"Understanding Frontend and Backend Development" matching MCP servers:

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

Analyzes frontend project code (React, Vue, Angular) and converts it into AI-understandable flow diagrams and object structures. Provides tools for code analysis, variable/function/component inspection, and project structure insights.

A comprehensive framework for building reliable Python MCP servers that enforces incremental development practices through Cursor Rules, featuring built-in security measures and a smart rule system.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Enables AI agents to track and manage product development projects through structured 7-phase lifecycles with sprint tracking, role-based collaboration, and multi-project support. Provides phase management, progress tracking, and team coordination tools for complete product development workflows.

Validates startup ideas with a deterministic scorecard, evidence brief, and verdict before code is written, integrating with MCP-aware build agents to avoid building dead-on-arrival products.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

Enables AI agents to analyze videos and images using Qwen3-VL deployed on Modal, supporting hours-long videos with timestamp grounding, text extraction, video summarization, and Q\&A with 256K context window.

Connects a real ChatGPT Dot to QQ through QQBridge and SnowLuma, letting it receive wake events, read text and image messages, and send replies, images, and forwarded media within whitelisted conversations. Supports event subscriptions, per-conversation claiming, and explicit turn completion across private and group chats.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

An MCP-based trivia game server where AI creates rounds with two true statements and one false 'twist' about various topics, letting players guess which statement is false.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Enables AI agents to claim, heartbeat, submit, split, park, and resume atomic work-queue tasks, with fencing tokens and receipts, while storing and versioning records and content-addressed files through MCP.

Enables real-time multi-agent AI collaboration in shared rooms over MCP, allowing coding agents to exchange structured messages, track evidence-gated tasks, and export reports for distributed development, code review, and frontend-backend integration.

Enables autonomous frontend and backend AI agents to asynchronously coordinate development by registering and discovering OpenAPI-compatible route contracts, tracking integration issues, and managing route groups for efficient LLM context.

Enables enterprise MCP clients and partner agents to execute tenant-isolated underwriting, claims, and compliance workflows with durable step checkpointing, evidence-grounded tool calls, and deterministic PDF citation receipts. It supports A2A task dispatch and MCP JSON-RPC operations while blocking cross-tenant access and ungrounded deliverables.

Enables Grok Bots to install, verify, build, seal, unseal, list, and update hash-pinned skill packs from static URLs, expanding skill capacity beyond template limits without executing pack content.

Enables MCP agents to play chess locally, supporting join, state, legal moves, and move execution with per-session color ownership.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

MCP server that exposes Gmail and SAP tools, enabling natural-language email management and SAP data browsing through a Mistral-powered AI assistant.

Provides 8 specialized AI agents (Frontend, Backend, Database, API, DevOps, Testing, Security, Performance) for full-stack web development, powered by high-speed Cerebras inference and integrated with OpenCode CLI.

Enables AI agents to manage personal expenses via natural language through add, query, summarize, and delete expense tools.

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

A generic OpenAI-compatible subagent MCP server that lets Claude Code delegate coding grunt-work to a local model via a tool-calling agent loop, with configurable concurrency and backend.

Provides plan state management and phase gate enforcement for AI development loops. It tracks task progress and coordinates the lifecycle of agents by requiring specific evidence before advancing through development phases.

Enables AI agents to request human decisions and send notifications through chat platforms like Slack and WhatsApp, with ephemeral in-memory pending requests and no central backend.