MCP server for generating isometric tilemap worlds with sprite synthesis, seamless edge matching, and physics collision polygons
Critical failures across all dimensions. Tool 1 (execute_skill_action) has no visible input schema in source code, schema score must be 0. Tool 2 (synthesize_tilemap_world_pack) has a schema visible in the rubric input but NOT in the actual source code provided (client.py shows only a method signature with defaults, no JSON Schema registration). Both tools lack proper MCP server registration, mcp_server.py prints a static JSON response and does not implement the MCP protocol. No tool definitions are explicitly registered with the MCP server. Descriptions are present but generic (Tool 1: 10 words; Tool 2: 23 words). No error handling, no output schema documentation, no parameter validation. This is a stub implementation, not a functional MCP server.
Execute a skill action for the generative isometric tilemap sprite world synthesizer
Generate an isometric tilemap world pack with specified biome theme and grid resolution, including seamless edge-matched tiles, collision polygons, spritesheet, and Tiled TMX map
No input schemas visible in source code. Tool 1 has no schema at all. Tool 2's schema is inferred from rubric input, not from actual mcp_server.py registration.
MCP server does not implement the MCP protocol. mcp_server.py prints a static JSON blob and exits. No request/response handling, no tool invocation dispatch, no proper server loop. This is not a functional MCP server.
Tool descriptions are too short and generic. Tool 1: 'Execute a skill action...' (10 words, 85 chars). Tool 2 is better at 23 words but lacks WHEN to use it, prerequisites, or what it returns. Baseline: 194 chars average for A+ tools.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 25 | 2026-07-28+ | v2 |
No output schema documented. Tool 2 returns a dict with 6 fields (world_pack_id, theme, grid_resolution_px, etc.) but this structure is not declared in any schema. LLMs cannot plan downstream calls without knowing return fields.
No error handling or recovery guidance. If synthesis fails, the client returns nothing. No error classification, no actionable messages, no retry hints.