发现优秀的 MCP 服务器

dataville-mcp

Enables MCP clients to query Dataville's data sources using tools like list_dataville_sources and search_dataville.

gtm-mcp-server

A Model Context Protocol server for Google Tag Manager that uses service account authentication, providing tools to manage accounts, containers, workspaces, tags, triggers, variables, and versions.

vmd-hydrate-mcp

Drive VMD from any LLM — render GROMACS/LAMMPS trajectories and analyze clathrate-hydrate cages through the Model Context Protocol.

Alpacon MCP Server

Enables AI assistants to securely access and manage servers via Alpacon's zero-trust platform, allowing natural language command execution, file transfer, and monitoring without SSH keys or VPNs.

claude-consult-mcp

Enables OpenAI Codex to consult Claude Code for co-analysis, adversarial second opinions, and read-only file review over the Model Context Protocol, with Claude remaining advisory and unable to modify files.

NVIDIA NIM MCP Server

Enables local task execution using the user's NVIDIA NIM API quota, with tools to process file contents through LLMs, send chat prompts, and list available models.

elementor-cli

CLI and MCP server for reading and writing Elementor page data on WordPress.

RPG Maker MCP

A content-blind MCP server for RPG Maker MZ that enables applying, validating, diffing, and managing event structures from EventSpec files without accessing sensitive story text or images.

mcp-crewd-rs

Enables AI agents to spawn, coordinate, and message worker cells via a single MCP server over a local Unix socket.

bazi

Bazi MCP is an AI based Bazi calculator that provides accurate Bazi chart data for personality analysis and destiny prediction.

intacct-docs

Enables AI assistants to search, browse, and generate example payloads from Sage Intacct REST API documentation using OpenAPI specs.

Etherscan MCP Python

Provides comprehensive access to Ethereum blockchain data through the Etherscan API, including account, block, contract, transaction, token, gas, statistics, logs, and RPC proxy tools.

flowindex

Enables AI coding agents to analyze codebase behavior through entrypoints, call paths, and tests, providing impact analysis and context packs for code changes.

