Discover Awesome MCP Servers

EDT Unicaen MCP Server

Enables querying University of Caen (Unicaen) schedule data to find next classes for professors, students, or rooms and check room availability. Integrates with ADE scheduling system to provide real-time schedule information through natural language queries.

Goodreads MCP Server

A CLI tool that integrates with Claude Desktop to allow users to retrieve their Goodreads library data through MCP.

Discourse MCP

Enables AI agents to interact with Discourse forums through search, reading topics/posts, browsing categories and tags, and creating posts. Supports both read-only public access and authenticated operations with configurable write permissions.

NotionMCP Server

A Python server that integrates MCP with Notion to automate documentation workflows and manage Notion pages via LLMs, supporting operations like creating, updating, and searching Notion content.

Capture Win MCP

Enables AI assistants to interact with macOS windows through yabai, providing window listing organized by Spaces and screenshot capture capabilities.

Revit MCP

Allows AI to interact with Autodesk Revit via the MCP protocol, enabling retrieval of project data and automation of tasks like creating, modifying, and deleting elements.

MongoDB MCP Server by CData

MongoDB MCP Server by CData

Metabase MCP Server

Enables AI assistants to interact with Metabase analytics platform, allowing them to query databases, execute SQL, manage dashboards and cards, and access analytics data through natural language.

Fal Recraft V3 MCP Server

Provides access to the fal-ai/recraft/v3 image generation model for creating high-quality images with advanced style controls including realistic, digital illustration, and vector art styles, plus color guidance and local image download capabilities.

aster-info-mcp

An MCP server that provides structured access to Aster DEX market data—covering candlesticks, order books, trades, and funding rates.

Klipper MCP Server

An MCP server that enables AI assistants to control and monitor Klipper 3D printers via the Moonraker API. It supports comprehensive printer management, including G-code execution, toolchanger operations, and real-time status monitoring.

freedcamp

freedcamp

Enhanced Sequential Thinking MCP Server

Enables structured problem-solving through sequential thinking stages with persistent storage and analysis. Helps break down complex problems into manageable cognitive steps while tracking progress and generating summaries of the entire thought process.

TypeScript MCP Server Boilerplate

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example tools (calculator, greeting) and resources pre-configured.

EndNote Library Reader

Enables programmatic access to EndNote .enl libraries, allowing users to list, search, and extract full text from bibliographic references and their attached PDFs through MCP tools.

LangChain Documentation MCP Server

Provides real-time access to official LangChain documentation, API references, and GitHub code examples to assist in LangChain-based development. It enables LLMs to search for tutorials, version info, and detailed class specifications directly from live sources.

Nodit Mcp Server Poc

Nodit API MCP サーバー POC

Portainer MCP

Portainer MCP サーバー (Portainer MCP sābā)

Rezdy Agent MCP Server

An MCP server for integrating with the Rezdy Agent API to manage travel marketplace products and bookings. It enables travel agents to search availability, process payments, and manage customer relationships using natural language.

mcp-k8s

KubernetesクラスタとMCPツールを介したインタラクションを可能にする、Kubernetes MCP (Model Control Protocol) サーバー。

Locations MCP Server

Enables WordPress content extraction, keyword research using Google autocomplete and Trends data, and generation of SEO-optimized content with quality metrics.

Ronin Defense MCP

Detects and sanitizes hidden-payload attacks in MCP communications to protect LLMs in agentic IDEs from malicious prompt injection and adversarial content delivery.

Todoist MCP

Enables LLMs to interact with Todoist task management platform through its API, supporting all features from the official Todoist TypeScript Client.

microsandbox

microsandbox

Ember MCP Server

A Model Context Protocol server that provides tooling support for Ember.js development, allowing developers to execute CLI commands, run codemods, access documentation, and discover community resources.

