MCP Tools | Model Context Protocol Server Directory

The LNbits MCP server bridges LLMs directly with the Lightning Network, enabling AI agents to manage Bitcoin payments and receive funds programmatically. Instead of relying on static API calls, developers can integrate real-time wallet capabilities—such as generating invoices and executing payouts—directly into their AI workflows. This tool transforms an LLM from a simple chat interface into a financial agent capable of handling micro-payments. It is particularly useful for building autonomous payment bots, implementing 'pay-per-query' AI services, or automating Lightning-based reward systems. Integration is straightforward for any MCP-compatible client, removing the need to write custom boilerplate for LNbits API authentication and endpoint mapping.

The supabase-mcp-server bridges the gap between LLMs and your Supabase infrastructure by exposing project and organization management via the Model Context Protocol. Instead of manually toggling through the Supabase dashboard to track project IDs or manage organizational settings, developers can now perform these administrative tasks directly through an MCP-enabled client. It effectively turns your AI assistant into a DevOps helper capable of querying project metadata and handling structural updates. This is particularly useful for developers managing multiple environments or automating the scaffolding of new projects without leaving their IDE. Integration is straightforward, requiring standard MCP configuration and a valid Supabase API key to authenticate requests.

The last9-mcp-server bridges the gap between local development and production observability by exposing telemetry data directly to LLMs. Instead of manually switching between an IDE and a monitoring dashboard to debug regressions, developers can query real-time logs, metrics, and distributed traces through the Model Context Protocol. This integration allows AI assistants to analyze live system behavior and correlate production errors with specific lines of code, transforming the debugging workflow from manual hypothesis testing to data-driven resolution. It is particularly useful for resolving 'it works on my machine' bugs where the root cause is tied to environmental state or specific production traffic patterns.

The bitcoin-mcp server bridges the gap between LLMs and the Bitcoin blockchain by providing a standardized interface for on-chain data retrieval and cryptographic operations. Instead of relying on the model's training data—which is inherently outdated for blockchain states—this tool allows AI to perform real-time tasks such as validating wallet addresses, decoding raw transaction hex, and querying current block heights or transaction details. For developers building crypto-integrated agents, it eliminates the need to write repetitive boilerplate for API calls to explorers or local nodes. It effectively transforms an AI from a general knowledge base into a functional Bitcoin interface capable of parsing complex chain data and managing key generation logic locally.

The bear-mcp-server bridges the gap between LLMs and Bear Notes, providing a standardized interface for AI models to interact with your local macOS knowledge base. Instead of manually copying and pasting notes, this MCP implementation allows the model to query, read, and retrieve specific content directly from your Bear library. For developers building personal productivity workflows or RAG-like systems on macOS, this tool transforms Bear from a static markdown editor into a dynamic context source. It integrates seamlessly with any MCP-compliant host, enabling the AI to reference your private documentation or project notes in real-time without requiring complex API middleware.

The azure-cli-mcp server bridges the gap between LLMs and your Azure environment by exposing the Azure CLI as a set of executable tools. Instead of manually copying resource IDs or querying the portal, developers can delegate infrastructure management—such as checking VM status, managing App Services, or listing resource groups—directly to the model. It functions as a thin, efficient wrapper, meaning it inherits the full capability and authentication flow of your local az CLI installation. For those already using the CLI, this integration removes the context-switching overhead, allowing for natural language infrastructure auditing and rapid resource manipulation without leaving the IDE.

The cal-mcp server extends LLM capabilities by offloading mathematical computations to a dedicated execution environment, eliminating the common 'hallucination' issues associated with complex arithmetic in large language models. Instead of relying on probabilistic token prediction for math, this tool allows the model to perform precise calculations via a standardized interface. It is particularly useful for developers building agents that require high numerical accuracy for financial data, scientific formulas, or dynamic coordinate calculations. Integration is straightforward via the Model Context Protocol, allowing any MCP-compatible client to treat the calculator as a native tool without requiring custom wrapper code for every new mathematical operation.

The mcp-stockfish server bridges the gap between LLMs and the Stockfish engine, allowing AI models to perform precise chess analysis and move validation. Instead of relying on the model's probabilistic understanding of board states—which often leads to illegal moves in complex positions—this tool provides a deterministic source of truth. Developers can integrate it to build chess coaching apps, automated game analysis tools, or sophisticated bots that combine LLM natural language reasoning with grandmaster-level engine calculations. It abstracts the engine's UCI protocol into a standardized MCP interface, making it trivial to plug Stockfish's evaluation capabilities into any MCP-compliant client across Windows, macOS, or Linux.