mpc-csharp-semantickernel

Okay, here's an example demonstrating how to use Microsoft Semantic Kernel with OpenAI and a hypothetical "MCP Server" (assuming MCP stands for something like "My Custom Processing Server" or "Message Control Protocol Server"). Since "MCP Server" is vague, I'll make some assumptions about its functionality and how it might interact with Semantic Kernel. You'll need to adapt this to your specific MCP Server's capabilities. **Conceptual Overview** The core idea is to use Semantic Kernel to orchestrate interactions between OpenAI (for language understanding and generation) and your MCP Server (for specialized processing, data retrieval, or control actions). **Assumptions about the MCP Server** * **API Endpoint:** It exposes an API endpoint (e.g., REST API) for receiving requests and sending responses. * **Functionality:** Let's assume it can perform a specific task, like: * **Data Lookup:** Retrieve information from a database based on a query. * **System Control:** Execute a command on a system. * **Message Routing:** Route a message to a specific destination. * **Input/Output:** It expects structured input (e.g., JSON) and returns structured output (e.g., JSON). **Example Scenario: Smart Home Control** Let's imagine an MCP Server that controls smart home devices. We want to use Semantic Kernel and OpenAI to allow users to control their home with natural language. **Code Example (C#)** ```csharp using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Connectors.OpenAI; using System.Net.Http; using System.Text; using System.Text.Json; using System.Threading.Tasks; public class SmartHomePlugin { private readonly HttpClient _httpClient; private readonly string _mcpServerEndpoint; public SmartHomePlugin(string mcpServerEndpoint) { _httpClient = new HttpClient(); _mcpServerEndpoint = mcpServerEndpoint; } [KernelFunction, Description("Controls a smart home device.")] public async Task<string> ControlDevice( [Description("The device to control (e.g., lights, thermostat).")] string device, [Description("The action to perform (e.g., turn on, turn off, set temperature).")] string action, [Description("The value to set (e.g., 22 for temperature).")] string value = "" ) { // 1. Prepare the request to the MCP Server var requestData = new { device = device, action = action, value = value }; string jsonRequest = JsonSerializer.Serialize(requestData); var content = new StringContent(jsonRequest, Encoding.UTF8, "application/json"); // 2. Send the request to the MCP Server HttpResponseMessage response = await _httpClient.PostAsync(_mcpServerEndpoint, content); // 3. Handle the response from the MCP Server if (response.IsSuccessStatusCode) { string jsonResponse = await response.Content.ReadAsStringAsync(); // Deserialize the JSON response (assuming MCP Server returns JSON) try { var responseObject = JsonSerializer.Deserialize<Dictionary<string, string>>(jsonResponse); return responseObject?["status"] ?? "Unknown status"; // Assuming MCP returns a "status" field } catch (JsonException ex) { Console.WriteLine($"Error deserializing MCP Server response: {ex.Message}"); return "Error processing MCP Server response."; } } else { Console.WriteLine($"MCP Server request failed: {response.StatusCode}"); return $"MCP Server request failed with status code: {response.StatusCode}"; } } } public class Example { public static async Task Main() { // 1. Configure Semantic Kernel string apiKey = "YOUR_OPENAI_API_KEY"; string orgId = "YOUR_OPENAI_ORG_ID"; // Optional Kernel kernel = Kernel.CreateBuilder() .AddOpenAIChatCompletion("gpt-3.5-turbo", apiKey, orgId) // Or "gpt-4" .Build(); // 2. Define the MCP Server endpoint string mcpServerEndpoint = "http://your-mcp-server.com/api/control"; // Replace with your actual endpoint // 3. Import the SmartHomePlugin var smartHomePlugin = new SmartHomePlugin(mcpServerEndpoint); kernel.ImportPluginFromObject(smartHomePlugin, "SmartHome"); // 4. Create a Semantic Function (Prompt) string prompt = @" Control the smart home device. Device: {{$device}} Action: {{$action}} Value: {{$value}} {{SmartHome.ControlDevice $device $action $value}} "; var smartHomeFunction = kernel.CreateFunction(prompt); // 5. Run the Semantic Function with user input var arguments = new KernelArguments { ["device"] = "lights", ["action"] = "turn on", ["value"] = "" }; var result = await smartHomeFunction.InvokeAsync(kernel, arguments); Console.WriteLine($"Result: {result.GetValue<string>()}"); // Example 2: More natural language input using OpenAI to extract parameters string naturalLanguagePrompt = "Turn on the living room lights."; // Define a prompt to extract device, action, and value from the natural language input string extractionPrompt = @" Extract the device, action, and value from the following text: Text: {{$text}} Device: Action: Value: "; var extractionFunction = kernel.CreateFunction(extractionPrompt); var extractionResult = await extractionFunction.InvokeAsync(kernel, new KernelArguments { ["text"] = naturalLanguagePrompt }); string extractedText = extractionResult.GetValue<string>()!; // Parse the extracted text (this is a simplified example; you might need more robust parsing) string extractedDevice = extractedText.Split("Device:")[1].Split("Action:")[0].Trim(); string extractedAction = extractedText.Split("Action:")[1].Split("Value:")[0].Trim(); string extractedValue = extractedText.Split("Value:")[1].Trim(); Console.WriteLine($"Extracted Device: {extractedDevice}"); Console.WriteLine($"Extracted Action: {extractedAction}"); Console.WriteLine($"Extracted Value: {extractedValue}"); // Now use the extracted parameters with the SmartHome.ControlDevice function var controlArguments = new KernelArguments { ["device"] = extractedDevice, ["action"] = extractedAction, ["value"] = extractedValue }; var controlResult = await smartHomeFunction.InvokeAsync(kernel, controlArguments); Console.WriteLine($"Control Result: {controlResult.GetValue<string>()}"); } } ``` **Explanation:** 1. **`SmartHomePlugin`:** * This class represents a Semantic Kernel plugin that interacts with the MCP Server. * It takes the MCP Server endpoint as a constructor parameter. * The `ControlDevice` function is decorated with `[KernelFunction]` to make it available to Semantic Kernel. * It constructs a JSON request based on the input parameters (`device`, `action`, `value`). * It sends a POST request to the MCP Server. * It handles the response from the MCP Server, deserializing the JSON and returning a status message. Error handling is included. 2. **`Example.Main`:** * **Configure Semantic Kernel:** Sets up the Semantic Kernel with your OpenAI API key and organization ID. * **Define MCP Server Endpoint:** Replace `"http://your-mcp-server.com/api/control"` with the actual URL of your MCP Server's API endpoint. * **Import Plugin:** Creates an instance of the `SmartHomePlugin` and imports it into the Semantic Kernel. This makes the `ControlDevice` function available for use in prompts. * **Create Semantic Function (Prompt):** Defines a prompt that uses the `SmartHome.ControlDevice` function. The prompt takes `device`, `action`, and `value` as input parameters. * **Run Semantic Function:** Creates a `KernelArguments` object with the desired device, action, and value, and then invokes the semantic function. The result from the MCP Server is printed to the console. * **Natural Language Example:** Demonstrates how to use OpenAI to extract the device, action, and value from a natural language prompt. This allows users to control their smart home with more natural commands. A separate prompt is used for extraction. The extracted parameters are then used to call the `SmartHome.ControlDevice` function. **Key Points and Considerations:** * **MCP Server API:** The most important part is understanding the API of your MCP Server. You need to know the endpoint, the expected request format (JSON schema), and the format of the response. * **Error Handling:** The example includes basic error handling for network requests and JSON deserialization. You should add more robust error handling for production code. * **Security:** If your MCP Server requires authentication, you'll need to add authentication headers to the `HttpClient` requests. Never hardcode sensitive information like API keys directly in your code. Use environment variables or a secure configuration mechanism. * **Prompt Engineering:** The prompts are crucial for getting the desired behavior. Experiment with different prompts to improve the accuracy and reliability of the system. Consider using techniques like few-shot learning to provide examples to the language model. * **JSON Serialization/Deserialization:** The example uses `System.Text.Json`. You can use other JSON libraries like Newtonsoft.Json if you prefer. * **Dependency Injection:** For larger applications, consider using dependency injection to manage the `HttpClient` and other dependencies. * **Asynchronous Operations:** The example uses `async` and `await` for asynchronous operations. This is important for avoiding blocking the main thread and improving performance. * **Parameter Extraction:** The natural language example uses a simple string splitting approach to extract parameters. For more complex scenarios, you might need to use more sophisticated techniques like regular expressions or a dedicated natural language processing library. Semantic Kernel also offers more advanced techniques for parameter extraction. * **Semantic Kernel Plugins:** Consider breaking down your MCP Server functionality into multiple Semantic Kernel plugins for better organization and reusability. * **Testing:** Write unit tests to verify the functionality of your Semantic Kernel plugins and the interactions with the MCP Server. **How to Adapt This Example:** 1. **Replace Placeholders:** Replace `"YOUR_OPENAI_API_KEY"`, `"YOUR_OPENAI_ORG_ID"`, and `"http://your-mcp-server.com/api/control"` with your actual values. 2. **Implement MCP Server Interaction:** Modify the `SmartHomePlugin` to match the API of your MCP Server. Adjust the request format, response handling, and error handling accordingly. 3. **Customize Prompts:** Adjust the prompts to match the specific tasks you want to perform. 4. **Add Error Handling:** Implement more robust error handling to handle potential issues with the MCP Server or the OpenAI API. 5. **Add Security:** Implement appropriate security measures to protect your API keys and other sensitive information. **Chinese Translation of Key Concepts:** * **Microsoft Semantic Kernel:** 微软语义内核 (Wēiruǎn yǔyì kènèi) * **OpenAI:** 开放人工智能 (Kāifàng réngōng zhìnéng) * **MCP Server:** (You'll need to translate this based on what MCP stands for in your context. For example, if it's "My Custom Processing Server," you could translate it as: 我的自定义处理服务器 (Wǒ de zì dìngyì chǔlǐ fúwùqì)) * **Plugin:** 插件 (Chājiàn) * **Kernel Function:** 内核函数 (Nèihé hánshù) * **Prompt:** 提示 (Tíshì) * **Semantic Function:** 语义函数 (Yǔyì hánshù) * **API Endpoint:** 应用程序接口端点 (Yìngyòng chéngxù jiēkǒu duāndiǎn) * **Natural Language:** 自然语言 (Zìrán yǔyán) This comprehensive example should give you a solid foundation for using Microsoft Semantic Kernel with OpenAI and your MCP Server. Remember to adapt the code to your specific needs and to thoroughly test your implementation. Good luck!