mcp-angular-cli

mcp-angular-cli

MCP Kali Server

MCP (Management Control Protocol) configuration to connect an AI agent to a Linux machine depends heavily on the specific MCP implementation and the AI agent you are using. There isn't a single, universal configuration. However, I can provide a general outline and considerations, along with examples of common approaches. You'll need to adapt this to your specific tools and environment. **General Considerations:** * **MCP Implementation:** What MCP system are you using? Examples include: * **Custom MCP:** If you've built your own MCP, you'll need to refer to its documentation. * **Existing Management Frameworks:** Some frameworks like Ansible, Chef, Puppet, or SaltStack could be used as a foundation for your MCP. In this case, you'd configure the AI agent to interact with the framework's API. * **Cloud Provider Services:** If your Linux machine is in a cloud environment (AWS, Azure, GCP), you might leverage their management services (e.g., AWS Systems Manager, Azure Automation, Google Cloud Operations). * **AI Agent Capabilities:** What can your AI agent do? Can it: * Execute commands on the Linux machine? * Read files and system information? * Monitor system metrics? * Deploy software? * Authenticate securely? * **Security:** This is paramount. Consider: * **Authentication:** How will the AI agent authenticate with the Linux machine? SSH keys, passwords (discouraged), certificates, or API keys are possibilities. * **Authorization:** What permissions will the AI agent have? Use the principle of least privilege. Don't give it root access unless absolutely necessary. * **Encryption:** Use encryption (e.g., SSH, TLS) for all communication between the AI agent and the Linux machine. * **Auditing:** Log all actions performed by the AI agent. * **Network Connectivity:** Ensure the AI agent can reach the Linux machine over the network. Firewall rules may need to be adjusted. **Example Scenario: AI Agent using SSH and Python (Paramiko) for a Custom MCP** This is a common and relatively straightforward approach. Let's assume: * The AI agent is a Python script. * It uses the `paramiko` library to connect to the Linux machine via SSH. * It needs to execute commands and retrieve output. **1. Linux Machine Setup:** * **SSH Server:** Ensure the SSH server (`sshd`) is running and configured securely. Disable password authentication if possible and use SSH keys. * **User Account:** Create a dedicated user account for the AI agent with limited privileges. For example: ```bash sudo adduser aiagent sudo usermod -aG sudo aiagent # If the agent needs to run commands with sudo sudo passwd aiagent # Set a strong password (if you must use passwords) ``` * **SSH Key Authentication (Recommended):** * Generate an SSH key pair on the AI agent's machine. * Copy the public key (`id_rsa.pub`) to the `~/.ssh/authorized_keys` file of the `aiagent` user on the Linux machine. ```bash # On the AI agent's machine: ssh-keygen -t rsa -b 4096 # Copy the public key to the Linux machine (using ssh-copy-id or manually): ssh-copy-id aiagent@<linux_machine_ip_address> # Or manually: scp ~/.ssh/id_rsa.pub aiagent@<linux_machine_ip_address>:/tmp/aiagent_key.pub ssh aiagent@<linux_machine_ip_address> "mkdir -p ~/.ssh && cat /tmp/aiagent_key.pub >> ~/.ssh/authorized_keys && rm /tmp/aiagent_key.pub && chmod 700 ~/.ssh && chmod 600 ~/.ssh/authorized_keys" ``` **2. AI Agent (Python) Configuration:** ```python import paramiko def execute_command(hostname, username, private_key_path, command): """Executes a command on a remote Linux machine via SSH.""" try: ssh_client = paramiko.SSHClient() ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # WARNING: Insecure for production! Use known_hosts. private_key = paramiko.RSAKey.from_private_key_file(private_key_path) ssh_client.connect(hostname=hostname, username=username, pkey=private_key) stdin, stdout, stderr = ssh_client.exec_command(command) output = stdout.read().decode('utf-8') error = stderr.read().decode('utf-8') ssh_client.close() return output, error except Exception as e: return None, str(e) # Configuration hostname = "<linux_machine_ip_address>" username = "aiagent" private_key_path = "/path/to/your/id_rsa" # Path to the AI agent's private key command_to_execute = "ls -l /home/aiagent" # Execute the command output, error = execute_command(hostname, username, private_key_path, command_to_execute) if output: print("Output:\n", output) else: print("Error:\n", error) ``` **Explanation of the Python Code:** * **`paramiko.SSHClient()`:** Creates an SSH client object. * **`set_missing_host_key_policy(paramiko.AutoAddPolicy())`:** **CRITICAL SECURITY WARNING:** This automatically adds the host key to the `known_hosts` file. This is convenient for testing but **highly insecure** for production. In production, you