"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.

Enables searching and reading a private local library of licensed components and template source files through read-only MCP tools for browsing categories, templates, components, and dependencies.

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.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

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.

Security scanner for third-party AI agent-skill files: SKILL.md manifests, hooks, and bundled scripts, exposed via an MCP tool.

Provides TypeScript and JavaScript code refactoring operations using ts-morph, allowing AST-based symbol renaming, file/folder renaming, reference searching, and path alias removal when integrated with editor extensions like Cursor.

SkillTotal is a local stdio MCP server that lets an agent check a component before installing it. It statically analyzes MCP servers, agent skills, npm/PyPI packages and repos for supply-chain risks, dangerous capabilities (shell, filesystem, network, credentials), prompt-injection surfaces like tool poisoning, and exfiltration paths. Each finding cites file, line and snippet.

Provides static analysis of ROS 2 workspaces, enabling inspection of packages, dependencies, interfaces, launch files, and robot descriptions without running ROS 2.

Enables Claude Code to perform fuzzy and semantic codebase search via a query_codebase tool.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Contract-first programming language with a local MCP server for checking types, effects, capabilities, and contracts in agent-written modules. Its review tool compares before and after source sets and returns receipts for new authority, weaker promise tiers, and new Guarded runtime obligations.

Copy/paste (duplicate code) detection for codebases. The jscpd detector's built-in MCP server lets AI agents check snippets and files for duplication against a scanned project over stdio (jscpd --mcp).

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.

Enables Claude Code to leverage local Ollama models for tasks like summarizing large files, reviewing diffs, semantic code search, and generating boilerplate or tests, while also supporting a fully offline mode.

Ariadne's thread — a way out of the microservice maze. Local cross-service semantic chain hinter for microservices (GraphQL/HTTP/Kafka/frontend)

A deterministic structural code map server for AI agents, giving them the shape of a codebase (imports, exports, classes, functions, signatures, comments, TODO-markers) without reading whole files into context. Powered by tree-sitter WASM grammars, it runs anywhere Node 18+ works.

MCP server that indexes your codebase's public API at startup and serves it via compact tool responses, saving tokens vs reading source files.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

An MCP server that helps AI agents comprehend a codebase by providing tools for navigating, searching, and understanding code structure and history.

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.

A self-hosted MCP server that fetches live documentation and performs code audits using real-time authoritative references to prevent AI hallucinations. It provides tools for searching documentation across 330+ libraries and scanning local source code for security, performance, and accessibility issues.

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.

MCP server for searching code via SourceGraph's GraphQL API, enabling fast symbol and text search across local or cloud SourceGraph instances.

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

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.

Provides role-aware repository maps for LLM agents by analyzing codebases and ranking files within a token budget.