The latex-mcp-server bridges the gap between LLMs and the complex LaTeX ecosystem, moving beyond simple code generation to actual document orchestration. Instead of manually copying snippets into an editor, developers can now delegate the compilation process, bibliography management, and figure integration directly to the model. It is particularly useful for researchers and technical writers who need to automate the synchronization of cited papers with their manuscripts or execute visualization scripts to generate dynamic plots. By treating LaTeX as a programmable environment rather than a static markup language, this server reduces the friction of the 'write-compile-debug' loop and allows the AI to verify visual outputs in real-time.

The mcp-server-chatsum tool bridges the gap between fragmented chat histories and LLM context windows. Instead of manually scrolling through archives or copying logs, developers can programmatically query and summarize conversation threads directly within their MCP-enabled environment. It functions as a retrieval layer for chat data, allowing you to extract key decisions, action items, or technical context from previous discussions without leaving your IDE. By treating chat logs as a queryable data source, it streamlines the process of catching up on project threads or documenting undocumented verbal agreements, making it a practical utility for teams relying on asynchronous communication.

The tsgram-mcp server bridges the gap between Telegram's messaging interface and Claude's reasoning capabilities, specifically enabling local workspace interaction via a mobile device. Unlike standard chat bots, this implementation leverages the Model Context Protocol to allow developers to read and modify local files through a Telegram client. It effectively transforms a mobile device into a remote IDE terminal, allowing for asynchronous code reviews, quick hotfixes, and workspace management without needing a full desktop environment. For developers working in TypeScript, it provides a streamlined way to integrate LLM-driven file operations directly into a platform they already use for communication, reducing the friction of switching between mobile communication and development tools.

The mcp-simple-openai-assistant tool bridges the gap between Claude and OpenAI's Assistant API, allowing developers to leverage specific GPT-based assistants directly within an MCP-enabled environment. Instead of managing separate chat interfaces, you can now delegate complex tasks to pre-configured OpenAI assistants that possess their own custom instructions and knowledge bases. This is particularly useful for workflows requiring the specialized reasoning of a GPT-4o assistant while utilizing Claude as the primary orchestration layer. Integration is straightforward, requiring only an OpenAI API key and the assistant ID, effectively turning your LLM interface into a multi-model hub without writing custom glue code.

The mcp-datetime server addresses a common LLM limitation: the lack of a reliable, real-time clock and native date manipulation capabilities. Instead of relying on the model's static training data or inconsistent system prompts for the current time, this tool provides a standardized interface for retrieving precise timestamps and formatting dates across different locales. For developers building agents, this is essential for tasks involving scheduling, calculating time deltas, or generating time-stamped logs. It integrates seamlessly via the Model Context Protocol, allowing the AI to call specific time functions as tools rather than guessing the date, which significantly reduces hallucinations in time-sensitive workflows.

The QGIS MCP server bridges the gap between LLMs and Geographic Information Systems by exposing QGIS Desktop's internal API to Claude. Instead of manually writing PyQGIS scripts or navigating complex toolboxes, developers can now manipulate geospatial data, manage project layers, and execute spatial analysis via natural language prompts. This tool effectively turns an AI assistant into a remote operator for your local GIS environment. It is particularly useful for rapid prototyping of spatial workflows, automating repetitive map styling, or debugging PyQGIS code in real-time without leaving the chat interface. By treating QGIS as a programmable tool rather than just a standalone application, it streamlines the transition from conceptual spatial queries to actual map production.

The hmk/attio-mcp-server implements the Model Context Protocol to bridge the gap between LLMs and Attio CRM data. Instead of manually exporting CSVs or writing custom API middleware, developers can now grant their AI agents direct read-write access to Attio records and notes. This is particularly useful for automating lead qualification, updating pipeline stages via natural language, or synthesizing customer interaction history during a chat session. By exposing Attio's data schema as MCP tools, the server allows the model to query specific objects and modify entries dynamically, turning a static CRM into an actionable context layer for your AI workflow.

The mcp-shell-server bridges the gap between LLMs and your local terminal by implementing the Model Context Protocol for shell execution. Unlike basic code interpreters that run in isolated sandboxes, this tool allows a model to interact directly with your system's shell, enabling it to perform file system operations, execute build scripts, and manage local processes in real-time. It is particularly useful for developers who want their AI assistant to handle repetitive CLI tasks, debug environment configurations, or automate git workflows without manual copy-pasting. Integration is straightforward via MCP-compliant hosts, providing a standardized interface for command execution while maintaining a focused scope on shell interaction rather than general-purpose API orchestration.

