Discover Awesome MCP Servers

Discord Agent MCP

Provides comprehensive Discord bot automation with 71 tools for messaging, channel management, moderation, roles, members, emojis, stickers, scheduled events, auto-moderation, and application commands through AI assistants.

crypto-powerdata-mcp

A comprehensive MCP service for cryptocurrency data acquisition with advanced technical analysis capabilities, supporting 100+ exchanges, 158 TA-Lib indicators, and dual transport protocols (stdio and HTTP/SSE).

Markdownify MCP Server

A Model Context Protocol server that converts diverse file types, including PDFs, images, audio, and Office documents, into Markdown format. It also transforms web content like YouTube transcripts and Bing search results into readable text for model consumption.

MCP Uplink

A CLI and cloud platform providing managed hosting for MCP servers with intelligent tool filtering to reduce token costs and improve LLM accuracy. It serves as a secure proxy that connects AI clients to cloud tools while keeping sensitive credentials on the local machine.

personal-health-mcp

Self-hosted MCP server that aggregates personal health data from Google Health, Oura, and Withings into a single, provider-attributed interface with configurable source of truth preferences.

WordPress Plugin Directory MCP Server

Enables searching, downloading, extracting, and comparing WordPress.org plugins with local plugins via natural language.

SmartSuite MCP Server

A locally-hosted MCP server enabling AI agents to securely interact with SmartSuite data, with configurable access modes and audit logging.

MCP Hub Documentation

MCP Hub es un marco de trabajo integral para construir, gestionar y desplegar clientes y servidores del Protocolo de Contexto de Modelos (MCP). Proporciona herramientas y configuraciones para permitir una integración y ejecución fluidas de flujos de trabajo MCP de extremo a extremo.

PayPal MCP Server by CData

This read-only MCP Server allows you to connect to PayPal data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

kingbase-mcp-server

MCP server for KingBase (PostgreSQL-compatible) databases, enabling AI assistants to query and manage database schemas, tables, and data with configurable access control.

NikolaNddTesla/ssh-mcp-server

SSH MCP Server - AI manages remote servers via SSH. Multi-server, SFTP zero-token transfer, SOCKS5 proxy, jump host, 2FA, async transfer with progress.

MCP TensorBoard

Exposes TensorBoard experiment data through a standardized MCP API, enabling AI coding agents to query and analyze scalars, tensors, histograms, distributions, and images from ML experiment logs.

Solodit MCP Server

A server that allows searching and retrieving Solodit vulnerability reports through Model Context Protocol (MCP).

macOS Notification MCP

macOS Notification MCP

cep-tools

An MCP server that provides a tool to query Brazilian postal codes (CEPs) via the ViaCEP API, returning address details such as street, neighborhood, city, and state.

MCP Action Firewall

A transparent proxy that intercepts high-risk tool calls and requires OTP-based human approval before they can be executed. It acts as a configurable circuit breaker between AI agents and target MCP servers to prevent unauthorized or dangerous actions.

google-workspace-mcp

A multi-account Google Workspace MCP server that drives Gmail, Google Calendar, and Google Drive across any number of Google accounts in parallel from one server.

meta-mcp

MCP server for Instagram Graph API, Threads API & Meta platform — posting, insights, comments, messaging

MCP Outlook - Microsoft Outlook Integration for Claude

MCP Server and CLI for Microsoft Outlook integration

Related Identity MCP Server

Discovers related blockchain addresses and domain names for web3 identities across different platforms including Ethereum, Farcaster, Lens, and ENS using next.id's relation server data.

Qontinui MCP Server

Enables AI-driven visual automation by connecting to Qontinui Runner to load workflow configurations, execute visual automation workflows, and monitor execution status across multiple displays.

codeledgerECF/codeledger

Deterministic context selection for AI coding agents. Scores and selects the minimal file set for each task, then records outcomes into a local ledger that compounds into reusable patterns.

