Discover Awesome MCP Servers

@looppause/mcp

Pauses AI agent execution and routes approval requests to humans via Slack or email, with cryptographically signed proof of the human's decision.

linkedin-mcp

Enables AI agents to publish posts, images, comments, and reactions to LinkedIn as the authenticated user, with built-in safety features like daily budgets and deduplication.

talonic-mcp

Official Talonic MCP server. Lets AI agents extract structured, schema-validated data from any document, including PDFs, scans, images, spreadsheets, and forms, directly inside the chat.

ActTrace

MCP server for EU AI Act compliance, providing risk classification of AI features and Article 50 transparency notices.

depsonar

Comprehensive dependency audit MCP server supporting 9 languages and 23 tools for scanning, updating, security auditing, and migration detection.

browser-mcp

Exposes your Chrome/Edge browser as MCP tools for AI agents, enabling browser automation through a Chrome extension with 47 tools for navigation, interaction, page reading, and more.

MCP TypeScript SDK

A toolkit for building Model Context Protocol servers and clients that provide standardized context for LLMs, allowing applications to expose resources, tools, and prompts through stdio or Streamable HTTP transports.

Task MCP Server

A simple task management server that enables users to add, list, complete, and delete tasks through natural language interactions in Claude Desktop.

mcp-api-tester Tools & Interfaces

For testing LLM APIs, an "MCP server" isn't a standard term. It's likely you're looking for a way to **mock**, **simulate**, or **proxy** an LLM API endpoint. Here are a few ways to approach this, along with their Chinese translations: **1. Mock Server (模拟服务器 - mó nǐ fú wù qì):** * **Concept:** A mock server simulates the behavior of a real LLM API. You define the expected requests and the corresponding responses. This is ideal for testing your application's logic without actually calling the LLM. * **Tools:** * **Mockoon:** A popular and easy-to-use mock server application. * **WireMock:** A more powerful and flexible tool, often used in Java environments. * **JSON Server:** A simple way to create a REST API from a JSON file. * **How it works:** You configure the mock server to listen on a specific port and define routes that match the API endpoints you want to test. When your application makes a request to the mock server, it returns the pre-defined response. **2. Proxy Server (代理服务器 - dài lǐ fú wù qì):** * **Concept:** A proxy server sits between your application and the real LLM API. It can intercept requests and responses, allowing you to: * **Inspect traffic:** See exactly what data is being sent and received. * **Modify requests/responses:** Change the data being sent to the LLM or the data being returned to your application. * **Simulate errors:** Force the API to return error codes to test your application's error handling. * **Rate limiting:** Simulate rate limits to test your application's behavior under load. * **Tools:** * **Charles Proxy:** A popular commercial proxy tool. * **Fiddler:** A free proxy tool from Telerik. * **mitmproxy:** A free and open-source interactive HTTPS proxy. * **How it works:** You configure your application to use the proxy server as its HTTP/HTTPS proxy. The proxy server then intercepts all traffic and allows you to inspect and modify it. **3. Local LLM (本地LLM - běn dì LLM):** * **Concept:** Run a smaller, open-source LLM locally on your machine. This allows you to test your application against a real LLM without relying on an external API. * **Tools:** * **llama.cpp:** A library for running LLaMA models on CPUs. * **Ollama:** A tool for running and managing LLMs locally. * **GPT4All:** A project that provides a GUI and API for running LLMs locally. * **How it works:** You download and install the local LLM and then configure your application to connect to it. This gives you full control over the LLM and allows you to test your application in a completely isolated environment. **4. API Simulation Library (API 模拟库 - API mó nǐ kù):** * **Concept:** Libraries that allow you to create mock API responses directly within your code. * **Examples:** * **Python:** `unittest.mock` (built-in), `responses` * **JavaScript:** `nock`, `jest.fn()` * **How it works:** You use the library to replace the actual API call with a mock function that returns a pre-defined response. This is useful for unit testing individual components of your application. **Which approach is best for you depends on your specific needs:** * **Simple testing with static responses:** Mock server (Mockoon, JSON Server) * **Inspecting and modifying API traffic:** Proxy server (Charles, Fiddler, mitmproxy) * **Testing against a real LLM without external dependencies:** Local LLM (llama.cpp, Ollama, GPT4All) * **Unit testing individual components:** API simulation library (unittest.mock, responses, nock) **Example Scenario (使用 Mockoon 的例子 - shǐ yòng Mockoon de lì zi):** Let's say you want to test your application's interaction with an LLM API that generates text completions. The API endpoint is `https://api.example.com/completions` and it expects a JSON payload like this: ```json { "prompt": "The quick brown fox", "max_tokens": 50 } ``` You can use Mockoon to create a mock server that listens on port 3000 and defines a route for `/completions`. The mock server would return a JSON response like this: ```json { "completion": "The quick brown fox jumps over the lazy dog." } ``` Your application would then be configured to send requests to `http://localhost:3000/completions` instead of `https://api.example.com/completions`. This allows you to test your application's logic without actually calling the real LLM API. **In summary (总结 - zǒng jié):** Instead of a specific "MCP server," you're likely looking for a way to simulate or mock an LLM API. Consider using a mock server, proxy server, local LLM, or API simulation library, depending on your testing requirements. Each approach offers different levels of control and complexity. Choose the one that best suits your needs.

