alignment_check
Check if a decision aligns with your principles by searching principles files and semantic history for guidance.
Instructions
Check if a decision aligns with your principles. Searches principles file and semantic history for guidance.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| decision | Yes |
Implementation Reference
- The 'alignment_check' tool handler function in brain_mcp/server/tools_synthesis.py. It evaluates if a given decision aligns with principles by checking them against the decision text and performing a semantic search for related past decisions.
def alignment_check(decision: str) -> str: """ Check if a decision aligns with your principles. Searches principles file and semantic history for guidance. """ decision_lower = decision.lower() principles = get_principles() output = [f"## Alignment Check: {decision}\n"] # 1. Check principles (YAML/JSON) principles_section = principles.get("principles", principles.get( "SECTION_2_THE_EIGHT_UNIVERSAL_PRINCIPLES_DETAILED", {} )) relevant_principles = [] if isinstance(principles_section, dict): for key, principle in principles_section.items(): if isinstance(principle, dict): name = principle.get("name", "").lower() definition = principle.get("definition", "").lower() if (decision_lower in name or decision_lower in definition or any(word in definition for word in decision_lower.split() if len(word) > 4)): relevant_principles.append({ "name": principle.get("name", key), "definition": principle.get("definition", ""), "formula": principle.get("core_formula") or principle.get("implementation_formula"), }) elif isinstance(principles_section, list): for principle in principles_section: if isinstance(principle, dict): name = principle.get("name", "").lower() definition = principle.get("definition", "").lower() description = principle.get("description", "").lower() text = f"{name} {definition} {description}" if any(word in text for word in decision_lower.split() if len(word) > 4): relevant_principles.append({ "name": principle.get("name", "Unknown"), "definition": principle.get("definition", principle.get("description", "")), "formula": principle.get("formula"), }) if relevant_principles: output.append("### Relevant Principles:\n") for p in relevant_principles[:3]: output.append(f"**{p['name']}**") output.append(f"> {p['definition'][:200]}...") if p.get("formula"): output.append(f"_Formula: {p['formula']}_\n") # 2. Semantic search for related past decisions cfg = get_config() embedding = get_embedding(decision) if embedding and cfg.lance_path.exists(): results = lance_search(embedding, limit=5, min_sim=0.35) if results: output.append("### Related Past Thinking:\n") for title, content, year, month, sim in results: preview = content[:200] + "..." if len(content) > 200 else content output.append(f"**[{year}-{month:02d}]** {title or 'Untitled'} (sim: {sim:.2f})") output.append(f"> {preview}\n") if len(output) == 1: output.append("_No direct alignment guidance found. Try rephrasing or use semantic_search._") return "\n".join(output) - The handler function `alignment_check` for the MCP tool of the same name. It performs an alignment check against principles and past thinking.
@mcp.tool() def alignment_check(decision: str) -> str: """ Check if a decision aligns with your principles. Searches principles file and semantic history for guidance. """ decision_lower = decision.lower() principles = get_principles() output = [f"## Alignment Check: {decision}\n"] # 1. Check principles (YAML/JSON) principles_section = principles.get("principles", principles.get( "SECTION_2_THE_EIGHT_UNIVERSAL_PRINCIPLES_DETAILED", {} )) relevant_principles = [] if isinstance(principles_section, dict): for key, principle in principles_section.items(): if isinstance(principle, dict): name = principle.get("name", "").lower() definition = principle.get("definition", "").lower() if (decision_lower in name or decision_lower in definition or any(word in definition for word in decision_lower.split() if len(word) > 4)): relevant_principles.append({ "name": principle.get("name", key), "definition": principle.get("definition", ""), "formula": principle.get("core_formula") or principle.get("implementation_formula"), }) elif isinstance(principles_section, list): for principle in principles_section: if isinstance(principle, dict): name = principle.get("name", "").lower() definition = principle.get("definition", "").lower() description = principle.get("description", "").lower() text = f"{name} {definition} {description}" if any(word in text for word in decision_lower.split() if len(word) > 4): relevant_principles.append({ "name": principle.get("name", "Unknown"), "definition": principle.get("definition", principle.get("description", "")), "formula": principle.get("formula"), }) if relevant_principles: output.append("### Relevant Principles:\n") for p in relevant_principles[:3]: output.append(f"**{p['name']}**") output.append(f"> {p['definition'][:200]}...") if p.get("formula"): output.append(f"_Formula: {p['formula']}_\n") # 2. Semantic search for related past decisions cfg = get_config() embedding = get_embedding(decision) if embedding and cfg.lance_path.exists(): results = lance_search(embedding, limit=5, min_sim=0.35) if results: output.append("### Related Past Thinking:\n") for title, content, year, month, sim in results: preview = content[:200] + "..." if len(content) > 200 else content output.append(f"**[{year}-{month:02d}]** {title or 'Untitled'} (sim: {sim:.2f})") output.append(f"> {preview}\n") if len(output) == 1: output.append("_No direct alignment guidance found. Try rephrasing or use semantic_search._") return "\n".join(output)