"Understanding Perplexity in Relation to Claude" matching MCP servers:

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

Analyzes source code dependencies across multiple programming languages in the specified directory to identify file relationships, assisting in dependency management and project structure understanding.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Provides access to Perplexity AI models through two tools: ask\_perplexity for expert programming assistance and chat\_perplexity for maintaining ongoing conversations with context preservation.

A comprehensive MCP server that provides intelligent access to Perplexity AI's search and reasoning models with automatic model selection, conversation management, and project-aware storage. Supports real-time search, deep research, chat sessions, and async operations for complex queries.

Enables intelligent code analysis and debugging through the Perplexity AI's API, offering detailed error analysis, pattern detection, and comprehensive solutions, with integration support for the Claude desktop client.

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 mobile device automation for Android (via ADB) and iOS Simulator (via simctl), allowing you to control devices with natural language through screenshots, UI interactions, text input, and app management.

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.

A lightweight Python-based microservice that provides simple echo functionality, receiving messages and returning them back to the client for diagnostic and testing purposes within the MCP framework.

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 AI assistants like Claude and Cursor to interact directly with Figma to create, modify, and analyze design elements in real-time. It provides a comprehensive suite of tools for document inspection, styling, component management, and automated layout via a bidirectional WebSocket connection.

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.

MCP server that enables AI agents to read, analyze, and modify Figma designs, including comment management via Figma's REST API. Works with any Figma account and supports parallel execution.

Enables MCP clients such as Cursor to hand a bounded task to a scoped, non-interactive Claude Code session billed to a Claude subscription rather than Cursor model credits. Runs read-only by default, with optional file edits or Bash, plus budget, timeout, and session-resume controls.

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.

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.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

Enables users to describe an LLM fine-tuning workload and automatically estimate VRAM/FLOPs to recommend the cheapest, fastest, and balanced GPU options across multiple cloud providers. It supports natural-language requests through MCP, returning structured ranked hardware recommendations without manual price comparison.

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.