consistency guidelines

Developing Genkit Tooling by the numbers

  • 4 all-time installs (skills.sh)
  • Ranked #13,372 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs4
repo stars6.3k
Last updatedAugust 5, 2026
Repositorygenkit-ai/genkit

What it does

Follow best practices for authoring Genkit tooling, including CLI command and MCP server tool naming conventions, architectural patterns, and consistency guidelines.

Files

SKILL.mdMarkdownGitHub ↗

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}` }],
  };
}

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