Discover Awesome MCP Servers

beacon-mcp

MCP server for the beacon log analytics platform, exposing beacon's REST API as MCP tools and resources for AI agents to query and analyse AI-assistant usage data.

fast-note-sync-mcp

MCP server that wraps the Fast Note Sync REST API to enable querying and editing Obsidian vaults via MCP tools.

BigBrain MCP Server

Enables AI agents to overcome context loops by extracting and packaging code for external AI consultation or automated multi-agent collaboration, with optional ChatGPT Desktop integration.

rybbit-mcp

Exposes Rybbit Analytics as MCP tools for querying site traffic, page views, visitor sessions, and live visitor counts through natural language.

yt-analytics-mcp

Owner-side YouTube analytics for AI agents: watch time, traffic sources, audience retention, playlist and podcast-series metrics, and episode-over-episode comparison. Read-only, all of it.

Doppler MCP Server

Enables secure secrets management through the Doppler CLI via natural language interactions. Supports managing secrets, projects, configs, and environments across different Doppler workspaces.

Surfmeter MCP Server

A Model Context Protocol server for the AVEQ Surfmeter management API. It exposes typed tools for clients, measurements, anomalies, groups, ISPs, users, keys, capabilities, settings, license usage, and the built-in AI assistant.

SkySQL MCP Server

SkySQL MCP サーバーとクライアントのリポジトリ。

dad-jokes-mcp

Enables fetching and managing dad jokes with tools for random jokes, multiple jokes, categories, and persistent storage.

CCCMemory MCP

Provides Claude with long-term memory by indexing conversation history, enabling semantic search, decision tracking, and cross-project search.

bhs-mcp

An MCP server for Blackhearts & Sparrows product data that enables searching products, retrieving details, listing stores, and managing carts via a sandboxed TypeScript execution environment.

date-today-mcp

Provides the current date in multiple formats (e.g., European, ISO, US) via a simple MCP tool.

GitLab MCP Server

Enables AI agents to interact with GitLab via a restricted set of 9 tools using OAuth 2.0 authentication, supporting merge requests, comments, pipelines, and labels.

shiryan-mcp-server

Enables AI agents to access SHIRYAN's change detection, geofencing, and alert synthesis for monitoring pipeline and critical infrastructure corridors in Saudi Arabia via MCP stdio tools.

marm-mcp

MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

Microsoft Copilot Studio ❤️ MCP

A lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.

sru-mcp

An MCP server that searches library catalogs worldwide using the SRU protocol, enabling bibliographic search without API keys.

MCP-Context-Provider

A static MCP server that helps AI models maintain tool context across chat sessions, preventing loss of important information and keeping conversations smooth and uninterrupted.

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.

Sentinela MCP

Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.

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.

patents-mcp

MCP server for patent search and prior art discovery powered by Google Patents public dataset on BigQuery. Supports searching patents, fetching full patent details with CPC codes and citations, and retrieving legal claims text.

ORMCP Server

ORMCP Server is a database-agnostic MCP server that exposes relational databases as governed business objects (Customers, Orders, Products) for AI agents via ORM abstraction — instead of raw SQL or schema access. Works with any JDBC-compliant database (PostgreSQL, MySQL, Oracle, SQL Server, SQLite, and more). Reduces LLM token consumption by 60-70% through semantic data abstraction.

Godot MCP

A security-first MCP server and Godot editor addon enabling AI agents to observe and control Godot games through bounded, permission-gated tools for debugging, input automation, and project editing.

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.

PocketMCP

A lightweight, local-first MCP server that automatically watches folders, chunks and embeds files using Transformers.js, and exposes semantic search capabilities to VS Code and Cursor. Runs completely offline with SQLite vector storage, designed for resource-constrained environments.

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.

Android MCP Server

Enables AI agents to directly control Android devices via Termux, providing 120+ tools for screen manipulation, file management, app control, and system operations with layered loading and security gating.

TRAECNclaw MCP

Control TraeCN desktop automation through explicit, profile-scoped MCP tools. Supports task delegation, model control, dialog handling, code review, and unattended workflows.

Cloudflare Remote PostgreSQL MCP Server

Enables natural language interaction with PostgreSQL databases through MCP tools, with GitHub OAuth authentication and role-based access control.