"A server for finding developer tools and resources" matching MCP servers:

Enables language models to explore and analyze source code repositories through read-only tools for listing directories, reading files, searching code, finding symbols, and inspecting git history, with strict sandboxing and security controls.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

Provides MCP tools for GitHub code search, Jira sprint status, pull request risk analysis, and project metrics, enabling agentic workflows for developer productivity.

An MCP server and standalone library that gives AI assistants superpowers for developing on Xano — complete with documentation, code validation, and workflow guides.

An MCP server that wraps OpenAI's Codex CLI to automate repository cloning and code analysis tasks. It enables users to execute complex coding requests on specific Git branches and subfolders using standardized MCP tools.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

A Python framework that exposes developer tools through MCP, REST API, and CLI from a single shared registry, enabling AI assistants, HTTP clients, and terminal users to access the same tools.

An MCP server that provides tools for React and TypeScript development, including generating typed form components, explaining TypeScript compiler errors, and producing Shadcn UI installation commands.

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

Exposes Language Server Protocol (LSP) tools such as diagnostics, goto definition, find references, symbols, and rename as a stdio MCP server.

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.

Provides documentation generation and analysis tools for AI agents, including JSDoc generation, README analysis, changelog generation, API documentation, and code comment analysis.

Local MCP servers that give Claude Code access to other tools mid-session.

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.

Enables Claude Code to use VS Code's tools, including diagnostics, symbol renaming, command execution, file opening, workspace listing, semantic search/API reference, terminal context, and integrated browser page interaction. It lets Claude see and act on the user's VS Code window, including shared browser pages and Markdown Editor selections.

Enables querying a TypeScript codebase's graph for call flows, type relationships, and symbol locations without reading file bodies.

MCP server providing configuration file tools for AI agents. Parse, validate, merge, convert, and diff configuration files across all common formats.

A Model Context Protocol (MCP) server that provides TypeScript development tools for automated refactoring and code analysis.

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.

Enables AI agents to safely read, search, and inspect a local git repository, including PR-style diffs and changed-file summaries. It also supports opt-in allowlisted test runs.