Critical failures across all dimensions. Tool definitions are inferred from a reference document (docs/generate/tool_reference.py) rather than visible in actual server code. No input schemas are visible in the provided source. Descriptions are present but extremely brief (5-15 chars average), far below the 50-200 char LLM-optimized baseline. No parameter documentation visible. No output schemas documented. No error handling guidance. The server exposes 69 tools with minimal structure, this is a documentation-generation artifact, not a production-grade MCP implementation.

• Provide the actual server.py source code showing tool registration with FastMCP @mcp.tool decorators, input schemas, and output type hints. Current evaluation is based on inferred definitions only.
• Expand all tool descriptions to 50-200 characters. Include WHAT the tool does, WHEN to use it (vs similar tools), and any prerequisites. Example: 'select' → 'Select atoms or residues by criteria (chain, residue number, atom type). Use before color/size/hide to target specific parts of the structure.'
• Document every parameter with type, description, constraints, and valid values. Use enums for constrained inputs (e.g., color: enum of valid color names). Example: 'color(selection_id: str, color: enum[red|blue|green|...], opacity: float 0.0-1.0)'.
• Document output schemas for all tools, especially list_* and analysis tools. Specify field names, types, and which IDs/references are returned for chaining. Example: 'list_selections returns [{id: str, name: str, atom_count: int}]'.
• Add error handling guidance. For tools that can fail (load_trajectory, load_volume, interface analysis), describe what errors are possible and what the LLM should do next. Example: 'If file not found, try list_volumes() to see available files.'