MCP server for Obsidian

DodoPayments MCP Server allows AI assistants to: - Manage Payments: Create, retrieve, and list payments. - Handle Subscriptions: Create, retrieve, update, and list subscriptions. - Manage Customers: Create, retrieve, update, and list customer information. - Process Refunds: Initiate and retrieve refund details. - Manage Products: Create, retrieve, update, and list products. This integration enables AI-driven interaction with DodoPayments' services, facilitating tasks such as payment processing and subscription management through natural language commands.

MCP Server Chatsum allows AI assistants to access and summarize chat messages from a local SQLite database. It provides tools to query messages based on specific parameters and generate summaries, enhancing the efficiency of managing and retrieving chat histories.

DevDocs allows AI assistants to access and process technical documentation, enabling efficient retrieval and understanding of coding resources. It integrates seamlessly with various development tools, enhancing the coding experience by providing quick access to relevant documentation.

Raindrop.io MCP Server allows AI assistants to: - Add Bookmarks: Save new bookmarks to Raindrop.io with specified tags. - Fetch Latest Bookmarks: Retrieve the most recently added bookmarks. - Search Bookmarks by Tag: Find bookmarks associated with specific tags. - Search Bookmarks by Query: Perform text-based searches across bookmarks.

WCGW empowers AI assistants to perform coding and shell tasks directly on your local machine. It offers features like: Autonomous Execution: AI can execute shell commands and coding tasks. Interactive Command Handling: Supports interactive commands using arrow keys and other inputs. File Protections: Ensures AI reads a file before editing to prevent accidental overwrites. Shell Optimizations: Manages single shell instances to avoid rogue processes. Large File Editing: Supports incremental edits to large files, avoiding token limit issues. Syntax Checking: Provides feedback to the AI if edits contain syntax errors. These capabilities allow AI assistants to code, build, and run tasks locally, enhancing development workflows.

The Unity MCP Server is an open-source utility that facilitates seamless communication between the Unity Editor and Large Language Models (LLMs) such as Claude Desktop and Cursor via the Model Context Protocol (MCP). This integration empowers developers to automate workflows, manipulate assets, and control the Unity Editor programmatically. Key Features: - Editor Automation: Enables execution of various Unity Editor actions through MCP clients, allowing for tasks such as creating and modifying assets, managing scenes, and controlling game objects. - Two-Way Communication: Facilitates sending commands from Unity to MCP-compliant tools and receiving responses, enabling dynamic interactions between the Unity environment and external AI models. - Extensibility: Provides a framework for adding new tools and command handlers, allowing developers to extend functionality by creating custom C# command handlers and corresponding Python tools. - Integration with MCP Clients: Designed to work seamlessly with MCP clients like Claude Desktop and Cursor, enhancing the capabilities of AI-assisted development within Unity. Recent Updates: - Tool Overhaul (v1.0.1): The number of tools has been streamlined from 36 to 7 to reduce context and improve the accuracy of LLMs in selecting appropriate tools.

EdgeOne Pages MCP allows AI assistants to: - Deploy HTML Content: Send HTML content to EdgeOne Pages and receive a publicly accessible URL. - Automate Content Hosting: Utilize EdgeOne Pages' infrastructure for rapid content delivery. This integration facilitates AI-driven deployment of web content, streamlining the process of hosting and sharing HTML pages.

Documentation for Velt SDK. Velt provides ready-made enterprise grade collaboration features like Comments, Notifications, Recording, Live Editing etc.

Baidu AI Search MCP Server integrates Baidu's search capabilities with large language models via the Model Context Protocol (MCP), enabling AI assistants to provide intelligent responses with real-time web information.

Serena enhances LLMs by providing essential tools for semantic code retrieval and editing. It integrates with existing LLMs, enabling them to perform coding tasks directly within your codebase. Serena supports multiple programming languages through the Language Server Protocol (LSP), including Python, Java, and TypeScript. It can be integrated with LLMs via the Model Context Protocol (MCP) or used with Agno, a model-agnostic agent framework.