"A server that supports using a Python debugger for tasks like finding memory leaks" matching MCP servers:

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

Enables AI agents to perform step-through debugging of Python, JavaScript/Node.js, and Rust programs using the Debug Adapter Protocol, with support for breakpoints, variable inspection, and stack traces.

Enables debugging of Python, JavaScript/TypeScript, Go, and Rust code using real debuggers with breakpoints, stepping, variable inspection, and stack trace navigation through the Debug Adapter Protocol.

Enables AI agents like Claude to control VS Code's debugger, supporting any language with a Debug Adapter Protocol implementation.

MCP server for remote debugging of Apps in Toss mini-apps, providing CDP relay and test runner capabilities.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Provides debugging capabilities for Python, C, C++, Rust, Swift, and more through the Debug Adapter Protocol, enabling breakpoints, step-by-step evaluation, and stack traces.

Enables coding agents to identify likely runtime failures in JavaScript and TypeScript by ranking risky files, tracing dependencies, analyzing logs, and obtaining independent model reviews with line-level reasons.

A comprehensive and efficient Model Context Protocol server for task management that works with Claude, Cursor, and other MCP clients, providing powerful search, filtering, and organization capabilities across multiple file formats.

An MCP (Model Context Protocol) server that enables interactive debugging with code instrumentation for AI coding assistants. Inspired by Cursor's debug mode.

Enables comprehensive JavaScript code analysis and debugging using ESLint and custom pattern matching. Identifies syntax errors, potential bugs, code quality issues, and provides actionable fix suggestions for single files or entire projects.

Exposes Evan's Debugger (EDB) features as MCP tools, enabling AI-driven debugging, reverse engineering, and exploit development with 147 tools including program control, breakpoints, memory analysis, ROP gadgets, and pwntools integration.

Enables AI assistants to debug ARM Cortex-M targets via GDB and OpenOCD, supporting attach, breakpoints, stepping, register/memory inspection, and SVD peripheral decoding.

MCP server for agentic task orchestration and management, enabling AI agents to create goals, plan tasks with acceptance criteria, track dependencies, request human input, and provide proof of completion.

Bringing the debugging we know and love as human programmers to our AI agents – debug any supported language with breakpoints, variable/state inspection, and stepping, to supercharge agents' capabilities to reason about runtime code.

An MCP server that lets AI agents debug Python code by running scripts, setting breakpoints, stepping through execution, inspecting live variable values, and editing or reverting files to verify fixes. It relies only on Python's standard sys.settrace, requiring no debugpy, DAP, pdb, or PTY.

Enables MCP clients to manage a project-local tracker by exposing sprint, task, and subtask operations as tools over stdio.

Broccoli turns "the agent reads a stack trace and guesses" into "the agent drives the real debugger": it sets breakpoints, steps, inspects live variables over the Debug Adapter Protocol, and proposes a fix you approve in a diff — never applied silently.