"Understanding or finding information about cursors" matching MCP servers:

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.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

MCP server that analyzes videos locally with frame-by-frame understanding and optional browser sidecars, producing structured context bundles for coding agents to fix bugs, add features, or build new functionality.

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

Lets you @mention a bot in a Slack channel or DM to send work to a local Claude Code session and watch it run, with live self-updating turn cards, threaded answers, progress checklists and a per-channel inbox with read cursors. It runs entirely on your machine over Slack Socket Mode—no tunnel or public IP—routes misdelivered messages to the right project, and masks credentials in anything it posts back.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Runs as an MCP server providing file system operations, shell execution, and system information tools, allowing Claude or any MCP client to edit files and control the machine.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

A Model Context Protocol (MCP) server providing 62 AI-optimized tools for .NET/C# semantic code analysis, navigation, refactoring, and code generation using Microsoft Roslyn. Built for AI coding agents - provides compiler-accurate code understanding that AI cannot infer from reading source files alone.

Provides IDE-like semantic code retrieval and editing tools for LLMs, enabling precise code understanding and manipulation in large codebases via the Model Context Protocol.

An MCP server leveraging the Rust-based ty type checker to provide AI models with high-performance, semantic Python code analysis and structural navigation. It enables precise symbol searching, cross-file renaming, and diagnostic reporting to improve code understanding and editing accuracy.

An MCP server that enables Claude and other LLM clients to access Hanzo AI platform capabilities and provide powerful code management tools for analyzing, modifying, and understanding projects.

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

Enables finding existing shadcn-format UI components by plain-language description instead of exact names, returning ranked matches with install commands and per-dimension match reasons.

Enables coding agents to check human-stated assumptions against authoritative sources like AWS AppConfig, Sentry, and the repository, detecting when beliefs have drifted from reality. It provides tools to inspect belief history, list verifiable properties, review open drifts, and record verified understanding.

MCP server that lets Claude, ChatGPT, or VS Code build a complete, machine-checked model of your domain and generate an app from it.

An MCP server for codebase context that gives AI coding agents structural understanding through symbol graph, semantic search, blast radius, and convention detection tools.

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.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

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