Refactor CLI commands to use typed ProjectClient instead of raw HTTP calls,
and add workspace metadata to cloud project listings so users can distinguish
personal vs organization projects.
Key changes:
- 🔧 CLI commands now use ProjectClient typed API clients instead of
call_get/call_post with manual URL construction
- 🏢 Cloud project listings include workspace_name, workspace_type, and
workspace_tenant_id for each cloud-sourced project
- Pass config.default_workspace when fetching cloud projects via
_fetch_cloud_projects() and CLI list_projects
- Add --workspace flag to `bm project list` for explicit workspace override
- Add "Workspace" column to CLI project list table
- Add `bm tool list-projects` and `bm tool list-workspaces` JSON commands
- Comprehensive tests for workspace passthrough, merge behavior, and CLI routing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
The /proxy/projects/projects POST endpoint returns a ProjectStatusResponse
with fields: message, status, default, old_project, new_project.
Updated CloudProjectCreateResponse schema to match this format instead of
expecting name, path, message fields.
Also updated all related tests to use the correct response format:
- tests/cli/test_cloud_utils.py (3 tests)
- tests/cli/test_bisync_commands.py (1 test)
Fixes the validation error when creating cloud projects via upload command:
"bm cloud upload --project test --create-project specs"
Signed-off-by: Pablo Hernandez <pablo@basicmachines.co>
Signed-off-by: phernandez <paul@basicmachines.co>
The lifespan-based instrumentation only runs when FastAPI app starts.
In MCP context, the app never starts but the httpx client is still used.
Solution: Instrument the client immediately after creation at module level.
This works in both contexts:
- MCP: client is instrumented when module is imported
- API: client is instrumented before lifespan runs (lifespan still safe)
This enables distributed tracing from MCP -> Cloud -> API.
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>
Paul Hernandez