vs-token-safer
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- AlicenseAqualityBmaintenanceLocal code search for AI agents over a tree-sitter index of your repo: exact symbol lookup, scope-aware usages, call graph and impact analysis, AST pattern search, indexed grep and semantic search. 28 MCP tools, 19 languages, runs fully offline.2812MIT
- AlicenseNot gradedqualityCmaintenanceEnables coding agents to search a locally indexed repository and get back a token-budgeted deck of ranked code and doc snippets instead of a full-tree grep dump, with lexical, semantic, and prose-answering modes plus index status reporting. Runs fully offline for lexical queries and optionally uses an OpenAI-compatible provider for semantic or ask modes.MIT
- AlicenseNot gradedqualityNot gradedmaintenanceEnables AI agents to explore a repository by querying a pre-built structural knowledge graph of symbols and their relationships — definitions, callers, callees, blast radius, and file-level impact — instead of grepping and reading files. This cuts the tokens spent on code exploration by roughly 96–99% while running entirely on local SQLite with tree-sitter parsing.1MIT
- AlicenseNot gradedqualityBmaintenanceCode-search engine that parses TypeScript and JavaScript with a real AST, stores the symbol graph in SQLite, and serves AI assistants exact matching declarations (functions, classes, types) via BM25 fused with declaration-exact ranking — saving tokens by returning only the relevant code instead of whole files.19 npm3MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to find code by describing its behaviour rather than guessing file names, returning ranked file:line snippets that cost far fewer tokens than grep-then-read. Also surfaces neighbouring implementations, callers and tests around a location you already know, indexing local paths or git URLs locally on CPU.510 npm1MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents to search a project's code and receive the most relevant source excerpts with exact line numbers, so agents spend fewer tokens, tool calls and rounds locating the code behind a question. Search runs locally, with only the question and selected snippets sent to a relevance ranking service.14MIT
TDQS
Scored across 16 tools
Each tool targets a distinct operation: search, reference, definition, diagnostics, hover, outline, and symbol edits are clearly separated. Even the three search tools (search_symbol, search_text, concept_search) are explicitly differentiated by whether the input is a symbol name, raw text, or a fuzzy concept.
Most tools follow a readable verb_noun snake_case pattern (find_references, replace_symbol_body, insert_symbol), but there are notable deviations: concept_search reverses the search_* convention, vts_admin is a cryptic meta-tool name, and diagnostics/hover/rename are bare nouns or verbs. The set is still readable, but the naming pattern is not consistently applied.
At 16 tools, the server is slightly above the usual sweet spot, but each tool maps to a distinct code-intelligence or symbol-editing capability. There is minimal redundancy, and the breadth is justified by the wide range of navigation, search, editing, and safety operations covered.
The set provides full symbol lifecycle coverage: read_symbol for reading, insert_symbol for creating, replace_symbol_body and rename for updating, and safe_delete for deleting. It also covers navigation, search, diagnostics, diff impact analysis, and file discovery, so agents have no obvious dead ends when working with code symbols.