"Information about Azure (Microsoft Cloud Computing Service or General Search)" matching MCP servers:

Enables agents to refactor source code and extract structured code summaries, plus generate interview questions and query 5G NWDAF analytics through MCP tools.

Enables business services to push error logs and generate diagnostic reports using MCP tools for log searching, code querying, and report generation.

MCP server for the Codex Security Cloud service, enabling agents to list, inspect, close, reopen findings, request PRs, and apply generated patches.

Enables AI assistants to read Bitbucket Cloud pull requests and diffs through natural conversation.

Enables SDK and MCP clients to perform repository code search and retrieval, inspect current source and related evidence, and optionally use semantic search, reranking, and web sources while keeping the calling agent in control.

Enables AI agents to analyze and search IFS Cloud codebases using hybrid semantic-lexical search, PageRank importance ranking, and dependency graph analysis. It supports comprehensive exploration of PL/SQL, entities, and projection files to facilitate intelligent architectural understanding.

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.

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

Audits dbt Core projects for migration blockers and generates actionable guidance for migrating to dbt Cloud, including auto-fixing deprecated syntax.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

Clones a Git repo into a temp directory so MCP clients can search and read files from another repo for context.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Enables AI coding agents to perform fast regex/text and structural AST searches across codebases, find symbols, list files, and safely preview or apply refactors. It unifies ripgrep and ast-grep with pagination, gitignore-aware defaults, and fallback engines for zero-setup code search.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.

Semantic code search for Claude Code, enabling natural language codebase indexing and search using AI embeddings.

Enables intelligent code analysis and search across repositories using the CodeRank algorithm (inspired by PageRank) to identify critical modules, trace dependencies, find code hotspots, and perform context-aware keyword searches with importance-ranked results.

Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.

Enables AI coding agents to perform semantic code search locally, finding code by meaning rather than exact keywords.

Enables token-bounded, read-only hybrid code search across local repositories by fusing literal matches with AST symbol relations to return high-signal snippets, line ranges, and call metadata.

Enables cross-repository code search over a local mirror of a self-hosted Gitea or Forgejo instance, covering file contents, file paths, and commit messages across all repositories and branches. It also lets users read a single file from the mirror and list mirrored repositories, all without network access during search or read.

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 read-only code search in a specified local directory and public web search through the user's own Devin session, with login handled only in the terminal.

Enables MCP clients to list workspaces and inspect concise, workspace-scoped code review findings and hosted run summaries. It can also prepare a hosted review for an open pull request and, after explicit user confirmation, start or rerun it, without exposing raw diffs, reports, or credentials.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

Enables natural language search over indexed codebases with source citations and incremental indexing.

Enables semantic code search over local repositories, providing tools like semantic_search and list_indexed_repos to Claude Code, so users can find relevant code sections via natural language instead of grep.

A CLI-first semantic code search tool with MCP integration for AI assistants, enabling semantic search, AST-aware parsing, and code analysis across 13 languages.

Enables an MCP client to sign in and inspect workspace-scoped review findings and hosted runs, and to prepare, then start or rerun after explicit confirmation, an existing hosted code review for a pull request. It exposes only concise summaries, never raw diffs, full reports, artifacts, credentials, or prompt text.