Discover Awesome MCP Servers

Jenkins MCP Server

Enables AI coding tools to interact with Jenkins CI instances behind Cloudflare Access, supporting job inspection, build logs, test results, and artifact retrieval.

Yandex MCP Server

Enables interaction with Yandex advertising and analytics APIs (Direct, Metrika, Audience, Webmaster, AdMetrica) through MCP tools, resources, and prompts for campaign management and data retrieval.

aily-local-file-mcp

Exposes local file system operations to AI assistants via Streamable HTTP with security features like auth, path whitelisting, and audit logging.

Sitrep

Real-time parallel development coordination for AI agents and humans.

Resemble AI Voice Generation MCP Server

ClaudeとCursorにModel Context Protocolを使って統合し、Resemble AIの音声を使ってテキストから音声オーディオを生成します。

MetaMCP

Aggregates multiple MCP servers into a single interface, reducing token overhead and simplifying tool management for LLMs.

grok-build-mcp

MCP server that provides a single run_grok tool to execute Grok build agent tasks in a specified working directory, automatically reusing sessions and returning results with logs.

phishunt-mcp

Provides MCP tools to query the phishunt.io phishing feed, enabling domain checks, brand-specific phishing lists, delta syncs, and phishing-signal analysis.

EdgeOne Pages MCP Server

A self-hosted MCP server that enables AI assistants to deploy and manage static websites directly on EdgeOne Pages platform with KV storage support.

MCP Server

A centralized architecture that serves as the only data access layer between frontend/backend applications and Supabase/Redis, enforcing strict data access control while maintaining simplicity and efficiency.

MCP Home Assistant

Enables natural language control of Home Assistant smart home devices through Cursor AI, supporting entity queries, automation management, configuration file editing, and system operations.

ollqd

Enables indexing and semantic search of codebases and documents via MCP, using Ollama embeddings and Qdrant vector store.

Multilead Open API MCP Server

Enables AI assistants to interact with the Multilead platform for lead management, email campaigns, conversations, webhooks, and analytics through 74 API endpoints.

CBCTScope MCP Server

Enables AI agents to control a local CBCT viewer for dental and maxillofacial scans, with navigation verbs like open scan, set window, navigate slices, and snapshot, without executing code or returning interpretations.

Task Master

Simplified Trello-style task management via MCP with 11 essential tools for efficient CRUD operations and a real-time web dashboard.

VaultPDF MCP

A local PDF manipulation server for AI agents that provides tools for merging, splitting, extracting info, and converting images to PDF, all without uploading files.

travel-planner

Enables travel planning through natural language, providing weather forecasts, attraction search, itinerary generation, and distance calculations using free APIs.

Onde Inference MCP Server

Enables managing Onde Inference accounts and model catalog operations through MCP tools such as login, app management, model registration, and assignment. Returns structured JSON over stdio for use with any MCP client.

Gemini Code Context MCP

Provides persistent codebase context caching via Gemini API, enabling fast, cost-effective repeat queries for MCP hosts like Claude Code.

Argo Workflow MCP Server

Enables AI agents to manage Argo Workflows through REST API, supporting workflow template and instance operations including creation, submission, monitoring, and deletion with token authentication.

