"A tool for searching local files" matching MCP servers:

A Model Context Protocol (MCP) server that enables AI applications to access and analyze local code repositories without manual uploads, providing file listing, content reading, code searching, and project structure analysis capabilities.

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.

Safe local MCP server for Windows to list, read, search, patch, backup, and verify code files in allowed folders, with Git integration and dry-run diffs.

A local MCP server that turns AI clients into power users of local git repositories, enabling clone, browse, search, and inspect code without burning API tokens.

An MCP server that indexes local Python projects into a SQLite database to enable efficient symbol searching and dependency tracking. It allows users to find function or class definitions, trace module imports, and read file contents through natural language interfaces.

A token-efficient, schema-aware MCP server that enables AI assistants to safely read, modify, query, and validate JSON, YAML, and TOML files with automatic schema detection and format conversion capabilities.

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.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.

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.

MCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.

Enables binary comparison capabilities by leveraging IDA Pro and BinDiff to analyze similarities and differences between files. Users can perform automated function analysis to identify changed functions and compare original binaries against patched versions.

A code review tool server based on Model Context Protocol (MCP) providing multi-dimensional code review and scoring.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

locidx

A Model Context Protocol server that provides tools to find regex pattern positions in files and list allowed directories, enabling text analysis with LSP-like functionality.

Enables AI assistants to access and interact with Cursor/VS Code Local History data for file recovery and enhanced context awareness. Provides tools to browse file history, search across snapshots, and restore previous versions of files.

Provides AI assistants with complete visibility into AL dependencies and symbols by exposing compiled .app files through the Model Context Protocol, enabling smart object discovery, deep code analysis, and reference tracking for Business Central development.

Generates DeepWiki-style documentation for private code repositories with RAG-based Q\&A capabilities, semantic code search, and multi-language AST parsing. Supports local LLMs (Ollama) or cloud providers for privacy-focused codebase analysis.

MCP server that indexes TypeScript/React project symbols in memory, enabling agents to find definitions, references, and dependencies without reading the whole repo. Also includes a local visual dashboard with no outbound network calls.

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.