comfyui-character-mcp

An MCP server that exposes ComfyUI-generated character "avatars" as tools. Each character is a preset: a frozen ComfyUI workflow graph plus a small, named set of controls (pose, expression, seed, ...) that a caller is allowed to adjust. The model never sees or edits the ComfyUI graph itself - only the specific knobs each preset chooses to expose.

Why stdio, not HTTP

This server runs as a local subprocess that the MCP client (Claude Desktop / Claude Code) launches directly, talking JSON-RPC over stdin/stdout. That's the natural transport when ComfyUI and the MCP server both run on the same machine, since there's no networking or auth to set up. Returned images are sent as base64-encoded content blocks regardless of transport - stdio doesn't limit what can flow back to the client, it only changes how the process is reached. If ComfyUI ever needs to live on a different machine, or you want one long-running server shared by multiple clients, this can move to Streamable HTTP transport later without changing the tool logic at all.

Related MCP server: ComfyUI MCP Server

Requirements

• A running local ComfyUI instance (default: http://127.0.0.1:8188, override with the COMFYUI_URL environment variable)

• uv for dependency management

Running

uv run comfyui-character-mcp

To register it with an MCP client, point the client's config at this command (adjust the path to wherever you cloned this repo):

{
  "mcpServers": {
    "comfyui-character": {
      "command": "uv",
      "args": ["run", "--directory", "C:/Users/alexg/src/comfyui-character-mcp", "comfyui-character-mcp"]
    }
  }
}

Tools

• list_characters() - lists every loaded character, its description, and the expressions (emoji + label) it supports.

• set_expression(emoji, character?) - renders the avatar showing that expression and returns the image. character is optional; omit it to reuse the currently-selected character. Only emoji in the character's allow-list are accepted - anything else is rejected before it reaches the model.

• get_current_look() - reports the currently-selected character and its last expression (the small bit of session state the setter tools maintain).

How a render works

Each character is an img2img flow: the preset's reference image is uploaded to ComfyUI, VAE-encoded, and run through the sampler at a low denoise. Low denoise keeps the generated face locked to the reference (that's the identity mechanism); the chosen expression's prompt fragments nudge the face within that budget. Turn denoise up in the preset for more expression range, down to stay closer to the reference. If denoise alone can't hold identity across strong expressions, the next step is adding low-strength ControlNet edge-following.

This works well for expressions that reshape features already present in the reference (a frown, wider eyes, a smirk). It does not work for "additive" expressions that introduce geometry the reference doesn't have at all - cat ears/whiskers, sunglasses, a party hat. No amount of denoise fixes this: the base identity prompt (e.g. "portrait of a cheerful man...") keeps winning even near denoise 1.0, because the prompt and the reference agree on "this is a specific human face" and fight any addition to it. See vocabularies/expressions.json's _additive_emoji_limitation note. The fix isn't a bigger denoise number, it's giving those expressions their own reference image (a version of the character that already has cat ears, say) so the prompt and reference agree on what's being rendered - not yet supported, but the natural next step for this class of expression.

Before encoding, the reference image is also rescaled (via an ImageScale node) so its shorter side is ~512px, preserving aspect ratio - by default, computed automatically from the reference image's own dimensions (see output_width/output_height below). This is a quality setting, not a display one: SD1.5-family checkpoints are trained around 512px, and encoding either much smaller or much larger than that tends to hurt output quality regardless of denoise. Shrinking the image for chat display is a separate, later concern - see "Suggested system prompt" below for controlling that with plain markdown, independent of the actual render resolution.

Expression vocabulary

vocabularies/expressions.json is the shared, character-independent set of emoji. Each maps to {positive, negative} prompt fragments (CLIP can't read emoji, so this translation is mandatory). A preset overrides only the emoji it wants to tune via expression_overrides; everything else falls back to the shared default. The emoji keys are the allow-list set_expression() enforces.

Suggested system prompt

Some MCP clients bridge to models over a text-only tool-result API (common for local models served via Ollama/vLLM-style endpoints). Those clients receive the image content block from set_expression, save it to disk themselves, and hand the model back a text placeholder with a markdown image link to include in its reply - the model never "sees" the pixels, it just has to remember to echo that markdown back to the user. Models reliably forget this unless told to.

Beyond just remembering to include the image, plain markdown image syntax also forces a line break before and after the image, at full render resolution (~512px, kept large for generation quality - see "How a render works" above) - the picture ends up as a big block, disconnected from the model's commentary about it. A raw HTML <img> tag with a width fixes both problems at once: it shrinks the image for display and lets text wrap beside it (most chat renderers that support markdown also render inline HTML):

Don't do this (forces the image onto its own block, no wrap):

![Image](./image-1783113534781.png)

He grinned, clearly pleased with how that turned out.

Do this instead (text flows beside a small inline image):

<img src="./image-1783113534781.png" width="150" align="left"> He grinned,
clearly pleased with how that turned out, and asked what you wanted to see
next.

A system prompt covering all of this:

You have access to an avatar tool for this character. Guidelines:

- Call set_expression(emoji) whenever the character's emotional tone
  shifts in the conversation - don't wait to be asked for a picture.
- Only use emoji from the character's supported list (call
  list_characters() if you're unsure which ones are allowed). Using an
  unsupported emoji will be rejected.
- The tool result includes an image file reference. You MUST include that
  image in your reply - it will not be shown to the user unless you
  include it yourself.
- Wrap the image inline with your commentary instead of putting it on its
  own line: use `<img src="..." width="150" align="left">` (or
  `align="right"`) directly before the paragraph of text that comments on
  the expression, so the picture and the reaction read together. Don't use
  bare `![Image](...)` markdown for this - it breaks the image onto its
  own block and separates it from the text about it.
- Don't describe the image in words instead of showing it; the point of
  the tool is the picture, not a caption.

The middle two points matter for any client; the last two are the ones that fix "generated the image but never showed it" and "showed it, but as an ugly disconnected block" respectively.

Adding a character preset

Each character is a directory under presets/ named after its id, containing three standard-named files - presets/<id>/{preset,workflow}.json,reference.png:

1. workflow.json - a ComfyUI img2img workflow exported in API format (enable Dev Mode in ComfyUI settings, then "Save (API Format)"). This is the frozen graph; only the inputs named in bindings change per request.

2. preset.json - the character definition (no id field - the directory name is the id, so there's nothing to keep in sync):

   • base_positive / base_negative - the character's identity prompt and quality negatives. Expression fragments are appended to these.

   • denoise - the identity-vs-expression trade-off knob (not model-facing).

   • output_width / output_height (optional) - the resolution the reference image is rescaled to before VAE encoding, via a resize node in the workflow. If omitted, both are computed automatically from the reference image's own dimensions so its shorter side lands at 512px, preserving aspect ratio - only set these explicitly to override that. Needs matching output_width/output_height bindings pointed at the resize node.

   • bindings - maps logical roles (positive, negative, denoise, seed, reference_image, plus optionally output_width/ output_height) to [node_id, input_name] pairs in the workflow. This is the only place ComfyUI node ids appear.

   • expression_overrides (optional) - per-emoji fragment overrides.

3. reference.png - the reference portrait this character is generated from.

See presets/example/ for a worked example - before real use, swap reference.png, set ckpt_name in workflow.json to a checkpoint you have installed, and rewrite base_positive for your character.