"How to find and troubleshoot a memory leak in software" matching MCP servers:

Pull Deep Memory from across your Claude Code Sessions — when you need it.

Enterprise-grade MCP (Model Context Protocol) server for detecting secrets and sensitive information in GitHub repositories. Scans for 35+ types of secrets including API keys, passwords, tokens, and credentials with production-ready reliability features.

Enables AI agents to autonomously scan repositories and staged changes for leaked secrets, move exposed credentials into environment files, purge secrets from Git history, and verify remediation through deterministic tools.

Provides software design guidance via MCP, including SOLID principles, design patterns, pattern scaffolding, and heuristic code-smell detection to help review and refactor code.

An MCP server that facilitates interactive software development planning by breaking down projects into manageable tasks, tracking progress, and saving implementation plans.

Facilitates software development planning through interactive sessions that break down projects into manageable tasks with complexity scoring, code examples, and implementation plan management.

Facilitates interactive software development planning by managing tasks, tracking progress, and creating detailed implementation plans through the Model Context Protocol.

Enables converting software development requirements into traceable estimates, WBS breakdowns, PERT calculations, risk registers, roadmaps, quotations, and professional PDF proposals or action plans. It supports reviewing assumptions, exclusions, scope gaps, and commercial documentation before sending deliverables to clients.

Enables Claude to function as a full-stack software engineer with comprehensive development capabilities including project creation, database management, frontend/backend development, testing, deployment, and DevOps operations across multiple frameworks and technologies.

A discovery tool for AI agents to search, evaluate, and install MCP servers from multiple registries.

Enables scanning arbitrary text and added lines in unified git diffs for accidentally committed credentials, using precise vendor-format matches and entropy-based detection. All detected secrets are redacted before results are returned, helping coding agents and developers catch leaks without exposing full values.

A Windows-local MCP server that connects natural-language engineering tasks to COMSOL, Autodesk Fusion, and LabVIEW through their official software interfaces. It lets users submit, track, and verify parametric simulation, CAD modeling, and measurement jobs as configurable tools.

Provides software development planning tools to help users create implementation plans and manage todo items.

An MCP server that enables searching for specific files by name within the current directory and its subdirectories. It uses Server-Sent Events (SSE) to provide a find_file tool for locating local files and libraries.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Enables visual drag-and-drop editing of Mermaid diagrams through Claude, allowing iterative refinement of software architecture designs.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.

Auto-captured, auto-recalled, path-scoped memory for AI coding agents and teams.

Enables users to describe an LLM fine-tuning workload and receive VRAM/FLOPs estimates plus ranked GPU recommendations across multiple cloud providers, optimizing for cost, speed, or balance.