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IO-AtelierTech

ComfyUI MCP Server

by IO-AtelierTech
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

list_embeddings

Retrieve available text embedding names for use in ComfyUI prompts to enhance AI-generated content with specific styles or characteristics.

Instructions

List available text embeddings.

Returns list of embedding names that can be used in prompts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/comfy_mcp_server/tools/discovery.py:133-144 (handler)
    The handler function implementing the list_embeddings tool. It lists text embeddings by calling the get_embeddings helper, with error handling.
    @mcp.tool()
def list_embeddings(ctx: Context = None) -> list:
    """List available text embeddings.

    Returns list of embedding names that can be used in prompts.
    """
    if ctx:
        ctx.info("Listing embeddings...")
    try:
        return get_embeddings()
    except Exception as e:
        return [f"Error: {e}"]
  • src/comfy_mcp_server/api.py:262-264 (helper)
    Supporting helper function that fetches embeddings from the ComfyUI API endpoint.
    def get_embeddings() -> list:
    """Get available embeddings."""
    return comfy_get("/embeddings")
  • src/comfy_mcp_server/tools/__init__.py:26-26 (registration)
    Calls register_discovery_tools which defines and registers the list_embeddings tool among others.
    register_discovery_tools(mcp)

Tool Definition Quality

B3.2/5.0
Behavior2/5

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 states what the tool returns ('list of embedding names that can be used in prompts'), which is helpful, but lacks critical details such as whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or how the list is formatted (e.g., pagination, sorting). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 extremely concise and well-structured with two brief sentences that directly address purpose and output. Every word earns its place, with no redundant information or fluff. It's front-loaded with the core purpose followed by additional context about the return value.

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

Completeness3/5

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It explains what the tool does and what it returns, which covers the basics for a listing tool. However, without annotations or output schema, it should ideally provide more behavioral context (e.g., read-only nature, any limitations) to be fully complete for agent use.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description appropriately doesn't discuss parameters, which is correct for a parameterless tool. It adds value by explaining the return semantics ('list of embedding names that can be used in prompts'), compensating for the lack of output schema.

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

Purpose4/5

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

The description clearly states the tool's purpose with a specific verb ('List') and resource ('available text embeddings'), making it immediately understandable. It distinguishes itself from siblings by focusing on embeddings rather than workflows, nodes, or other resources. However, it doesn't explicitly differentiate from potential similar tools like 'list_models' or 'list_extensions' beyond the resource name.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or compare it to sibling tools like 'list_models' or 'list_extensions'. The agent must infer usage based solely on the tool name and description without explicit direction.

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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