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IO-AtelierTech

ComfyUI MCP Server

by IO-AtelierTech
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

generate_workflow_name

Generate creative workflow names for saving ComfyUI workflows, producing random slug-style names like 'cosmic-penguin' or 'mighty-purple-narwhal' to help organize your automation projects.

Instructions

Generate a random funny workflow name.

    Args:
        words: Number of words in the name (2-4, default: 2)

    Returns a slug like 'cosmic-penguin' or 'mighty-purple-narwhal'.
    Use this when saving new workflows for creative naming.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordsNoNumber of words (2-4)

Implementation Reference

  • src/comfy_mcp_server/tools/workflow.py:334-352 (handler)
    The handler function for the 'generate_workflow_name' tool. It generates a random funny name using coolname.generate_slug(words), with input validation clamping words to 2-4. Includes inline schema via pydantic Field and docstring description. Decorated with @mcp.tool() for registration.
    @mcp.tool()
def generate_workflow_name(
    words: int = Field(default=2, description="Number of words (2-4)"),
    ctx: Context = None,
) -> str:
    """Generate a random funny workflow name.

    Args:
        words: Number of words in the name (2-4, default: 2)

    Returns a slug like 'cosmic-penguin' or 'mighty-purple-narwhal'.
    Use this when saving new workflows for creative naming.
    """
    if ctx:
        ctx.info(f"Generating {words}-word workflow name")

    words = max(2, min(4, words))  # Clamp to valid range
    return generate_slug(words)
  • src/comfy_mcp_server/tools/__init__.py:27-27 (registration)
    Within register_all_tools, calls register_workflow_tools(mcp) which registers the generate_workflow_name tool (via its @mcp.tool() decorator in workflow.py).
    register_workflow_tools(mcp)

Tool Definition Quality

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it generates random funny names, returns a slug format (e.g., 'cosmic-penguin'), and has a default parameter value. However, it lacks details on randomness characteristics (e.g., seed, reproducibility) or potential errors, which would elevate the score to 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by parameter details, return format, and usage guidelines in a logical flow. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, parameters, return format, and usage context. However, it lacks output schema details (e.g., exact structure of the slug), which slightly reduces completeness for a tool with no structured output documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents the 'words' parameter fully. The description adds minimal value by restating the parameter range (2-4) and default (2), but it doesn't provide additional semantic context beyond what's in the schema. Since there's only one parameter, the baseline is 4, but it doesn't fully compensate with extra insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('generate a random funny workflow name') and distinguishes it from sibling tools like 'create_workflow' or 'save_workflow' by focusing on name generation rather than workflow creation or management. It specifies the resource ('workflow name') and the creative nature ('funny'), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Use this when saving new workflows for creative naming.' This provides clear context for its application, distinguishing it from other workflow-related tools like 'create_workflow' or 'save_workflow' that handle different aspects of workflow management.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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