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Atomizer/mcp_server/tools/__init__.py

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Initial commit: NX OptiMaster project structure - Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 07:56:35 -05:00
"""
Rebrand project from nx-optimaster to Atomizer - Update project name in all documentation files - Update GitHub repository references to Anto01/Atomizer - Update Python package name to 'atomizer' - Update conda environment name references - Update all module docstrings with new branding 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 08:05:19 -05:00
MCP Tools for Atomizer
Initial commit: NX OptiMaster project structure - Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 07:56:35 -05:00
Available tools:
- discover_fea_model: Analyze .sim files to extract configurable elements
- build_optimization_config: Generate optimization config from LLM instructions
- start_optimization: Launch optimization run
- query_optimization_status: Get current iteration status
- extract_results: Parse FEA result files
- run_nx_journal: Execute NXOpen scripts
- search_nxopen_docs: Search NXOpen API documentation
"""
from typing import Dict, Any
feat: Implement Option A - MCP Model Discovery tool This commit implements the first phase of the MCP server as outlined in PROJECT_SUMMARY.md Option A: Model Discovery. New Features: - Complete .sim file parser (XML-based) - Expression extraction from .sim and .prt files - Solution, FEM, materials, loads, constraints extraction - Structured JSON output for LLM consumption - Markdown formatting for human-readable output Implementation Details: - mcp_server/tools/model_discovery.py: Core parser and discovery logic - SimFileParser class: Handles XML parsing of .sim files - discover_fea_model(): Main MCP tool function - format_discovery_result_for_llm(): Markdown formatter - mcp_server/tools/__init__.py: Updated to export new functions - mcp_server/tools/README.md: Complete documentation for MCP tools Testing & Examples: - examples/test_bracket.sim: Sample .sim file for testing - tests/mcp_server/tools/test_model_discovery.py: Comprehensive unit tests - Manual testing verified: Successfully extracts 4 expressions, solution info, mesh data, materials, loads, and constraints Validation: - Command-line tool works: python mcp_server/tools/model_discovery.py examples/test_bracket.sim - Output includes both Markdown and JSON formats - Error handling for missing files and invalid formats Next Steps (Phase 2): - Port optimization engine from P04 Atomizer - Implement build_optimization_config tool - Create pluggable result extractor system References: - PROJECT_SUMMARY.md: Option A (lines 339-350) - mcp_server/prompts/system_prompt.md: Model Discovery workflow
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from .model_discovery import discover_fea_model, format_discovery_result_for_llm
feat: Add MCP build_optimization_config tool Integrate OP2 data extraction with optimization config builder: - Add build_optimization_config() MCP tool - Add list_optimization_options() helper - Add format_optimization_options_for_llm() formatter - Update MCP tools documentation with full API details - Test with bracket example, generates valid config Features: - Discovers design variables from FEA model - Lists 4 available objectives (mass, stress, displacement, volume) - Lists 4 available constraints (stress/displacement/mass limits) - Validates user selections against model - Generates complete optimization_config.json Tested with examples/bracket/Bracket_sim1.sim: - Found 4 design variables (support_angle, tip_thickness, p3, support_blend_radius) - Created config with 2 objectives, 2 constraints, 150 trials 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 10:23:02 -05:00
from .optimization_config import (
build_optimization_config,
list_optimization_options,
format_optimization_options_for_llm
)
Initial commit: NX OptiMaster project structure - Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 07:56:35 -05:00
__all__ = [
"discover_fea_model",
feat: Implement Option A - MCP Model Discovery tool This commit implements the first phase of the MCP server as outlined in PROJECT_SUMMARY.md Option A: Model Discovery. New Features: - Complete .sim file parser (XML-based) - Expression extraction from .sim and .prt files - Solution, FEM, materials, loads, constraints extraction - Structured JSON output for LLM consumption - Markdown formatting for human-readable output Implementation Details: - mcp_server/tools/model_discovery.py: Core parser and discovery logic - SimFileParser class: Handles XML parsing of .sim files - discover_fea_model(): Main MCP tool function - format_discovery_result_for_llm(): Markdown formatter - mcp_server/tools/__init__.py: Updated to export new functions - mcp_server/tools/README.md: Complete documentation for MCP tools Testing & Examples: - examples/test_bracket.sim: Sample .sim file for testing - tests/mcp_server/tools/test_model_discovery.py: Comprehensive unit tests - Manual testing verified: Successfully extracts 4 expressions, solution info, mesh data, materials, loads, and constraints Validation: - Command-line tool works: python mcp_server/tools/model_discovery.py examples/test_bracket.sim - Output includes both Markdown and JSON formats - Error handling for missing files and invalid formats Next Steps (Phase 2): - Port optimization engine from P04 Atomizer - Implement build_optimization_config tool - Create pluggable result extractor system References: - PROJECT_SUMMARY.md: Option A (lines 339-350) - mcp_server/prompts/system_prompt.md: Model Discovery workflow
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"format_discovery_result_for_llm",
Initial commit: NX OptiMaster project structure - Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 07:56:35 -05:00
"build_optimization_config",
feat: Add MCP build_optimization_config tool Integrate OP2 data extraction with optimization config builder: - Add build_optimization_config() MCP tool - Add list_optimization_options() helper - Add format_optimization_options_for_llm() formatter - Update MCP tools documentation with full API details - Test with bracket example, generates valid config Features: - Discovers design variables from FEA model - Lists 4 available objectives (mass, stress, displacement, volume) - Lists 4 available constraints (stress/displacement/mass limits) - Validates user selections against model - Generates complete optimization_config.json Tested with examples/bracket/Bracket_sim1.sim: - Found 4 design variables (support_angle, tip_thickness, p3, support_blend_radius) - Created config with 2 objectives, 2 constraints, 150 trials 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 10:23:02 -05:00
"list_optimization_options",
"format_optimization_options_for_llm",
Initial commit: NX OptiMaster project structure - Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 07:56:35 -05:00
"start_optimization",
"query_optimization_status",
"extract_results",
"run_nx_journal",
]
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