Discover Awesome MCP Servers

Algorand MCP Server

Provides 50+ tools for comprehensive Algorand blockchain development including account management, asset operations, smart contracts, atomic transactions, swap functionality via Pera Swap, and semantic search through Algorand documentation.

Icecast MCP Server

Analyzes and optimizes Icecast streaming server configurations with automated security audits, performance recommendations, and capacity planning for internet radio stations.

GOLEM-3DMCP-Rhino-

GOLEM-3DMCP implements the Model Context Protocol to give AI agents direct, programmatic control of Rhino 8 — create geometry, run booleans, drive Grasshopper, capture viewports, and execute arbitrary Python scripts, all through natural language. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible host.

mcpGraph

Enables orchestration of MCP tool calls through declarative YAML-defined directed graphs with data transformation, conditional routing, and observable execution flows.

Bocha Search MCP

Un motor de búsqueda centrado en la inteligencia artificial que permite a las aplicaciones de IA acceder a conocimiento de alta calidad proveniente de miles de millones de páginas web y fuentes de contenido del ecosistema en diversos dominios, incluyendo el clima, noticias, enciclopedias, información médica, billetes de tren e imágenes.

Remote MCP Server on Cloudflare

my-mcp-server (sin autenticación)

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Okay, I can help you understand how to create different MCP (Model Configuration Protocol) servers using the TypeScript SDK. However, I need a little more information to give you the *most* helpful and specific answer. Please tell me: 1. **Which MCP SDK are you using?** There are several possibilities. For example: * Are you referring to a specific cloud provider's MCP service (e.g., Google Cloud's Model Registry, AWS SageMaker Model Registry, Azure Machine Learning Model Registry)? If so, please specify which one. * Are you using a more general-purpose MCP library or framework? If so, please provide the name of the library/framework. * Are you building your own MCP server from scratch? 2. **What do you mean by "different"?** What aspects of the MCP server do you want to customize or differentiate? For example: * **Different data storage:** Do you want to store model configurations in different databases (e.g., PostgreSQL, MongoDB, a file system)? * **Different authentication/authorization:** Do you need different ways to authenticate users or control access to model configurations? * **Different APIs:** Do you want to expose different endpoints or use a different API style (e.g., REST, gRPC)? * **Different model configuration formats:** Do you want to support different formats for defining model configurations (e.g., JSON, YAML, Protobuf)? * **Different model deployment targets:** Do you want to manage models deployed to different environments (e.g., cloud, edge, on-premise)? * **Different metadata:** Do you want to store different metadata about your models? 3. **What is your current level of understanding?** Are you just starting out, or have you already tried something? Providing code snippets of what you've tried will help me understand your current progress and provide more targeted guidance. **General Concepts and Approaches (Without Specific SDK Information)** Assuming you're building something from scratch or using a more general-purpose library, here's a breakdown of the common elements involved in creating an MCP server and how you might differentiate them: * **Data Model:** The core of an MCP server is the data model that represents model configurations. This typically includes: * Model name/ID * Model version * Model metadata (e.g., description, author, creation date) * Model parameters (e.g., learning rate, batch size) * Model artifacts (e.g., the trained model file, code dependencies) * Deployment information (e.g., target environment, resource requirements) You can differentiate MCP servers by using different data models. For example, one server might focus on deep learning models and store specific information about neural network architectures, while another might focus on traditional machine learning models and store information about feature engineering pipelines. * **Storage Layer:** The storage layer is responsible for persisting model configurations. Common options include: * **Relational databases (e.g., PostgreSQL, MySQL):** Good for structured data and complex queries. * **NoSQL databases (e.g., MongoDB, Cassandra):** Good for flexible schemas and scalability. * **Object storage (e.g., AWS S3, Google Cloud Storage):** Good for storing large model artifacts. * **File system:** Simple but less scalable. You can differentiate MCP servers by using different storage layers. For example, one server might use PostgreSQL for metadata and S3 for model artifacts, while another might use MongoDB for everything. * **API Layer:** The API layer provides an interface for clients to interact with the MCP server. Common options include: * **REST:** A widely used API style based on HTTP. * **gRPC:** A high-performance API style based on Protocol Buffers. * **GraphQL:** A query language for APIs. You can differentiate MCP servers by using different API layers. For example, one server might expose a REST API for easy integration with web applications, while another might expose a gRPC API for high-performance communication between microservices. * **Authentication and Authorization:** These mechanisms control access to the MCP server. Common options include: * **API keys:** Simple but less secure. * **OAuth 2.0:** A widely used standard for delegated authorization. * **Role-based access control (RBAC):** Assigning permissions to roles and then assigning roles to users. You can differentiate MCP servers by using different authentication and authorization mechanisms. For example, one server might use API keys for internal access and OAuth 2.0 for external access. * **Deployment:** How the MCP server is deployed and managed. * **Cloud-based:** Deployed on cloud platforms like AWS, Azure, or GCP. * **On-premise:** Deployed on your own infrastructure. * **Containerized (Docker, Kubernetes):** Provides portability and scalability. You can differentiate MCP servers by deploying them in different environments. **Example (Conceptual - No Specific SDK)** Let's say you want to create two MCP servers: * **MCP Server 1:** For managing TensorFlow models deployed to Google Cloud. It uses PostgreSQL for metadata and Google Cloud Storage for model artifacts. It exposes a REST API and uses Google Cloud IAM for authentication. * **MCP Server 2:** For managing PyTorch models deployed to edge devices. It uses MongoDB for metadata and a local file system for model artifacts. It exposes a gRPC API and uses API keys for authentication. In this case, you would need to: 1. Define different data models for TensorFlow and PyTorch models. 2. Implement different storage layers using PostgreSQL/GCS and MongoDB/file system. 3. Implement different API layers using REST and gRPC. 4. Implement different authentication mechanisms using Google Cloud IAM and API keys. **TypeScript Code Snippet (Illustrative - Requires Specific SDK)** ```typescript // This is a very high-level example and needs to be adapted to your specific SDK. // Example of defining a data model (simplified) interface TensorFlowModelConfig { name: string; version: string; architecture: string; learningRate: number; gcsArtifactPath: string; // Path to the model in Google Cloud Storage } interface PyTorchModelConfig { name: string; version: string; modelDefinition: string; // Path to the model definition file batchSize: number; localArtifactPath: string; // Path to the model on the local file system } // Example of a simplified API endpoint (using Express.js) import express from 'express'; const app = express(); const port = 3000; app.get('/tensorflow/models/:name', (req, res) => { // Logic to retrieve TensorFlow model config from PostgreSQL and GCS const modelName = req.params.name; // ... (Retrieve from database and storage) const modelConfig: TensorFlowModelConfig = { name: modelName, version: "1.0", architecture: "CNN", learningRate: 0.001, gcsArtifactPath: "gs://my-bucket/my-model.pb" }; res.json(modelConfig); }); app.listen(port, () => { console.log(`Example app listening at http://localhost:${port}`); }); ``` **Next Steps** Please provide the information I requested at the beginning of this response (specifically, the MCP SDK you're using and what you mean by "different"). With that information, I can give you much more specific and helpful guidance, including code examples tailored to your situation.

