"MCP servers for curated context in Cursor IDE to plan, debug, and iterate on features" matching MCP servers:

Enables Cursor AI to read and modify Figma designs programmatically via the Model Context Protocol.

Cross-platform MCP server providing Claude Code-style plan mode via four tools (enter_plan_mode, get_plan_mode_standards, todo_write, exit_plan_mode) with soft guidance through SKILL.md.

A FastMCP server for Cursor platform APIs enabling spend guardrails, cloud agent monitoring, and inter-agent inbox messaging.

This MCP server generates individualized meal plans for children with chronic kidney disease (CKD) based on KDIGO 2024/PRNT 2020/KRCP 2025 guidelines, incorporating patient age, CKD stage, allergies, dietary culture, and diet diary analysis. It provides deterministic nutrient computations and tools for meal plan generation, food database lookup, and risk analysis.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Enables interactive feedback with image support in Cursor, allowing users to paste images for visual context and feedback.

Enables systematic investigation of VMware errors and events by correlating event timelines, ranking root-cause hypotheses, and suggesting next diagnostic steps.

An MCP server that lets AI agents drive real Android apps on emulators/devices, returning screen state, logs, and screenshots after every action, and learns a navigation map of the app for efficient route-based interaction.

Enables AI tools to perform full-featured embedded microcontroller debugging via pyOCD and CMSIS-DAP probes, including probe management, flashing, breakpoints, register/memory access, fault analysis, and RTT communication.

Generates multi-day CKD meal plans for children based on PRNT targets (energy, protein, potassium, phosphorus, sodium), providing nutrient summaries and target achievement rates for dietitians.

Provides GDB and clangd-based tools for debugging C++ programs, enabling step execution, variable inspection, diagnostics, and combined runtime-static analysis.

Provides AI agents with Roku BrightScript debugging tools, including log reading, scene graph inspection, breakpoint management, variable inspection, and execution control.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

A workspace of six MCP servers—grounded-cv (hybrid RAG with citations), repo-intel (GitHub repo intelligence), web-research (safe web search/extraction), ats-jobs (unified ATS job boards), outcome-ledger (durable task ledger), and coding-workflows (code review & planning)—purpose-built for agentic hiring evaluations.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

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.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.