Spring Ai Mcp Deepseek

Okay, I understand. You want to know how to integrate Spring AI with an MCP (Model Control Plane) service, specifically using an MCP server and a DeepSeek client. Here's a breakdown of the concepts and a general approach to achieve this, along with considerations and potential code snippets (though a complete, runnable example would be quite extensive): **Understanding the Components** * **Spring AI:** A Spring project that provides abstractions and tools for working with AI models, including large language models (LLMs). It simplifies tasks like prompting, model invocation, and result handling. * **MCP (Model Control Plane):** A system that manages the lifecycle of AI models. This includes: * **Model Registry:** Storing model metadata (name, version, location, capabilities). * **Model Deployment:** Deploying models to serving infrastructure. * **Model Monitoring:** Tracking model performance and health. * **Model Governance:** Enforcing policies around model usage. * **MCP Server:** The central component of the MCP, responsible for managing models and providing an API for clients to interact with them. It likely exposes endpoints for: * Listing available models. * Retrieving model metadata. * Invoking models. * **DeepSeek Client:** A client library or API that allows you to interact with DeepSeek models. DeepSeek is a specific LLM provider (like OpenAI, Google, etc.). This client handles authentication, request formatting, and response parsing for the DeepSeek API. **General Approach** The core idea is to create a Spring AI `ChatClient` (or `EmbeddingClient` if you're working with embeddings) that delegates the actual model invocation to the MCP server, which in turn uses the DeepSeek client. Here's a step-by-step outline: 1. **Set up your Spring Boot project:** * Create a new Spring Boot project using Spring Initializr ([https://start.spring.io/](https://start.spring.io/)). * Include the necessary dependencies: * `spring-boot-starter-web`: For building REST APIs. * `spring-ai-core`: The core Spring AI library. * Potentially other Spring AI modules depending on your needs (e.g., `spring-ai-openai` if you want to compare with a direct OpenAI integration). * A suitable HTTP client library (e.g., `spring-boot-starter-webflux` for reactive HTTP or `spring-boot-starter-web` with `RestTemplate`). * Any dependencies required by the DeepSeek client library (if it's a separate library). 2. **Configure the DeepSeek Client:** * Obtain API keys or credentials for DeepSeek. * If the DeepSeek client is a separate library, configure it according to its documentation. This might involve setting API keys, base URLs, and other parameters. 3. **Create a Spring AI `ChatClient` Implementation:** * Create a class that implements the `ChatClient` interface from Spring AI. This is the key part where you bridge Spring AI to your MCP. * **Dependency Injection:** Inject the DeepSeek client (or a service that uses the DeepSeek client) into your `ChatClient` implementation. * **`generate()` Method:** Implement the `generate()` method of the `ChatClient` interface. This method takes a `Prompt` object as input and returns a `ChatResponse`. Inside this method: * Extract the text from the `Prompt`. * Format the request according to the MCP server's API. This might involve creating a JSON payload with the prompt text and any other required parameters. * Send the request to the MCP server's endpoint for model invocation using your HTTP client. * Parse the response from the MCP server. The response should contain the generated text from the DeepSeek model. * Create a `ChatResponse` object from the parsed response and return it. 4. **Configure the MCP Server Interaction:** * **API Endpoint:** Determine the correct API endpoint on the MCP server for invoking the DeepSeek model. * **Authentication:** If the MCP server requires authentication, configure your HTTP client to include the necessary headers (e.g., API key, JWT token). * **Request/Response Format:** Understand the expected request format (e.g., JSON) and the format of the response containing the generated text. 5. **Register the `ChatClient` in Spring:** * Create a Spring configuration class. * Use the `@Bean` annotation to register your custom `ChatClient` implementation as a Spring bean. This makes it available for injection into other components. 6. **Use the `ChatClient` in your application:** * Inject the `ChatClient` into your Spring components (e.g., controllers, services). * Use the `generate()` method to send prompts to the DeepSeek model via the MCP server. **Example Code Snippets (Illustrative)** ```java import org.springframework.ai.chat.ChatClient; import org.springframework.ai.chat.ChatResponse; import org.springframework.ai.chat.prompt.Prompt; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.beans.factory.annotation.Value; import org.springframework.http.HttpEntity; import org.springframework.http.HttpHeaders; import org.springframework.http.MediaType; import org.springframework.stereotype.Component; import org.springframework.web.client.RestTemplate; import java.util.List; import java.util.Map; @Component public class McpDeepSeekChatClient implements ChatClient { @Value("${mcp.server.url}") private String mcpServerUrl; @Value("${mcp.server.api.key}") private String mcpServerApiKey; @Autowired private RestTemplate restTemplate; // Or WebClient for reactive @Override public ChatResponse