"Web search and data extraction capabilities for AI assistants" matching MCP servers:

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

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.

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 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.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

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

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.

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.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

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.

A Model Context Protocol server that extracts embedded data (such as i18n translations or key/value configurations) from TypeScript/JavaScript source code into structured JSON configuration files.

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

An MCP server providing tools for web research, code review, and concept explanation, callable by any MCP-compatible client.

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.

AI development assistant with 36 specialized tools for memory management, code analysis, project planning, and problem-solving through natural language interactions in Korean and English.

Spec-driven development tool for AI coding assistants that generates specs, validates code compliance, and provides actionable feedback.

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.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.