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**New Features:**
- Extend SolrClient.search() with facet_fields parameter
- Add facet support to MCP search tool
- Facet counts returned in response under facet_counts.facet_fields
**Implementation Details:**
- facet_fields accepts list of field names (e.g., ["category", "author"])
- Automatically sets facet=true and facet.mincount=1 in Solr query
- Response includes aggregated counts per field value
**Testing:**
- Added 2 new unit tests for faceted search
- Verified with MCP Inspector and live Solr instance
- All 7 unit tests passing
**Documentation:**
- Updated README.md with faceted search examples
- Added MCP Inspector usage guide for facets
- Updated feature list to highlight faceting capability
Example usage:
```json
{
"query": "*:*",
"rows": 10,
"facet_fields": ["category", "author"]
}
```
Response includes facet_counts with category/author distributions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <[email protected]>
Summary of ChangesHello @mjochum64, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request integrates faceted search capabilities into the system, enabling more dynamic data exploration and aggregation. By extending the Highlights
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**Version Updates:** - pyproject.toml: 1.0.0 → 1.2.0 - README.md: 1.1.0 → 1.2.0 **CHANGELOG Updates:** - Added missing [1.1.0] entry for MCP 1.21.0 modernization - Added [1.2.0] entry for Faceted Search feature **Version History:** - 1.0.0 (2025-04-26): Initial release - 1.1.0 (2025-11-08): MCP 1.21.0 modernization - 1.2.0 (2025-11-08): Faceted Search support 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
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Code Review
This pull request effectively introduces faceted search capabilities, a valuable feature for data exploration. The changes in the Solr client and MCP server are logical and well-implemented. The inclusion of new tests is also a great step. My review focuses primarily on enhancing the new tests to make them more robust and readable, ensuring they fully validate the new functionality.
| async def get(*args, **kwargs): | ||
| return mock_response | ||
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||
| mock_client = AsyncMock() | ||
| mock_client.__aenter__.return_value.get = get |
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This test checks the final output but misses a crucial validation: ensuring that the SolrClient constructs the correct HTTP request with faceting parameters. The current mock setup with a local get function makes it difficult to inspect the call arguments.
By refactoring to use a dedicated AsyncMock for the get method, you can then assert that httpx was called with the expected faceting parameters (facet=true, facet.field, etc.), making the test much more robust.
After applying the suggestion, you can add these assertions to the end of the test:
# Verify call parameters
get_mock.assert_called_once()
_, kwargs = get_mock.call_args
expected_params = {
'q': '*:*',
'wt': 'json',
'rows': 10,
'start': 0,
'facet': 'true',
'facet.field': ['category'],
'facet.mincount': 1
}
assert kwargs['params'] == expected_params get_mock = AsyncMock(return_value=mock_response)
mock_client = AsyncMock()
mock_client.__aenter__.return_value.get = get_mock| category_facets = result["facet_counts"]["facet_fields"]["category"] | ||
| assert "programming" in category_facets | ||
| assert category_facets[category_facets.index("programming") + 1] == 3 |
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The current method of asserting facet counts by finding the index of the value and checking the next element in the list works, but it can be hard to read and maintain. A more readable and robust approach is to convert Solr's flat list response (['value1', count1, 'value2', count2]) into a dictionary for assertions. This makes the test's intent much clearer.
| category_facets = result["facet_counts"]["facet_fields"]["category"] | |
| assert "programming" in category_facets | |
| assert category_facets[category_facets.index("programming") + 1] == 3 | |
| category_facets_list = result["facet_counts"]["facet_fields"]["category"] | |
| category_facets = dict(zip(category_facets_list[::2], category_facets_list[1::2])) | |
| assert category_facets.get("programming") == 3 |
| assert category_facets[category_facets.index("programming") + 1] == 3 | ||
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| # Verify ctx.info was called | ||
| assert mock_context.info.called |
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This test verifies the output of the search tool, but it doesn't confirm that the facet_fields argument is correctly passed to the underlying solr_client.search method. Adding an assertion to check the arguments of the mocked call will make the test more thorough and prevent regressions.
assert mock_context.info.called
# Verify that the solr_client.search was called with the correct facet fields
mock_context.request_context.lifespan_context.solr_client.search.assert_called_once_with(
query="*:*",
filter_query=None,
sort=None,
rows=10,
start=0,
facet_fields=["category", "author"]
)| category_facets = result["facet_counts"]["facet_fields"]["category"] | ||
| assert "programming" in category_facets | ||
| assert "technology" in category_facets | ||
| assert category_facets[category_facets.index("programming") + 1] == 3 | ||
| assert category_facets[category_facets.index("technology") + 1] == 3 No newline at end of file |
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Similar to the other new test, the facet count assertions here can be made more readable and maintainable. Converting the flat list from Solr's response into a dictionary makes the assertions clearer and less brittle.
category_facets_list = result["facet_counts"]["facet_fields"]["category"]
category_facets = dict(zip(category_facets_list[::2], category_facets_list[1::2]))
assert category_facets.get("programming") == 3
assert category_facets.get("technology") == 3
1 participant
Summary
Implements Faceted Search functionality for data exploration and aggregation, enabling users to analyze document distributions across field values.
What is Faceted Search?
Faceted search (also called faceted navigation) allows users to explore data by showing aggregated counts for different field values. This is extremely useful for:
Changes
Core Implementation
SolrClient (
src/server/solr_client.py):search()method withfacet_fields: Optional[List[str]]parameterfacet=trueandfacet.mincount=1when facet_fields providedfacet_counts.facet_fieldsMCP Server (
src/server/mcp_server.py):facet_fieldsparameter tosearchtoolTesting
Unit Tests (
tests/test_server.py):test_search_tool_with_facets: Tests MCP tool with facet parameterstest_solr_client_search_with_facets: Tests SolrClient faceting directlyIntegration Testing:
Documentation
Example Usage
Request
{ "query": "*:*", "rows": 10, "facet_fields": ["category", "author"] }Response
{ "response": { "numFound": 10, "docs": [...] }, "facet_counts": { "facet_fields": { "category": ["programming", 3, "technology", 3, "database", 1, ...], "author": ["john", 2, "smith", 2, "alice", 1, ...] } } }Test Results
Benefits for LLMs
Faceted search enables LLMs to:
Future Enhancements
Potential follow-up features:
🤖 Generated with Claude Code