generate(Prompt prompt) { String promptText = prompt.getContent(); // 1. Prepare the request to the MCP server String mcpEndpoint = mcpServerUrl + "/deepseek/generate"; // Example endpoint HttpHeaders headers = new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set("X-API-Key", mcpServerApiKey); // Example authentication Map<String, String> requestBody = Map.of("prompt", promptText); HttpEntity<Map<String, String>> requestEntity = new HttpEntity<>(requestBody, headers); // 2. Send the request to the MCP server Map response = restTemplate.postForObject(mcpEndpoint, requestEntity, Map.class); // 3. Parse the response from the MCP server String generatedText = (String) response.get("generated_text"); // Adjust based on MCP response format // 4. Create a ChatResponse object ChatResponse chatResponse = new ChatResponse(List.of(new org.springframework.ai.chat.Generation(generatedText))); return chatResponse; } } ``` ```java import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.web.client.RestTemplate; @Configuration public class AppConfig { @Bean public RestTemplate restTemplate() { return new RestTemplate(); } // Register the custom ChatClient @Bean public McpDeepSeekChatClient mcpDeepSeekChatClient() { return new McpDeepSeekChatClient(); } } ``` ```java import org.springframework.ai.chat.ChatClient; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RestController; import org.springframework.ai.chat.prompt.PromptTemplate; import org.springframework.ai.chat.prompt.Prompt; import java.util.Map; @RestController public class ChatController { @Autowired private ChatClient chatClient; @PostMapping("/chat") public String chat(@RequestBody Map<String, String> request) { String userMessage = request.get("message"); PromptTemplate promptTemplate = new PromptTemplate("User message: {message}"); Prompt prompt = promptTemplate.create(Map.of("message", userMessage)); return chatClient.generate(prompt).getReply().getContent(); } } ``` **Important Considerations** * **Error Handling:** Implement robust error handling to catch exceptions during API calls, response parsing, and other operations. * **Asynchronous Communication:** Consider using asynchronous communication (e.g., `WebClient` in Spring WebFlux) for better performance, especially if the MCP server or DeepSeek API has high latency. * **Rate Limiting:** Be aware of rate limits imposed by the MCP server and the DeepSeek API. Implement appropriate retry mechanisms and backoff strategies. * **Security:** Protect your API keys and credentials. Use environment variables or a secure configuration management system to store them. Consider using HTTPS for all communication. * **MCP Server Implementation:** The specific details of interacting with the MCP server will depend on its implementation. You'll need to consult its documentation to understand the API endpoints, request/response formats, and authentication mechanisms. * **DeepSeek Client Library:** If a dedicated DeepSeek client library exists, use it to simplify the interaction with the DeepSeek API. Otherwise, you might need to build your own client using HTTP libraries. * **Model Selection:** The MCP server might allow you to select different DeepSeek models. Make sure to include the model selection parameter in your request to the MCP server if necessary. * **Streaming:** If the DeepSeek API supports streaming responses, consider implementing streaming in your `ChatClient` to provide a more interactive user experience. Spring AI has support for streaming. * **Testing:** Write unit tests and integration tests to verify that your `ChatClient` implementation is working correctly. **In summary, integrating Spring AI with an MCP service involves creating a custom `ChatClient` that acts as a bridge between Spring AI's abstractions and the MCP server's API. You'll need to handle request formatting, response parsing, authentication, and error handling. The specific details will depend on the MCP server's implementation and the DeepSeek client library you're using.** This detailed explanation should give you a solid foundation for building your integration. Remember to consult the documentation for Spring AI, your MCP server, and the DeepSeek API for the most accurate and up-to-date information. Good luck!

AMM

Enables automatic memory retrieval and injection for AI conversations to provide continuous learning through semantic search and memory management.

COMSOL MCP Server

Enables AI agents to automate multiphysics simulations in COMSOL Multiphysics, covering model management, geometry building, physics configuration, and results visualization. It supports complex simulation workflows through the MCP protocol and includes integrated knowledge retrieval for documentation and troubleshooting.

modelforge

Financial model factory MCP server: turns a spec into a live-formula Excel workbook. 14 templates (LBO, DCF, M\&A, IPO, restructuring, project finance, NPL, structured credit, 3-statement) with every cell formulated and every number source-traced to its document page.

Connhex MCP Server

Enables natural language interaction with Connhex IoT platform APIs for managing devices, telemetry, rules, and resources via MCP tools.

GOAT MCP Server

Un servidor MCP que conecta Claude para Escritorio con funcionalidad blockchain, permitiendo a los usuarios consultar saldos y enviar tokens en cadenas EVM y Solana a través de interacciones en lenguaje natural.