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genpark-consistent-character-micro-texture-stylizer-skill MCP server · F · ToolBench

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genpark-consistent-character-micro-texture-stylizer-skill

github.com/alphaparkinc/genpark-consistent-character-micro-texture-stylizer-skill

MCP server for generating consistent styled characters with micro-texture detail and ray-traced lighting

2026-07-28+STDIO1 tools

36/100

Definition Quality50

Protocol Readiness15

Supportability28

Spec posture

Inferred effective spec 2026-07-28+·current baseline 2026-07-28

Static source inference · medium confidence

Current-spec patterns detected

Summary

Single tool 'execute_skill_action' has a reasonable description (89 chars, within 10-1024 range) and three parameters with type definitions and descriptions. However, the tool definition is inferred from client.py rather than explicitly registered in mcp_server.py. The mcp_server.py file contains only a stub that prints a JSON status message and does not actually register tools, define schemas, or handle tool calls. No input validation, error handling, or output schema documentation. The server is non-functional as an MCP implementation.

Tools (1)

execute_skill_actionread onlysource verified50/100

Execute character generation with consistent styling and micro-texture detail

Issues (5)

CRITICALtool ↗

Tool definition inferred from client.py, not explicitly registered in mcp_server.py. mcp_server.py contains only a stub that prints status; it does not implement MCP tool registration, schema validation, or request handling.

execute_skill_action

CRITICALresponse-shaper ↗

No output schema documented. Tool returns a dict with 7 fields (character_job_id, seed_identity, facial_consistency_similarity_pct, skin_micro_texture_resolution, lighting_ray_tracing_fidelity_score, generated_character_image_url, lora_embedding_exported) but LLM has no visibility into structure, types, or field meanings.

execute_skill_action

HIGHrecovery-guide ↗

No error handling or recovery guidance. Tool has no try-catch, validation, or error messages. If the underlying client fails, the MCP server will crash or return an unstructured exception.

execute_skill_action

HIGHconstrained-input ↗

Recommendations

Implement proper MCP server in mcp_server.py: register tools with Tool objects, define input/output schemas, and handle CallToolRequest messages per the MCP spec.
Document the output schema as a JSON Schema object with all 7 fields typed (string, number, boolean, URL format) and described for LLM consumption.
Add enum constraint to 'style_preset' parameter listing all valid presets (e.g., ['CINEMATIC_V6_MICRO_TEXTURE', 'PHOTOREALISTIC_V5', 'ANIME_V4', ...]).
Wrap the client call in try-catch and return structured error responses with actionable recovery hints (e.g., 'Invalid style_preset. Valid options: ...' or 'Generation timeout. Retry with a smaller scene_action.').
Migrate from STDIO to HTTP+SSE or Streamable HTTP transport to enable remote accessibility and compatibility with hosted MCP clients.

Score history

Overall score trend

First recorded score · v2 rubric

36/100

Scored	Grade	Overall	Spec posture	Rubric
2026-09-23	F	36	2026-07-28+	v2