"Information on Air Quality Index (AQI)" matching MCP servers:

An MCP server that allows Pebble Index to capture notes, enqueue tasks, create follow-ups, and launch visible Codex threads on a Windows PC. It uses a remote server with token-based access and a local runner that executes only explicit execution requests.

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.

An MCP server that allows coding agents to search the Swift Package Index and retrieve GitHub READMEs without requiring an API key. It features comprehensive search filters for stars, platforms, and licenses, providing token-efficient responses optimized for LLM comprehension.

Turn rough requests into rigorously structured prompts for any coding agent. Quality-scored to ≥90/100 across 12 dimensions, calibrated on 1,000+ real coding cases.

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 coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

A Model Context Protocol server that routes tasks to Z.ai's GLM-4.7 model, enabling Claude Code to delegate work to a more cost-efficient AI model without sacrificing quality.

Slimdex is a local MCP server that helps coding agents retrieve code narrowly—outlines, line ranges, symbol bodies, references, and intent-based searches—instead of reading whole files into context, featuring a persistent index and tools for exploration, editing, and session memory.

Enables planning with Claude Code and high-speed code generation using Cerebras, avoiding API limits while producing quality code with visual diffs.

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.

Provides read-only MCP tools for discovering UI libraries, recommending minimal compatible UI stacks, and auditing plans to enforce deterministic UI policy and quality gates for coding agents.

An MCP server that enforces hard quality gates on AI coding agents, including forced web search, planning, and empirical test execution, to prevent common failure modes and ensure production-grade code.

Bridges Codex to external CLI coding agents for read-only code analysis and deterministic quality fixes.

Enables coding agents to incrementally index project text and code, persist decisions and constraints with clear sources, and assemble focused project context across sessions via MCP.

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 acceptance gates for AI coding-agent runs by recording evidence, running deterministic validation, applying a quality gate, and rendering auditable outcomes.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

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 intelligent task management, multi-agent workflows, and cross-IDE project management through the BMAD methodology, with features like time tracking, quality gates, and project templates.

Provides a specification-driven workflow layer for AI-assisted coding, enabling agents to follow an explicit 11-phase feature workflow with checkpoints, artifacts, and quality gates.

Routes coding tasks to local and remote LLMs with intelligent cost-quality optimization, supports benchmarking and code search.

Enables users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Autonomous spec-to-product coding-agent CLI. Its MCP server exposes 34 tools over stdio: project state and task-queue ops, memory retrieve/store, code search, quality and verification reports, repo hotspots/co-changes, and structured findings/learnings.

Provides AI-powered development assistance with continuous chain-of-thought reasoning (CoConuT), automated task planning and execution, quality validation, and persistent memory of project context and decisions.

Enables AI harnesses to run iterative loops on code repositories with built-in quality evaluations, security gates, and approval workflows for tasks like fixing tests or implementing features.

A multi-AI MCP server that orchestrates 37 specialist agents across 9 AI tools to measure and improve code quality with session impact reports and quality gates.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.