Shade MCP

Shade MCP connects AI assistants to the creative assets in your Shade workspace. It can search and browse drives, inspect assets and transcripts, manage files and metadata, curate collections and views, add comments, create share links, work with custom objects, and manage automations.

H-ear MCP Server

Enables Claude to classify audio across 7,000+ sound classes, including bird species and urban noise, with tools for batch processing, webhooks, and usage tracking.

StatusCraft

Live operational status for 2,400+ major software services — AWS, GitHub, Stripe, OpenAI, Cloudflare, and more — pulled from each provider's official status page and returned as a normalised up / degraded / down result for any service you ask about.

taskboard-mcp

An MCP server that integrates a Kanban task board into a sandboxed agent environment, with tools for task management and deterministic state snapshots for agent evaluation.

llm-wiki-mcp

Serves an LLM-maintained markdown wiki to agents over MCP, providing tools for querying and navigating wiki pages without direct filesystem access.

testmcpgithubdemo1

从 MCP 服务器演示创建。

voice-mcp

An MCP server for AI voice synthesis with an inline audio player, allowing users to give their AI assistant a custom cloned voice using DashScope or ElevenLabs.

Mycelium

An on-call agent for data incidents, built on a bio-inspired context protocol. It triages data quality and freshness issues using DataHub, computing severity, blast radius, and ownership from the catalogue before generating prose.

Telegram MCP Server

MCP服务器向Telegram发送通知

Gavelin

Search bills and speaker-attributed hearing transcripts across all 50 US state legislatures.

Huly MCP

MCP server for Huly (task tracker) integration

Tensorfire

MCP server for security-testing AI models, exposing garak vulnerability scanning and URL/MCP-endpoint scanning as callable tools. It lets agents probe LLM endpoints for jailbreaks, prompt injection, and data leakage, and classify URLs or screen remote MCP endpoints for prompt-injection payloads.

agent-memory

A persistent, trust-scored project memory for AI coding agents, backed by PostgreSQL + pgvector, providing durable memory of architecture decisions, bug patterns, and coding conventions.

lightrag-mcp

Bridges AI assistants with LightRAG's knowledge graph capabilities, enabling document management and multi-mode queries through the Model Context Protocol.

GeekyGramps-MCP

A shared memory gateway for AI coding agents using PostgreSQL, enabling collaborative context across agents.