Azure Model Context Protocol (MCP) Hub

Okay, here's a breakdown of resources, tools, and samples for building and integrating Model Context Protocol (MCP) servers on Azure using multiple languages. Keep in mind that MCP is relatively new, so the ecosystem is still developing. I'll focus on what's available and how to adapt existing Azure resources. **Understanding the Model Context Protocol (MCP)** Before diving into specific languages, let's clarify what MCP is and its purpose. MCP is designed to provide contextual information to models, enabling them to perform tasks more effectively. This context can include user data, environment information, or other relevant details. The key is that the model *requests* this context from an MCP server. **General Azure Resources Relevant to MCP Server Development** Regardless of the language you choose, these Azure services will likely be involved: * **Azure Functions:** A serverless compute service that allows you to run code without managing servers. Excellent for building lightweight MCP servers. * **Azure App Service:** A fully managed platform for building, deploying, and scaling web apps. Suitable for more complex MCP servers. * **Azure Kubernetes Service (AKS):** For containerized deployments, providing scalability and orchestration. Useful for complex, high-demand MCP servers. * **Azure API Management:** A gateway to expose your MCP server as an API, providing security, rate limiting, and monitoring. * **Azure Cosmos DB:** A NoSQL database for storing context data. * **Azure SQL Database:** A relational database for storing context data. * **Azure Key Vault:** For securely storing secrets, API keys, and connection strings. * **Azure Monitor:** For logging and monitoring your MCP server's performance and health. **Language-Specific Resources and Samples** Here's a breakdown by language, focusing on how to adapt existing Azure resources to MCP: **1. Python** * **Azure Functions with Python:** * **Documentation:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-python](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-python) * **Adaptation for MCP:** You would create an Azure Function that receives a request from the model (following the MCP specification), retrieves the relevant context data (from Cosmos DB, SQL Database, or another source), and returns the context to the model. You'll need to define the MCP request/response format in your Python code. * **Example (Conceptual):** ```python import azure.functions as func import json # Assume you have a function to retrieve context from a database from your_data_access_module import get_context_data def main(req: func.HttpRequest) -> func.HttpResponse: try: req_body = req.get_json() model_request_id = req_body.get('model_request_id') # Example MCP request parameter user_id = req_body.get('user_id') # Example MCP request parameter if not model_request_id or not user_id: return func.HttpResponse( "Please pass a model_request_id and user_id in the request body", status_code=400 ) context_data = get_context_data(user_id) # Retrieve context based on the request return func.HttpResponse( json.dumps(context_data), # Return context as JSON mimetype="application/json", status_code=200 ) except Exception as e: return func.HttpResponse( f"Error: {str(e)}", status_code=500 ) ``` * **Flask/FastAPI on Azure App Service:** * **Documentation:** [https://learn.microsoft.com/en-us/azure/app-service/quickstart-python](https://learn.microsoft.com/en-us/azure/app-service/quickstart-python) * **Adaptation for MCP:** You can build a more robust MCP server using Flask or FastAPI. This allows you to define API endpoints, handle authentication, and manage dependencies more easily. Deploy the application to Azure App Service. * **Example (Conceptual - FastAPI):** ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel import uvicorn app = FastAPI() class ContextRequest(BaseModel): model_request_id: str user_id: str # Assume you have a function to retrieve context from a database from your_data_access_module import get_context_data @app.post("/context") async def get_model_context(request: ContextRequest): try: context_data = get_context_data(request.user_id) return context_data except Exception as e: raise HTTPException(status_code=500, detail=str(e)) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8000) ``` **2. C# (.NET)** * **Azure Functions with C#:** * **Documentation:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-vs](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-vs) * **Adaptation for MCP:** Similar to Python, you'd create a C# Azure Function to handle MCP requests, retrieve context, and return it. * **Example (Conceptual):** ```csharp using Microsoft.AspNetCore.Mvc; using Microsoft.Azure.WebJobs; using Microsoft.Azure.WebJobs.Extensions.Http; using Microsoft.AspNetCore.Http; using Microsoft.Extensions.Logging; using Newtonsoft.Json; using System.Threading.Tasks; public static class GetModelContext { [FunctionName("GetModelContext")] public static async Task<IActionResult> Run( [HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req, ILogger log) { log.LogInformation("C# HTTP trigger function processed a request."); string requestBody = await new StreamReader(req.Body).ReadToEndAsync(); dynamic data = JsonConvert.DeserializeObject(requestBody); string modelRequestId = data?.model_request_id; string userId = data?.user_id; if (string.IsNullOrEmpty(modelRequestId) || string.IsNullOrEmpty(userId)) { return new BadRequestObjectResult("Please pass a model_request_id and user_id in the request body"); } // TODO: Retrieve context data based on userId (e.g., from a database) var contextData = new { UserId = userId, SomeContextualInformation = "Example Data" }; string responseJson = JsonConvert.SerializeObject(contextData); return new OkObjectResult(responseJson); } } ``` * **ASP.NET Core Web API on Azure App Service:** * **Documentation:** [https://learn.microsoft.com/en-us/aspnet/core/tutorials/first-web-api](https://learn.microsoft.com/en-us/aspnet/core/tutorials/first-web-api) * **Adaptation for MCP:** Build a RESTful API using ASP.NET Core. Define a controller that handles MCP requests, retrieves context, and returns it as JSON. Deploy to Azure App Service. **3. Java** * **Azure Functions with Java:** * **Documentation:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-java-maven](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-java-maven) * **Adaptation for MCP:** Create a Java Azure Function to handle MCP requests. * **Example (Conceptual):** ```java import com.microsoft.azure.functions.ExecutionContext; import com.microsoft.azure.functions.HttpMethod; import com.microsoft.azure.functions.HttpRequestMessage; import com.microsoft.azure.functions.HttpResponseMessage; import com.microsoft.azure.functions.HttpStatus; import java.util.Optional; import com.google.gson.Gson; /** * Azure Functions with HTTP Trigger. */ public class Function { /** * This function listens at endpoint "/api/GetModelContext". To