"Debugging software using GDB (GNU Debugger)" matching MCP servers:

Enables AI assistants to monitor and analyze blockchain activity through Tenderly's infrastructure and direct EVM RPC calls. Provides comprehensive alert management, transaction simulation, and multi-chain querying capabilities for blockchain debugging and monitoring.

Enables users to generate complete, production-ready software projects from simple ideas by coordinating 8 specialized AI agents through the Model Context Protocol.

A Model Context Protocol server for the resQ emergency response system that integrates with digital twin simulations and coordination engines. It allows users to trigger simulations, generate deployment strategies, and monitor real-time drone status for incident response analysis.

Declarative MCP runtime over IDF artifacts. Tool descriptions carry invariants, lifecycle, irreversibility, and role scopes.

A git-backed MCP server that centrally manages AI agent skills, tools, and knowledge with role-based access, change requests, and a remote OAuth 2.1 endpoint.

The coordination layer for AI agent networks, providing persistent memory, task management, inter-agent messaging, and human oversight through native MCP tools.

Enables AI-powered research by breaking a topic into subtopics, gathering information via agents, and compiling a report. Integrates with LangGraph and RAG for orchestration and contextual retrieval.

Agent failure memory network. Search 235+ verified debugging lessons from real engineering sessions. Includes guided prompts for failure triage and release auditing.

Governance and session management layer for AI-assisted software development; enables agent binding, session tracking, and structured code review within the HestAI ecosystem.

Coordinates Codex and Antigravity CLI for a structured software development workflow with workspace authorization, Git-based verification, and acceptance criteria.

Enables Codex and Claude Code to delegate implementation, research, debugging, and long-log work to DeepSeek Harness, then observe, continue, or cancel those sessions without leaving the primary workflow.

Enables non-coders to build software by describing ideas in natural language, orchestrating AI agents and CLI workers via chat without needing a terminal.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Enables AI agents to manage infrastructure and deploy Docker services through EasyPanel using natural language commands. It supports full lifecycle management of services, deployments, and networks while facilitating automated scaling and assisted debugging.

A universal Model Context Protocol implementation that serves as a semantic layer between LLMs and 3D creative software, providing a standardized interface for interacting with various Digital Content Creation tools through a unified API.

Facilitates autonomous AI agent collaboration on software projects through file-based task management and inter-agent communication via the Model Context Protocol.

Coordinates specialized agents (Architecture, Quality, Cloud, Prompt) to plan, build, test, and deploy applications with self-healing capabilities, authentication, and analytics for autonomous software engineering workflows.

Multi-Agent Development Agentic Network that enables AI-driven software development through specialized agents, interactive wizards, and structured debate, with MCP integration for IDE tools like workflow execution, impact analysis, and ecosystem search.

An autonomous multi-agent orchestration layer that generates a bespoke software engineering organization for a given objective, runs parallel OpenCode workers in isolated git worktrees, and integrates reviewed code through a replan loop.

Agentic cloud platform with 45+ MCP tools. Deploy any containerized stack, debug live pods (shell, file editing, DB queries), manage custom domains & TLS, push to built-in container registry, scale pods, and manage GPU workloads. The infrastructure layer where AI agents ship software to production.