Adds type annotation parsing to create references edges for parameter types, return types, and variable type annotations in TypeScript and other typed languages. Expands symbol extraction from queries to capture lowercase identifiers and filters out more common English words. Removes obsolete search utility tests.
Three issues discovered testing CodeGraph against a Shopify Liquid theme:
1. Callers/callees only traversed 'calls' edges, missing 'references' and
'imports' edges that Liquid extraction creates for {% render %} and
{% section %} tags. Expanded edge filter in getCallers/getCallees.
2. Context builder only ran text search as a fallback when semantic search
returned nothing. For template-heavy codebases, semantic search returns
irrelevant results (e.g., "Toast" for a header navigation query) while
text/path-based matching would find the right files. Now always runs
text search alongside semantic search with multi-term boosting.
3. MCP findAllSymbols only matched nodes by exact name, missing file nodes
whose basename (without extension) matched the symbol. This caused
callers to find zero results even with correct edges, since references
edges point to file nodes (e.g., "product-card.liquid") not component
nodes (e.g., "product-card").
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eliminate cross-language false positives in name resolution and deprioritize
test files in context building. Benchmarked on a Python+Rust codebase where
37% of edges were false positives from Python built-in methods resolving to
Rust functions (e.g., list.extend → Rust extend).
Resolution fixes (index-time):
- Filter Python built-in type method calls (list.extend, dict.update, etc.)
- Filter bare Python built-in method names (append, extend, pop, keys, etc.)
- Add language boundary checks to matchMethodCall strategies 1, 2, and 3
- Penalize cross-language matches: -80 points in findBestMatch (was 0)
- Reduce confidence for single cross-language exact matches (0.5 vs 0.9)
- Prefer same-language candidates in matchFuzzy
Context relevance fixes (query-time):
- Add isTestFile() utility detecting test files across Python/JS/TS/Go/Rust/Java
- Deprioritize test files in scorePathRelevance (-15 penalty)
- Reduce test file scores to 30% in context builder result merging
- Both skip deprioritization when query mentions "test" or "spec"
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Implements security improvements inspired by PR #16 (credit: MO2k4):
- Add validatePathWithinRoot() to prevent path traversal attacks in
extraction and context building
- Clamp MCP tool inputs (limit, depth, maxDepth) to sane ranges
- Use atomic writes (temp file + rename) for config saves
- Add symlink cycle detection in directory scanning to prevent infinite loops
- Replace all JSON.parse calls in db/queries.ts with safeJsonParse fallbacks
to handle corrupted database metadata gracefully
- Add cross-process FileLock for DB write operations (indexAll, indexFiles,
sync) to prevent concurrent writes from CLI, MCP server, and git hooks
- Remove unused path import from context/index.ts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds support for Dart and Liquid languages with tree-sitter parsing.
Improves accuracy of code symbol extraction for existing languages.
Indexes project files to enhance code navigation features.
Migrates build system to facilitate code contributions.
Removes git hook functionality.
Integrates Sentry for error tracking and reporting.
Enhances project initialization and configuration loading.
- Add evaluation test suite with TypeScript and Python fixtures
- Fix MCP server to defer CodeGraph init until rootUri received
- Fix call edge extraction by calling resolveReferences() after indexAll/sync
- Fix glob matching for root-level files (e.g., **/*.py now matches auth.py)
- Fix duplicate node extraction for methods inside classes
- Update context tests to use buildContext for semantic search + graph traversal
- Export unused formatter functions to fix build
Evaluation results:
- TypeScript: 96% precision, 79% recall, 85% F1
- Python: 99% precision, 80% recall, 85% F1
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Colby McHenry
