mirror of
https://github.com/basicmachines-co/basic-memory
synced 2026-06-21 13:47:35 +00:00
Paul Hernandez
093dab5f03
## Summary - Add comprehensive documentation to all MCP prompt modules - Enhance search prompt with detailed contextual output formatting - Implement consistent logging and docstring patterns across prompt utilities - Fix type checking in prompt modules ## Prompts Added/Enhanced - `search.py`: New formatted output with relevance scores, excerpts, and next steps - `recent_activity.py`: Enhanced with better metadata handling and documentation - `continue_conversation.py`: Improved context management ## Resources Added/Enhanced - `ai_assistant_guide`: Resource with description to give to LLM to understand how to use the tools ## Technical improvements - Added detailed docstrings to all prompt modules explaining their purpose and usage - Enhanced the search prompt with rich contextual output that helps LLMs understand results - Created a consistent pattern for formatting output across prompts - Improved error handling in metadata extraction - Standardized import organization and naming conventions - Fixed various type checking issues across the codebase This PR is part of our ongoing effort to improve the MCP's interaction quality with LLMs, making the system more helpful and intuitive for AI assistants to navigate knowledge bases. 🤖 Generated with [Claude Code](https://claude.ai/code) --------- Co-authored-by: phernandez <phernandez@basicmachines.co>
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
from basic_memory.mcp.prompts.json_canvas_spec import json_canvas_spec
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from basic_memory.mcp.prompts.ai_assistant_guide import ai_assistant_guide
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import pytest
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@pytest.mark.asyncio
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async def test_canvas_spec_resource_exists(app):
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"""Test that the canvas spec resource exists and returns content."""
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# Call the resource function
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spec_content = json_canvas_spec()
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# Verify basic characteristics of the content
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assert spec_content is not None
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assert isinstance(spec_content, str)
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assert len(spec_content) > 0
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# Verify it contains expected sections of the Canvas spec
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assert "JSON Canvas Spec" in spec_content
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assert "nodes" in spec_content
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assert "edges" in spec_content
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@pytest.mark.asyncio
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async def test_ai_assistant_guide_exists(app):
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"""Test that the canvas spec resource exists and returns content."""
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# Call the resource function
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guide = ai_assistant_guide()
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# Verify basic characteristics of the content
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assert guide is not None
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assert isinstance(guide, str)
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assert len(guide) > 0
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# Verify it contains expected sections of the Canvas spec
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assert "# AI Assistant Guide" in guide
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