should manually verify and add the host key to the `known_hosts` file. See `ssh-keyscan`. * **`paramiko.RSAKey.from_private_key_file()`:** Loads the SSH private key from the specified file. * **`ssh_client.connect()`:** Connects to the Linux machine using the provided credentials. * **`ssh_client.exec_command()`:** Executes the specified command on the remote machine. * **`stdout.read().decode('utf-8')`:** Reads the standard output from the command and decodes it as UTF-8. * **`stderr.read().decode('utf-8')`:** Reads the standard error from the command. * **`ssh_client.close()`:** Closes the SSH connection. **3. MCP Integration:** The `execute_command` function is the core of the MCP interaction. You would integrate this into your AI agent's logic. For example: * **Command Execution:** The AI agent might receive a command from a central server and use `execute_command` to run it on the Linux machine. * **Data Collection:** The AI agent might run commands to collect system information (e.g., CPU usage, memory usage, disk space) and send it back to a central server. * **Automated Tasks:** The AI agent might be scheduled to run certain commands periodically to perform maintenance tasks. **4. Security Hardening (Important):** * **`known_hosts`:** Replace `paramiko.AutoAddPolicy()` with proper `known_hosts` verification. Use `ssh-keyscan` to get the host key and add it to the `known_hosts` file. * **Firewall:** Restrict SSH access to only the AI agent's IP address. * **Least Privilege:** Grant the `aiagent` user only the necessary permissions. Use `sudo` with caution and configure it to allow only specific commands. * **Logging:** Implement comprehensive logging of all actions performed by the AI agent. * **Regular Security Audits:** Regularly review the configuration and logs to identify and address any security vulnerabilities. **Example in Japanese (Configuration Comments):** ```python import paramiko def execute_command(hostname, username, private_key_path, command): """リモートのLinuxマシンでSSH経由でコマンドを実行します。""" try: ssh_client = paramiko.SSHClient() ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # 警告: 本番環境では安全ではありません！ known_hostsを使用してください。 private_key = paramiko.RSAKey.from_private_key_file(private_key_path) ssh_client.connect(hostname=hostname, username=username, pkey=private_key) stdin, stdout, stderr = ssh_client.exec_command(command) output = stdout.read().decode('utf-8') error = stderr.read().decode('utf-8') ssh_client.close() return output, error except Exception as e: return None, str(e) # 設定 hostname = "<linux_machine_ip_address>" # LinuxマシンのIPアドレス username = "aiagent" # ユーザー名 private_key_path = "/path/to/your/id_rsa" # AIエージェントの秘密鍵へのパス command_to_execute = "ls -l /home/aiagent" # 実行するコマンド # コマンドを実行 output, error = execute_command(hostname, username, private_key_path, command_to_execute) if output: print("出力:\n", output) else: print("エラー:\n", error) ``` **Other Approaches:** * **Ansible/Chef/Puppet/SaltStack:** Use these configuration management tools to manage the Linux machine. The AI agent would interact with the tool's API to trigger configuration changes or execute commands. This provides a more structured and auditable approach. * **Cloud Provider Services:** If you're using a cloud provider, leverage their management services. For example, AWS Systems Manager allows you to run commands on EC2 instances without SSH. * **gRPC/REST API:** Create a custom gRPC or REST API on the Linux machine that the AI agent can call. This provides a more controlled and secure interface. **Key Takeaways:** * **Security is paramount.** Implement strong authentication, authorization, and encryption. * **Use the principle of least privilege.** Grant the AI agent only the necessary permissions. * **Choose the right MCP implementation for your needs.** Consider the complexity, scalability, and security requirements of your application. * **Thoroughly test and monitor your configuration.** Remember to replace the placeholder values (e.g., `<linux_machine_ip_address>`, `/path/to/your/id_rsa`) with your actual values. This is a starting point; you'll need to adapt it to your specific environment and requirements.

Blind-Audition-MCP

A zero-cost MCP server that forces AI to self-correct code using prompt injection and context isolation.

GitHub DeepWiki MCP Server

This MCP server provides detailed GitHub repository information by integrating with mcp.deepwiki.com and GitHub API to fetch repository wiki content, README files, and enable intelligent question-answering about repositories.

Salesforce MCP Server

Enables natural language interactions with Salesforce data and metadata, allowing users to query records, manage custom objects, and manipulate Apex code. It provides comprehensive tools for schema exploration, aggregate queries, and field-level security management.