"Finding people on LinkedIn using their names" matching MCP servers:

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

YOUR AGENTS COLLIDE. File a FlightPlan. Run multiple coding agents without them stepping on each other. FlightPlan gives each agent the same preflight picture, lets them coordinate while plans are still cheap to change, and leaves behind what changed and why for whichever agent comes next. Across sessions. Across people. Across providers. Across time. One shared picture.

AST-targeted code editing MCP server with 28 surgical tools for structural edits across 11 languages. Built on tree-sitter, replaces brittle search/replace with byte-correct edits keyed by symbol names.

Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.

An MCP server that enables Claude to orchestrate multiple autonomous Claude Code agents working in parallel across different projects, with tools to dispatch, monitor, and manage their progress.

A safe, local MCP server that lets Claude drive a controlled software-development loop (inspect, read, plan, patch, apply, check, analyze, fix, summarize) on a project, using deterministic tools and real diffs/test runs.

An MCP server that connects Gemini 2.5 Pro to Claude Code, enabling users to generate detailed implementation plans based on their codebase and receive feedback on code changes.

A zero-cost MCP server that forces AI to self-correct code using prompt injection and context isolation.

An MCP server that lets coding agents ask their human via interactive UIs like sliders, forms, diffs, and live previews.

Enables finding existing shadcn-format UI components by plain-language description instead of exact names, returning ranked matches with install commands and per-dimension match reasons.

Enables multiple AI coding agents to collaborate on a project by coordinating tasks, file leases, and messages through a shared hub, preventing conflicts and enabling parallel development.

Enables coding agents to open files and git diffs directly in VS Code, allowing users to review code changes without leaving their conversation.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Enables Codex to delegate tasks to interactive Claude Code sessions as role-controlled subagents for exploration, code review, and implementation, using the existing Claude Code installation on macOS.

Enables coding agents to create, deploy, invoke, and monitor serverless functions using 12 MCP tools, with support for multiple runtimes and remote MCP access.

A zero-dependency MCP server that allows multiple coding agents to coordinate work on the same repository using file locks, task claims, and status messages.

Classifies software implementation tasks using local Ollama models and recommends coding-agent models based on a deterministic policy.

Exposes a remote coding agent as MCP tools so an MCP client can delegate tasks, manage their lifecycle, and inspect the agent card, with permission decisions enforced inside the bridge.