发现优秀的 MCP 服务器

Supply Chain Disruption Monitor

MCP server that exposes tools for monitoring supply chain disruptions, including vessel positions, port weather, congestion, and news. Includes an AI agent that synthesizes these sources to assess route risks.

MCP Database Server

Enables AI assistants to interact with PostgreSQL databases through query execution and schema inspection, supporting multiple schemas for customer data, document management, loan systems, and asset leasing.

nonprofit-explorer-mcp-server

Search and explore 1.8M+ US nonprofits, fetch Form 990 financials, and access IRS filing history via MCP.

cro_ro_cadastro_mcp

MCP server to query official dental council registration data for Rondônia, Brazil, via a pay-per-use hosted API.

蓝湖 Design Schema MCP

A read-only MCP server for reading and analyzing Lanhu (Blue Lake) design projects, providing tools to retrieve design lists, previews, node trees, schemas, and exportable resources.

md-redline

Inline review comments for markdown specs and design docs. Agents request human review mid-task via MCP and pause until you send feedback.

battery-mcp

Enables LLMs to verify the novelty and validity of invented battery materials, with support for searching and validating against a materials database.

Garage-on-the-Go MCP Server

Provides tools for vehicle service search, mechanic matching, and booking generation as part of a multi-agent AI system for roadside assistance.

artifactory-mcp

Enables AI assistants to query Maven artifacts in JFrog Artifactory by reusing credentials from ~/.m2/settings.xml and providing tools like getting latest stable versions, listing artifacts, and checking dependency updates.

Bazel MCP Server

镜子 (jìng zi)

Rvvel Affiliate Links MCP Server

Central hub for managing affiliate links across all Rvvel applications and AI agents, enabling storage, retrieval, search, and tracking.

AskOnSlackMCP

A Human-in-the-Loop MCP server that enables AI assistants to request information or clarification from humans via Slack. It uses real-time, thread-based conversations with socket mode integration to bridge the gap between AI systems and human experts.

Shopmonkey MCP Server

Enables AI agents to interact with the Shopmonkey REST API to manage shop management data including work orders, customers, vehicles, and inventory. It provides 33 tools across 9 resource groups with built-in support for rate limiting, concurrency control, and multi-location management.

Toolkits MCP

A collection of image processing tools that enables users to optimize images, remove backgrounds using AI, and generate favicons from logo files locally.

ERP MCP Server

It enables LLMs to query enterprise data such as sales orders, stock levels, and vendor information through structured MCP-compliant tools, using localized JSON databases for demonstration.

open-museum-mcp

Federated, license-verified search across open-access museum collections — currently The Met, Cleveland, AIC, Wikimedia Commons, and Europeana, with more being added. Strict-default-deny rights gate accepts only CC0 / Public Domain Mark, returning reuse-safe artwork with citations in three styles.

MCP Server Template (

这个模板可以帮助你基于推荐实践快速启动一个新的模型上下文协议（MCP）服务器项目。

ToolRoute

Intelligent routing layer for AI agents — recommends the best MCP server and LLM for any task, scored on 132+ real benchmark executions.

agent-cost-allocator-mcp

Multi-tenant LLM cost attribution for chargeback billing.

mcp-postman-runner

Run the requests in a Postman collection folder and get structured, assertion-level results back straight from your AI assistant.

Portfolio MCP Server

An MCP server that exposes an AI project portfolio as queryable tools, allowing users to ask about projects, search by technology, or get details via natural language.

MCP TypeScript Template

A starter template for building remote Model Context Protocol servers using TypeScript, providing modern tooling and best practices while leveraging the MCP TypeScript SDK.

systemd-mcp

Provides AI assistants with safe, read-only access to Linux systemd services, including status monitoring, log querying, and dependency analysis, with optional granular permissions for service management actions.

nexacorp-mcp-agent

A Model Context Protocol server that exposes a hybrid RAG pipeline (dense+sparse retrieval with reranking) for querying an enterprise knowledge base, enabling autonomous agents to search and retrieve relevant information.

Angular Toolkit MCP

A Model Context Protocol server that provides Angular project analysis and refactoring capabilities, enabling LLMs to analyze component usage patterns, dependency structures, and perform safe refactoring with breaking change detection.

Halo ITSM MCP Server

Enables AI-driven IT service management by exposing the full Halo ITSM REST API through 172 tools across 43 resource domains to any MCP-compatible client.

depsonar

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

Geond Agent Protocol

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

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.

@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.