Critical gaps across all dimensions. Tool descriptions are minimal (10-30 chars), providing almost no context for LLM selection. No input schemas visible in source code, tool definitions appear inferred from JavaScript bridge rather than explicitly registered with JSON Schema. Parameters are undocumented. Output schemas are not specified. The server relies on a dcc-mcp-core framework that abstracts tool registration, making it impossible to verify schema compliance from the provided source. This is a framework-dependent implementation with minimal direct tool definition visibility.

• Explicitly register all tools with full JSON Schema input definitions. Include type, description, enum constraints, and min/max for every parameter. Move from **kwargs to typed signatures.
• Expand tool descriptions to 50-200 characters. Answer: What does it do? When should the LLM call it? What does it return? Example: 'create_map: Creates a new Tiled map file with specified dimensions and tile size. Use this before adding layers or tilesets. Returns map_id and file_path.'
• Document output schemas for each tool. Specify which fields are returned, their types, and which are required for chaining (e.g., map_id needed by add_object_layer).
• Add parameter descriptions to skill_tools.py. For each tool, document what parameters it accepts, their types, valid ranges/enums, and defaults. Example: 'map_path (string, required): Absolute path to .tmx file. width (int, 1-10000): Map width in tiles.'
• Implement structured error responses with recovery guidance. Instead of fail('No Tiled map reader'), return {error: 'unsupported_format', message: 'No reader for .xyz. Supported: .tmx, .tsx', retryable: false}.