PlayCanvas Editor AI Automation
Automates the PlayCanvas Editor using an LLM for tasks such as creating and modifying entities, managing assets, and manipulating scene settings.
Browse 6,627+ Model Context Protocol servers for Claude, Cursor, and VS Code. Find the perfect tools for your AI workflow.
Automates the PlayCanvas Editor using an LLM for tasks such as creating and modifying entities, managing assets, and manipulating scene settings.
Exposes Portainer environment data through the Model Context Protocol (MCP), enabling AI assistants to interact with containerized infrastructure.
Empowers AI assistants to interact with Power BI datasets using natural language.
Enables management of Railway.app infrastructure, including deployments, variables, and services, through natural language using Model Context Protocol.
Enables AI agents to access and manage Raindrop.io bookmarks through the Model Context Protocol (MCP).
Enables AI assistants to control robots via ROS 2 topics using the Model Context Protocol.
Transforms SAP S/4HANA and ECC OData services into dynamic conversational AI interfaces by exposing them as MCP tools.
Integrates Sketch designs with IDEs to enable AI-powered design-to-code workflows.
Manages code snippets with AI-powered features using Azure Functions, OpenAI, and Cosmos DB, and exposes them as Model Context Protocol (MCP) tools for clients like GitHub Copilot.
Empowers LLMs to maintain memory state across interactions using a neural memory system that learns and predicts sequences.
Facilitates interaction with AI agents, supporting both default and custom agent configurations for multi-turn conversations and task automation.
Enables AI agents to query data stored in Axiom using the Axiom Processing Language (APL).
Enables AI models to interact with Bitcoin and Lightning Network, facilitating key generation, address validation, transaction decoding, and blockchain querying.
Enhances AI assistants with persistent conversation context, project history, and code relationships across sessions.
Serves as a Model Context Protocol (MCP) server for Daipendency, enabling access to dependency documentation.
Provides a native Delphi server implementation of the Model Context Protocol (MCP) for AI-powered Delphi development workflows.
Enables query and invocation of Dify workflows within an MCP server environment.
Automates external attack surface mapping by integrating reconnaissance tools and natural language processing.
Integrates the Fabric AI framework into Model Context Protocol (MCP) compatible applications.
Enables interaction with fal.ai models and services using the Model Context Protocol.
Connects AI agents with the CrowdStrike Falcon platform, powering intelligent security analysis in agentic workflows.
Provides an interface for AI assistants to access and analyze financial data from Financial Modeling Prep.
Connects Claude to FL Studio, enabling AI-driven music creation within the DAW.
Connects Large Language Models to GIS operations, enabling AI assistants to perform geospatial tasks.
MCP (Model Context Protocol) servers are tools that connect AI assistants like Claude, Cursor, and other AI coding tools to external data sources, APIs, and services. They enable AI models to access real-time information, execute code, interact with databases, and perform actions beyond their base capabilities. Check out our curated collection of the best MCP servers for Cursor →
MCP servers act as intermediaries between AI assistants and external systems. When you ask Claude or Cursor to perform a task requiring external data, the MCP server handles the request, fetches the data, and returns it in a format the AI can understand and use.
When selecting an MCP server, consider:
For detailed setup guides, see our Complete MCP Server Setup Guide →
Everything you need to know about installing, using, and troubleshooting Model Context Protocol.
To install MCP servers in Cursor: 1) Open Cursor settings (Cmd+,), 2) Navigate to MCP Servers section, 3) Click 'Add Server', 4) Enter server configuration (name and command), 5) Restart Cursor. Check out our curated collection of the best MCP servers for Cursor with detailed setup guides.
MCP servers are protocol-level integrations that work across multiple AI assistants (Claude, Cursor, etc.), while VS Code extensions are editor-specific. MCP servers provide context to AI models, whereas extensions add IDE functionality. MCP is more portable and AI-focused.
Most MCP servers listed are open-source and free. Some enterprise connectors (like Salesforce or Jira) may require paid API access to the underlying service, but the MCP server code itself is usually free.
Yes! The Model Context Protocol is open standard. You can build servers in Python or TypeScript/Node.js to expose your own internal APIs or databases to Claude and Cursor.
An MCP server is a lightweight program that exposes specific capabilities (data access, tool execution, prompts) to AI assistants through a standardized protocol. MCP servers allow Claude and other AI models to securely interact with external systems like databases, APIs, file systems, and web services while maintaining security boundaries.
MCP servers connect to Claude Desktop or other MCP-compatible clients through a standardized JSON-RPC protocol. When you configure an MCP server in Claude Desktop, Claude can access its resources (data/content), use its tools (functions to execute), and leverage its prompts (templated interactions). The protocol ensures secure, controlled access to external capabilities.
Essential MCP servers for beginners include: Filesystem (local file access), SQLite/PostgreSQL (database queries), GitHub (repository access), Brave Search (web search), and Memory (persistent context). These cover common use cases and have excellent documentation. Start with 2-3 servers and expand based on your needs.
Most MCP servers install via npm (npx) or Python (uvx/pip). Configure them in Claude Desktop's config file (claude_desktop_config.json) by specifying the command, arguments, and any required environment variables like API keys. Our directory links to each server's documentation with specific installation instructions.
MCP servers run locally on your machine with explicit permissions you configure. They can only access resources you authorize, and Claude cannot bypass these boundaries. However, always review server code, use official/verified servers when possible, limit permissions to what's necessary, and be cautious with servers that request broad access.
Resources are data sources Claude can read (files, database results, API responses). Tools are functions Claude can execute (run queries, send messages, create files). Prompts are pre-defined templates for common interactions. Most MCP servers expose one or more of these capability types.
MCP is supported by Claude Desktop (official), Cursor IDE, Continue (VS Code extension), and various community implementations. The protocol is open, so support is expanding to more AI development tools. Check our compatibility filters to find servers tested with your preferred client.
Our MCP servers directory is updated weekly with new servers, documentation improvements, and compatibility updates. We monitor GitHub releases, community contributions, and Anthropic announcements to ensure our listings reflect the latest MCP ecosystem developments.