Rovodev MCP Tool

Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.

Black_Wall remote MCP

Provides a pre-signature payment-risk verdict (GO/HOLD/STOP) for x402 payments based on counterparty reputation, price anomaly, and OFAC sanctions.

CloudSee Drive MCP Server

Enables natural language management of files on CloudSee Drive (Amazon S3) including browsing, searching, uploading, downloading, sharing, and organizing, with confirmation for destructive actions.

obsidian-mcp-server

Connects Claude.ai to your local Obsidian vault for full CRUD access, search, and daily note creation via the Model Context Protocol.

国金QMT-MCP

A modular quantitative trading assistant that integrates with XTQuant/QMT trading platform, enabling AI-assisted trading strategy generation, real-time trade execution, and performance backtesting.

Design-Pattern-MCP

Provides design pattern templates and anti-pattern guidance to AI coding agents for correct pattern implementation.

Multi Agent Orchestrator MCP

Coordinates specialized agents (Architecture, Quality, Cloud, Prompt) to plan, build, test, and deploy applications with self-healing capabilities, authentication, and analytics for autonomous software engineering workflows.

Real Browser MCP

Provides a real browser that bypasses bot detection (Cloudflare, Turnstile) for AI agents, enabling navigation, clicking, typing, screenshots, and data collection through MCP tools.

Couchbase MCP Server

Enables AI assistants to connect to Couchbase clusters locally or via SSH tunnels, providing tools for cluster management, key-value operations, N1QL queries, and index recommendations with safety features like read-only mode and write confirmations.

test-lnar-hosting-python-postgres

Enables CRUD operations on notes stored in PostgreSQL, with tools to list, create, update, delete notes and tags. The server provides both REST API and MCP endpoints via a single FastAPI process.

ServiceNow MCP Server

Enables Claude to interact with ServiceNow instances for incident management, knowledge base search, and JWT authentication.

gcal-mcp

An MCP server for managing Google Calendar and Tasks with energy-aware scheduling and priority-based task management. It enables natural language interactions for creating flight or lodging events, managing reading queues, and optimizing daily schedules.

Gemini MCP Server

Connects any MCP client to the Google Gemini API, providing tools for text generation, chat, vision analysis, embeddings, token counting, and model listing.

FotMoCP

Enables MCP clients to access live football data from FotMob, including match stats, team form, injuries, and player workload, without making predictions.

IBKR MCP

MCP server for Interactive Brokers via IB Gateway, enabling read access to account data and trading capabilities for paper accounts.

MyContext MCP Server

Enables personal project documentation management through local markdown files stored in nested directories. Supports organizing context by project and layer (backend/frontend/fullstack) with search functionality across all documentation files.

agent-commerce-guard

Provides a read-only approval gate for AI agent commerce actions, reviewing up to five non-sensitive actions and returning decisions and required evidence without executing, paying, or signing.

gsc-mcp

Provides read-only access to Google Search Console data, including search analytics, sitemap status, and URL inspection, for MCP clients like Claude.

thunderbit-web-scraping-mcp

Thunderbit MCP lets AI agents search, scrape, and extract structured data from any website.

ReadyTrader-Stocks

Enables AI agents to execute stock trading operations with built-in risk controls and human approval workflows. Supports paper trading simulation, real brokerage integration (Alpaca, Tradier), backtesting, sentiment analysis, and portfolio management while maintaining strict separation between AI intelligence and trade execution.

Autopilot Browser MCP Server

Enables AI agents to search for and execute automated browser workflows through Autopilot Browser's API, allowing web scraping, data extraction, and other browser automation tasks via natural language commands.