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Glama
wonderstone843

Vision QA MCP

by wonderstone843
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
ANTHROPIC_MODELNoOptional. The model to use for QA (default: claude-opus-4-8). Can be set to claude-haiku-4-5 or claude-sonnet-4-6 for cost savings.claude-opus-4-8
ANTHROPIC_API_KEYYesYour Anthropic API key required for using Claude vision.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
qa_checkA

Run a quality-control check on a generated image and return a structured verdict.

Call this immediately after generating an image, before showing it to the user.

Parameters:

  • image_path: Absolute path to the generated image to review.

  • reference_images: Optional list of reference image paths to compare against (e.g. character/style references). Up to 3 are used.

  • character_rules: Free-text rules the image must follow, e.g. "The pilot has NO eyebrows; the jacket has horizontal stripes."

  • style_notes: Free-text description of the expected visual style.

  • scene_type: One of solo, portrait, battle, combat, group, action, interview. Adjusts composition expectations (e.g. portraits may face camera).

  • pass_threshold: Minimum overall score (0-1) to pass. Default 0.7.

Returns a dict with: passed (bool), overall_score, character_accuracy, style_consistency, quality_score, composition_score (all 0-1), issues (list of {severity, category, description, recommendation}), should_regenerate (bool), notes, and model.

list_scene_typesA

List the supported scene_type values and what each one expects.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one lists scene types and their expectations, the other performs a quality check on images. There is no overlap or ambiguity.

Naming Consistency4/5

Both tools use snake_case, but one follows a verb_noun pattern (list_scene_types) while the other uses an abbreviated prefix (qa_check). The pattern is mostly consistent but has a minor deviation.

Tool Count3/5

With only 2 tools, the server is very focused. While it covers its core purpose, the count is on the low end and may feel thin for some use cases.

Completeness3/5

The tool set provides the essential functions: listing scene types and running a QA check. However, there is no tool to inspect or manage scene types beyond listing, and no tool to modify thresholds or references, leaving minor gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources