smart-coding-mcp
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- AlicenseAqualityAmaintenanceEnables AI coding assistants to perform local semantic code search using AST parsing and embeddings, finding code by meaning with exact file and line locations, plus AST-based usage lookup.4MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI coding agents to perform semantic code search locally, finding code by meaning rather than exact keywords.3MIT
- AlicenseAqualityFmaintenanceProvides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.634 npm200MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to perform intelligent semantic code search across a codebase using local AI embeddings, with tools for reindexing, cache management, workspace switching, package version lookup, and status checks.34 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding assistants to search codebases by meaning using local AI embeddings, with tools for indexing, cache management, workspace switching, status checks, and package version lookup. It supports incremental and progressive indexing with configurable embedding dimensions to find relevant code even when terminology differs.34 npmMIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to index and search codebases using semantic search powered by multiple embedding providers (OpenAI, VoyageAI, Gemini, Ollama) and vector database storage.MIT
TDQS
Scored across 6 tools
Each tool has a clearly described purpose and most are easy to distinguish. The main ambiguity is between b_index_codebase and c_clear_cache, since both can result in a rebuilt embedding index, but their described triggers differ enough to avoid serious confusion.
The names use consistent snake_case and mostly follow a verb_noun pattern, but the arbitrary a_, b_, c_ prefixes are not semantically meaningful and a_semantic_search breaks the verb_noun pattern by leading with an adjective. The overall style is readable but mixes conventions.
Six tools is a reasonable size for a focused semantic code search server. However, d_check_last_version is unrelated to the core semantic-search/indexing workflow, so the tool set is slightly less focused than it could be.
The core workflow is well covered: workspace setup, indexing, cache invalidation, semantic search, and server status. Minor gaps exist around configuration control or more granular index inspection, but agents can complete the primary tasks without dead ends.