MCP servers that give coding agents real developer tooling: linters, package registries, test runners, language docs and local build commands. 13,967 servers in this category.
Transforms AI agents into senior software engineers and DevSecOps auditors through specialized skills, master rules, and automated toolchains.
Enables AI agents to run isolated parallel coding tasks using Git worktrees, PowerShell, and GitHub Actions debugging.
Provides an interactive developer portal and token savings simulator for the Existential Cloud AI engineering ecosystem.
Tests whether a crashing MCP listener prevents subsequent listeners from receiving notifications in OpenAI MCP Extensions.
Connect local workspaces to remote MCP clients through an authenticated gateway with granular permission controls.
Enables two-way communication between Claude Code and OpenAI Codex to allow agents to exchange messages and review each other's plans.
Provides long-term markdown-based memory for AI coding agents and desktop applications using a private git repository.
Exposes existing MCP servers as authenticated streamable-HTTP endpoints using isolated gateway containers.
Enables AI assistants to perform senior-level code reviews by running compilers, linters, and analyzing call sites across merge requests.
Tracks and records structural changes in public MCP server tool contracts to detect silent schema drift.
Provides a deterministic security gateway that authorizes, blocks, or escalates AI agent tool calls using a tamper-evident audit ledger.
Enables Claude to manage project intelligence including issues, ADRs, roadmaps, and design decisions directly within your repository as markdown files.
A Fastify-first framework and semantic testing toolkit for building Model Context Protocol servers over Streamable HTTP.
Lets an AI client manage local Claude Code sessions by listing, inspecting, resuming, and controlling terminal-based conversations.
Connects OpenAI Codex to interactive Claude Code sessions to use Claude as a role-controlled subagent for code review and implementation.
Provides an open-source handbook of cheat sheets, prompts, and workflows for AI coding tools and MCP integration.
Enforces coding discipline for AI agents by verifying test results, git diffs, and bug reproduction steps.
A Codex starter kit that provides a persistent work environment using reusable skills, project checkpoints, and local MCP integrations.
Provides a machine-wide coordination layer for multiple AI coding agents to prevent file conflicts, duplicate work, and contradictory decisions.
Provides machine-readable skills to optimize websites for AI agents and answer engines through structured data and discovery signals.