Improve MCP tool usability for LLMs with auto-creation and workflow guidance

[Copilot]

Addresses the core issue where "Copilot seems to have trouble using the tools quite often" by implementing comprehensive usability improvements that reduce friction and provide clear workflow guidance.

Problem Statement

LLMs frequently struggled with the memory MCP tools due to:

• Failed operations when trying to add observations to non-existent entities
• Lack of clear workflow guidance (should view graph first, then create entities, then add observations)
• Confusing error messages without helpful examples
• Missing guidance on tool usage sequence

Key Improvements

🔄 Auto-Creation Features

• add_observation: Now automatically creates missing entities with configurable entityType (defaults to "Unknown")
• create_relations: Automatically creates missing entities referenced in relationships
• create_entities: Auto-creates empty observations arrays when missing

Example - this now works even if "John" doesn't exist:

#memory-test_add_observation workspaceId="project" entityName="John" observation="Software developer" entityType="Person"

📋 Enhanced Tool Descriptions & Workflow Guidance

• read_graph: Marked as "RECOMMENDED FIRST STEP" with clear guidance
• All tools now include workflow hints about when to use them
• Parameter descriptions include examples and expected formats
• Tools guide users through proper sequence: read → search → create → relate → observe

⚡ Improved Error Messages & Validation

• Specific error messages with corrective examples
• Better parameter validation with helpful guidance
• Auto-handling of edge cases to reduce friction

Before:

Error: Entity 'Alice' not found

After:

Error: entityName and observation are required. Please provide both parameters.
Example: {"entityName": "Alice", "observation": "Software engineer", "entityType": "Person"}

📚 Updated Documentation

• README now shows recommended workflow sequence with step-by-step examples
• Clear feature summary highlighting auto-creation capabilities
• Better examples demonstrating proper tool usage order

Impact

These changes significantly improve LLM usability by:

1. Reducing failed operations - Auto-creation handles missing dependencies
2. Providing clear guidance - Tools now guide proper workflow sequence
3. Better error recovery - Helpful messages show how to fix issues
4. Lowering complexity - Common edge cases handled automatically

The tools should now be much more intuitive and reliable for AI assistants to use correctly.

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