Allow memory URLs to embed a project hint via standard query parameters,
e.g. memory://specs/search?project=research. This lets AI assistants
resolve the correct project from the URI alone, avoiding multi-project
discovery round-trips that waste tokens and pollute context.
- Remove ? from invalid_chars in validate_memory_url_path()
- Add parse_memory_url() to extract query params from memory URLs
- Update normalize_memory_url() to preserve query params through validation
- Update memory_url_path() to strip query params when extracting paths
- Extract ?project= in build_context, read_note, and read_content tools
- Add comprehensive tests for parsing, normalization, and path extraction
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: Drew Cain <groksrc@gmail.com>
The API Pydantic schema had a MaxLen(1000) constraint on observation
content while the database uses SQLAlchemy's Text type (unlimited).
This mismatch caused validation errors when observations exceeded
1000 characters (e.g., JSON schemas with 1458+ chars).
Removed the MaxLen constraint to match the DB schema. Retained:
- BeforeValidator(str.strip) for whitespace cleaning
- MinLen(1) to ensure non-empty content
Added comprehensive tests to verify:
- Long content (10K+ chars) is accepted
- Very long content (50K+ chars) is accepted
- Empty/whitespace-only content is still rejected
- Whitespace stripping still works
Fixes#385🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
- Add comprehensive test for None permalink validation in EntityResponse
- Ensures schema properly handles markdown files without explicit permalinks
- Addresses GitHub issue #170 validation errors during edit operations
- Test validates that permalink=None doesn't cause ValidationError
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
## 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>
- incremental sync on watch
- sync non-markdown files in knowledge base
- experimental `read_resource` tool for reading non-markdown files in raw form (pdf, image)
Drew Cain