Server provides 7 tools with complete input schemas and descriptions. However, critical gaps limit production readiness: (1) No output schemas documented, LLMs cannot predict response structure for downstream chaining; (2) Parameter descriptions lack constraints (ranges, enums, formats), e.g., 'timeout' has no bounds, 'cpus' no min/max; (3) No error handling guidance, tools return generic 'Tool error: {e}' with no recovery hints; (4) Two tools (abaqus_run_modeling, abaqus_run_python) accept arbitrary code execution with no safety guardrails, validation, or sandboxing warnings; (5) Tool names are descriptive but descriptions could be more LLM-optimized (avg 150 chars, within baseline but some lack WHEN/WHY context). Schemas are present and typed, placing this above F/D tier, but missing output documentation and error recovery patterns prevent higher scores.

• Document output schemas for all 7 tools. Example for abaqus_submit_job: {job_name: string, output_files: {odb: string, log: string, msg: string}, status: 'submitted'|'running'|'completed'|'failed'}. This enables LLMs to chain tools correctly.
• Add numeric constraints to parameters: cpus (1 - 128), timeout (1 - 86400 seconds), max_per_field (1 - 10000), frame_index (must be -1 or ≥0). Include these in parameter descriptions, not just schema.
• Implement error categorization in handle_call_tool. Return structured errors: {error_type: 'not_found'|'invalid_input'|'timeout'|'permission_denied', message: '...', recovery_hint: 'Try ...'}. This guides LLM retry logic.
• For code execution tools, add input validation: reject scripts containing 'import os', 'subprocess', 'open()' patterns; document sandbox limitations; require explicit timeout and resource limits. Or wrap scripts in a safe execution harness.
• Enhance tool descriptions with decision trees. E.g., 'Use abaqus_submit_job with wait=true for synchronous execution (blocks until done), or wait=false for async (check status later with abaqus_job_status).' This clarifies when to use each tool.