# Milestone 1: Add themes parameter to enterprise_search_repository

**JJ Change ID**: zysnlpsw
**Parent Message**: FIX: Add themes parameter to enterprise search and handle JSON string input
**Implementation Date**: 2025-09-01 07:58
**Status**: ✅ Success

## What Was Attempted
Added themes parameter to the enterprise_search_repository function in the MCP server to fix the inconsistency where the tool description mentions using themes but the actual function didn't accept it. Implemented JSON string parsing to maintain API consistency with other string parameters.

## Files Modified
- `src/mcp_server_qdrant/mcp_server.py` - Added themes and other filter parameters to enterprise search functions

## Discoveries & Deviations
### Documentation Issues
- Tool description mentioned "Use themes to find semantic patterns" but parameter was missing
- Comment in code: "filter wrapping disabled for now" explained why parameters were missing

### Scope Adjustments
- Added: All filter parameters mentioned in tool description (not just themes)
- Added: JSON string parsing for themes to maintain API consistency
- Modified: All three enterprise functions to use consistent JSON string approach

## Technical Decisions
### Why This Approach
The MCP framework expects consistent parameter types across all fields. When some fields are strings and others are arrays, it creates confusion for LLM integrations. By accepting themes as a JSON string:
- All parameters are consistently strings
- LLMs can use a single format for all fields
- API follows principle of least surprise

### What Didn't Work
- Initial consideration of using list[str] type directly would have been inconsistent with other parameters
- Would have required LLMs to handle mixed types in a single API call

## Testing Results
```bash
# Need to run tests to validate
uv run pytest tests/test_enterprise_tools.py -v
```
- Test output: [pending]
- Issues found: [pending]

## Implementation Details
### Key Changes:
1. Added parameters to `enterprise_search_repository`:
   - `themes: str | None = None` (JSON string)
   - `programming_language: str | None = None`
   - `complexity_score: int | None = None`
   - `file_type: str | None = None`
   - `directory: str | None = None`
   - `has_code_patterns: bool | None = None`

2. Implemented JSON parsing with error handling:
   ```python
   if themes:
       try:
           import json
           themes_list = json.loads(themes)
           if not isinstance(themes_list, list):
               raise ValueError("themes must be a JSON array")
       except (json.JSONDecodeError, ValueError) as e:
           await ctx.error(f"Invalid themes format: {e}")
           return [f"Error: Invalid themes format. Expected JSON array string, got: {themes}"]
   ```

3. Built filters from parameters before calling underlying search function

4. Updated `enterprise_analyze_patterns` and `enterprise_find_implementations` to use same JSON string approach

## Next Steps
- [ ] Write tests for JSON string parsing
- [ ] Test with actual LLM to ensure it can use the new format
- [ ] Update tool descriptions if needed
- [ ] Consider creating a helper function to reduce code duplication for JSON parsing

## Rollback Instructions
If this implementation causes issues:
1. Revert changes to `src/mcp_server_qdrant/mcp_server.py`
2. The original functions accepted no filter parameters except query_filter
3. Use `jj restore --from xotlpslz src/mcp_server_qdrant/mcp_server.py`
