"Terraform - Infrastructure as Code Tool or Related Search" matching MCP servers:

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

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 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.

An advisory and generative copilot for Terraform that reviews existing scripts and generates compliant infrastructure using organization-specific rules.

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.

An MCP server that wraps terraform-ls to provide Terraform language intelligence as MCP tools, enabling AI assistants to understand and navigate Terraform codebases.

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

Generates structured prompts for Claude Code to create comprehensive Terraform test cases including unit, integration, and mock tests.

Enables fast, low-token code search for AI coding agents via a local BM25 engine built on SQLite FTS5, with support for camelCase, snake_case, and Japanese text. Provides a stateless MCP stdio server and a Hermes adapter for multi-agent environments.

Provides AI coding agents with structured access to indexed codebases via semantic search, symbol analysis, and file reading tools.

A paid remote MCP server for routing code search queries, providing structured receipts, audit logs, and reviewer-ready evidence.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

CodeGuard MCP is a real-time AI code security scanning tool used to detect vulnerabilities, keys, and compliance issues in AI-generated code, and is suitable for code security reviews in development environments

Enables local semantic code search across repositories using natural language, with AST-aware chunking and hybrid vector/FTS5 retrieval.

A semantic code search MCP server that enables natural language queries against your codebase, supporting features like related file discovery and context expansion, all running locally.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

Provides a code search service via MCP, allowing AI clients to perform fast, indexed searches across large codebases using Zoekt, with support for filters like repository, language, and path.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.