"Tool or method for converting Vue code to React code" matching MCP servers:

Enables agents to perform local hybrid code search and code intelligence across a workspace, including semantic and full-text search, symbol lookup, file outlines, and caller analysis.

Provides semantic code search and code insights via a knowledge graph, enabling AI to understand, navigate, and modify complex projects with deep dependency and architecture analysis.

Enables coding agents to find code by what it does rather than what it's called, searching local projects and remote GitHub or Bitbucket repositories all at once. Indexing and semantic search run fully offline with a locally loaded model, so code never leaves the machine.

Enables Claude to list directories, read text files, and search code within a designated read-only project folder, while skipping common dependency folders and blocking sensitive files.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Enables Claude to intelligently analyze and query codebases using knowledge graphs, supporting natural language code search, relationship discovery, and incremental updates.

Enables semantic search over codebases using natural language queries, returning relevant code snippets with source locations. Integrates with Claude Code for automatic codebase exploration.

MCP server that indexes a knowledge base of past bugs and fixes, making them searchable via BM25 from Claude Code or any MCP client.

Connects to vibe-code-best-practices.vercel.app to index 817 tools across 185 sections and 10 domains, routes the right tool/Skill for your project need, and can recommend a full stack.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.

MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.

Enables semantic code search across indexed codebases using natural language queries, with support for CLI and MCP interfaces.

Enables agents to look up Indiana state statutes by citation, run full-text topic searches, and retrieve historical per-year annual editions of the code, with amendment-history notes split out from section text. Covers all 37 Indiana Code titles from the official state legislature source without requiring an API key.

Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.

Enables AI agents to retrieve the full current text of any state statute section by citation, along with its amendment history, repealed/transferred status, neighboring sections, and any pending future-effective version. It also performs live full-text keyword search across actual section body text to find statutes by topic.

Enables lookup of Hawaii Revised Statutes sections by citation, returning full text, official catchlines, chapter context, and parsed amendment/enactment history, plus archived versions of the same section for any year from 1999 to present. Also supports topic and keyword search over the Legislature's official section and chapter catchlines. Requires no API key.

Local-first code indexer that provides deep code understanding for Claude and other LLMs with symbol/text search across 48+ languages, semantic search capabilities, and real-time index updates through the Model Context Protocol.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

Enables AI assistants to perform hybrid semantic and lexical code search across multiple repositories, retrieve symbol definitions and call hierarchies, and manage repository relations through MCP tools.