The dkvdm/onepassword-mcp-server bridges the gap between agentic AI workflows and secure credential management. Instead of hardcoding secrets or relying on insecure environment variables, this MCP server allows LLMs to dynamically fetch passwords, API keys, and configuration secrets directly from 1Password. For developers building autonomous agents, this means your AI can authenticate with third-party services on the fly without compromising security. It integrates seamlessly into any MCP-compliant host, transforming 1Password into a live context source for your AI's operational needs. Compared to manual secret injection, this approach provides a centralized, encrypted source of truth that scales across different environments and team members.

The tfmcp server bridges the gap between LLMs and Infrastructure as Code by implementing the Model Context Protocol for Terraform. Instead of manually copying HCL blocks into a chat window, developers can grant their AI assistants direct read/write access to their Terraform environments. The tool enables an AI to parse existing configurations, execute plans to preview changes, and trigger applies to deploy infrastructure. By exposing state management and plan analysis as tool-calls, it transforms the AI from a code generator into an operational partner capable of validating infrastructure changes against actual state before execution.

For developers integrating LLMs into local workflows, executing generated code safely remains a primary challenge. The mcp_safe_local_python_executor implements a secure runtime environment leveraging the Hugging Face Smolagents architecture. Unlike standard raw shell execution, this tool provides a sandboxed approach to Python interpretation, allowing the model to perform data manipulation, complex calculations, and logic processing without risking host system stability. It is particularly useful for building agents that require precise numerical outputs or dynamic data transformation where static prompting fails. Integration is straightforward via the Model Context Protocol, effectively turning your LLM into a functional data analyst with a restricted, safe execution layer.

The pyATS_MCP tool bridges the gap between LLMs and network infrastructure by exposing Cisco's pyATS framework via the Model Context Protocol. Instead of relying on fragile screen-scraping or raw CLI output, this tool allows developers to perform structured, model-driven queries across network devices. It is particularly useful for automating state verification, auditing configurations, and troubleshooting connectivity without writing boilerplate connection logic for every request. By integrating this into an MCP-compliant environment, you shift from manual 'show' commands to programmatic data retrieval, enabling the AI to analyze network health using validated schemas rather than guessing based on unstructured text.

The mcp-shell server bridges the gap between LLM reasoning and local execution by providing a secure interface for running shell commands. Unlike basic terminal plugins, this tool focuses on isolation, allowing developers to execute scripts and CLI tools within Docker containers rather than directly on the host OS. This architecture mitigates the risk of destructive commands while giving the AI the ability to perform real-world tasks like environment setup, log analysis, and file manipulation. It is particularly useful for automating repetitive DevOps workflows or debugging system configurations where an AI needs to observe actual command output to iterate on a solution. Integration is straightforward, fitting into any MCP-compliant client to turn a chat interface into a functional remote terminal.

The ntfy-mcp server bridges the gap between LLM workflows and real-time mobile alerts. Instead of polling a console or waiting for a chat interface to refresh, developers can integrate asynchronous notifications directly into their AI-driven agents. By leveraging the ntfy protocol, this tool allows an MCP-enabled client to push critical updates, error logs, or completion alerts to a mobile device without requiring a complex backend setup. It is particularly useful for long-running autonomous tasks where you need to be notified the moment a process fails or finishes while you are away from your workstation. Integration is straightforward, turning your LLM from a passive responder into a proactive alerting system.

Magic-MCP bridges the gap between LLM code generation and high-end UI engineering by providing direct access to a curated library of professional-grade components. Instead of relying on the model to guess modern design trends or struggle with complex Tailwind configurations, this tool allows developers to inject production-ready, aesthetically polished UI patterns directly into their workflow. It is particularly useful for rapid prototyping and building dashboards where visual fidelity is critical but manual CSS polishing is time-consuming. By integrating this into your MCP-enabled environment, you shift from generating generic boilerplate to implementing refined components inspired by top-tier design engineers, significantly reducing the iteration loop between initial prompt and final UI.

The omniparser-autogui-mcp server bridges the gap between LLMs and local desktop environments by enabling direct GUI interaction. Unlike standard API-driven tools, this MCP implementation leverages OmniParser to visually analyze screen coordinates and execute precise mouse and keyboard events. For developers, this means you can build agents capable of navigating legacy software, complex web apps, or any desktop application without requiring a native API or accessibility tree. It transforms the LLM from a text generator into an operator that can perceive UI elements and perform actions based on visual feedback, making it ideal for automated testing, RPA, or creating personalized desktop assistants.