"Research papers and studies on artificial intelligence" matching MCP servers:

Enables MCP clients to drive local Codex on a target Git repository: inspect repo state, start and follow up on tasks, review events and diffs, and record audit evidence for research governance.

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.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

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.

Provides Cursor-like code intelligence using tools like ripgrep, ctags, and tree-sitter to help LLMs explore and understand entire codebases. It implements a structured, phase-gated workflow to ensure high-confidence code modifications and eliminate hallucinations.

This MCP server is designed for planning with Claude Code, Cline, or Cursor and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. It uses the Qwen 3 Coder model for high-quality code generation and can be embedded in IDEs.

Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

MCP server that lets Claude Code ask GPT Codex for adversarial planning, code review, debugging, research, and risk triage without leaving your project workflow.

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.

Plumb is an MCP server that provides coding agents with IDE-level intelligence (LSP, tree-sitter index) and safe, atomic file writes with transactional rollback and crash resilience.

Enables Claude or any MCP client to drive the GitHub Copilot cloud agent via its REST API, allowing it to research, edit, and open pull requests on repositories without manual intervention.

A TypeScript-aware MCP server that provides coding agents with repository discovery, code intelligence, and web project context for local codebases. It enables deep symbol navigation, diagnostic reporting, and structural analysis of monorepos without requiring full IDE integration.

Enables AI coding agents to query a semantic cross-repository code graph for symbols, references, callers, dependencies, and change impact across registered repositories.

Enforces a three-phase gated workflow (Research → Plan → Implement) for complex coding tasks with explicit human approval at each phase transition and automatic context management to maintain focus.

Local-first code intelligence for AI coding assistants: MCP tools, symbol graph search, impact analysis, and auto-index watch for Cursor, Claude Code, and Codex.

Enables engineers to convert antenna papers/diagrams and natural language specs into auditable, versionable HFSS Python modeling code, with optional simulation and parametric optimization.

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.

An MCP bridge for the Pi coding agent running in WSL, enabling task execution, research, review, and session management through MCP.

Local-first code intelligence and safety layer for AI coding agents. MCP server exposes dependency graph, impact analysis, and AST-compressed repo context, backed by typed local memory, patch-scope safety gates, and git-independent transaction rollback.

Enables Claude Code to research design references per page section across galleries like Awwwards, Dribbble and Mobbin, render the user's page in multiple style directions with live 3D components, and collect votes on a local board. It then produces a PDF report and iteratively builds and refines the actual page, dashboard prototype or mobile screens from that feedback.

A local project intelligence, batch coding, and self-learning engine for Codex that enables task-level batch coding, reduces token usage, and automatically learns skills.

Privacy-first, read-only repo intelligence for AI coding agents: scans local codebases and recommends MCP servers, repos, and research without sending data to the cloud.

Supercharges AI coding agents with semantic code intelligence, providing pre-built knowledge graphs for surgical context, faster answers, and fewer tool calls.

Provides Roslyn-based code intelligence for AI coding agents via MCP, enabling efficient navigation and surgical edits of C# solutions.

A project intelligence platform for AI coding agents that scans, audits, generates, and monitors context files (CLAUDE.md, AGENTS.md, etc.) with drift detection and repair capabilities.

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.

Enables AI assistants to research topics, generate product requirements and user stories, map codebases, curate context, scaffold full-stack starter projects, and run multi-step development workflows.

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