invoke it using "curl" command in bash: * 1. Run: curl -d "{\"model_request_id\": \"123\", \"user_id\": \"456\"}" http://localhost:7071/api/GetModelContext * 2. POST method is used to pass the request body. */ @FunctionName("GetModelContext") public HttpResponseMessage run( @HttpTrigger( name = "req", methods = {HttpMethod.POST}, authLevel = AuthorizationLevel.FUNCTION) HttpRequestMessage<Optional<String>> request, final ExecutionContext context) { context.getLogger().info("Java HTTP trigger function processed a request."); // Parse request body try { final String requestBody = request.getBody().orElse(null); if (requestBody == null) { return request.createResponseBuilder(HttpStatus.BAD_REQUEST).body("Please pass a request body").build(); } Gson gson = new Gson(); ModelContextRequest mcr = gson.fromJson(requestBody, ModelContextRequest.class); if (mcr.model_request_id == null || mcr.user_id == null) { return request.createResponseBuilder(HttpStatus.BAD_REQUEST).body("Please pass a model_request_id and user_id in the request body").build(); } // TODO: Retrieve context data based on mcr.user_id (e.g., from a database) ModelContextData contextData = new ModelContextData(); contextData.userId = mcr.user_id; contextData.someContextualInformation = "Example Data"; String responseJson = gson.toJson(contextData); return request.createResponseBuilder(HttpStatus.OK).header("Content-Type", "application/json").body(responseJson).build(); } catch (Exception e) { return request.createResponseBuilder(HttpStatus.INTERNAL_SERVER_ERROR).body("Error: " + e.getMessage()).build(); } } // Inner classes to represent the request and response data static class ModelContextRequest { public String model_request_id; public String user_id; } static class ModelContextData { public String userId; public String someContextualInformation; } } ``` * **Spring Boot on Azure App Service:** * **Documentation:** [https://learn.microsoft.com/en-us/azure/developer/java/spring-framework/deploy-spring-boot-java-app-with-maven-plugin](https://learn.microsoft.com/en-us/azure/developer/java/spring-framework/deploy-spring-boot-java-app-with-maven-plugin) * **Adaptation for MCP:** Build a RESTful API using Spring Boot. **Key Considerations for MCP Server Implementation** * **MCP Specification:** The most important thing is to adhere to the MCP specification. This will define the request and response formats that your server must support. Unfortunately, a publicly available, detailed MCP specification is difficult to find. You'll likely need to work with the model provider to get the exact details. Expect it to involve JSON payloads. * **Authentication/Authorization:** Secure your MCP server. Use Azure Active Directory (Azure AD) for authentication and authorization. This ensures that only authorized models can access the context data. * **Data Access:** Choose the appropriate Azure database service (Cosmos DB, SQL Database, etc.) based on your data requirements. Implement efficient data access patterns to minimize latency. * **Performance:** Optimize your MCP server for performance. Use caching, connection pooling, and other techniques to reduce response times. Consider using Azure Content Delivery Network (CDN) for static context data. * **Monitoring and Logging:** Use Azure Monitor to track the performance and health of your MCP server. Implement comprehensive logging to help troubleshoot issues. * **Scalability:** Design your MCP server to scale to handle increasing demand. Use Azure App Service's scaling features or deploy to AKS for greater scalability. * **Error Handling:** Implement robust error handling to gracefully handle unexpected situations. Return informative error messages to the model. * **Configuration:** Use Azure App Configuration or environment variables to manage configuration settings. This allows you to easily change settings without redeploying your code. **Steps to Build and Integrate an MCP Server on Azure** 1. **Define the MCP Interface:** Work with the model provider to understand the exact request and response formats required by the MCP specification. 2. **Choose a Language and Framework:** Select a language (Python, C#, Java) and framework (Azure Functions, Flask, FastAPI, ASP.NET Core, Spring Boot) based on your team's skills and the complexity of the MCP server. 3. **Implement the MCP Server Logic:** Write the code to handle MCP requests, retrieve context data, and return it to the model. 4. **Secure the MCP Server:** Implement authentication and authorization using Azure AD. 5. **Deploy to Azure:** Deploy your MCP server to Azure App Service, Azure Functions, or AKS. 6. **Configure API Management (Optional):** Use Azure API Management to expose your MCP server as an API. 7. **Monitor and Log:** Set up Azure Monitor to track the performance and health of your MCP server. 8. **Test and Integrate:** Test the integration between your MCP server and the model. **Important Considerations and Caveats** * **Lack of a Public MCP Specification:** The biggest challenge is the lack of a publicly available, detailed MCP specification. You'll need to work closely with the model provider to get the necessary information. * **Evolving Landscape:** MCP is a relatively new concept, so the tools and resources are still evolving. Be prepared to adapt to changes. * **Model Provider Specifics:** The exact implementation details of MCP will vary depending on the model provider. **In summary,** while there isn't a single "MCP Server template" on Azure, you can leverage existing Azure services and language-specific frameworks to build a custom MCP server. The key is to understand the MCP specification provided by the model provider and implement the server logic accordingly. Remember to prioritize security, performance, and scalability. Good luck!

temporal-mcp

Provides LLM agents with a sense of time between turns via two MCP tools that track elapsed time and day rollover per conversation thread.

MCP Hub

An Express server implementation of Model Context Protocol that allows websites to connect to LLMs through streamable HTTP and stdio transports, with a built-in chat UI for testing responses.

Quant Watchtower

A read-only MCP operations console for monitoring an algorithmic trading fleet, providing heartbeat, reconciliation, alert, and audit data through six typed tools.

Primo MCP Server

MCP server for searching Ex Libris Primo library catalogues and subscribed databases, enabling search, record retrieval, autocomplete, citation generation, and export to BibTeX, RIS, or CSV.

TouchDesigner MCP Server

Enables natural language control of TouchDesigner via AI, allowing operator creation, parameter setting, Python execution, and node graph building.

zotero-mcp-lite

A lightweight and customizable MCP server for Zotero that enables AI research tools to access and manage references through a simple API.

Mail MCP

MCP server for any IMAP mailbox that lets AI models read, search, send, and manage email, including processing attachments as images.