Maiga API MCP Server

Provides comprehensive integration with the Maiga API for cryptocurrency analysis, including token technicals, social sentiment tracking, and KOL insights. It enables AI assistants to retrieve market reports, trending token data, and detailed on-chain information.

ONLYOFFICE DocSpace MCP Server

Connects AI agents to ONLYOFFICE DocSpace, enabling them to manage rooms, collaborate on files, and handle permissions via natural language. It supports multiple transport protocols and authentication methods to facilitate document workflow automation.

Spotify MCP Server

Un servidor de Protocolo de Control de Medios para controlar la reproducción de Spotify.

qBittorrent MCP

A service that provides programmatic access to qBittorrent's WebUI API, enabling management of torrents, trackers, tags, speed controls, and system information through natural language.

raindrop-mcp

Un servidor MCP para Raindrop.io (servicio de marcadores).

memory-engine-mcp

An MCP server that provides persistent memory for AI agents by storing session snapshots, factual memories, and conversation summaries. It enables seamless continuity between interactions by allowing agents to restore previous emotional states and recall relevant past experiences.

Get Notes MCP Server

Integrates with Get Notes API to search and retrieve knowledge from configured knowledge bases with AI-powered synthesis and raw recall capabilities.

1Panel MCP Server

Enables AI agents to manage server infrastructure through the 1Panel API, including Docker containers, databases, and system monitoring. It provides tools for website management, file operations, and application deployment via natural language commands.

MCP MySQL Server

Enables interaction with MySQL databases (including AWS RDS and cloud instances) through natural language. Supports database connections, query execution, schema inspection, and comprehensive database management operations.

Meraki Magic MCP

A Python-based MCP server that enables querying Cisco's Meraki Dashboard API to discover, monitor, and manage Meraki environments.

RAG Application

Una demostración de una aplicación de Generación Aumentada por Recuperación (RAG) con integración del servidor MCP.

Cursor Rust Tools

Un servidor MCP para permitir que el LLM en Cursor acceda a Rust Analyzer, Crate Docs y comandos de Cargo.

Canon Camera MCP

A minimal server for controlling Canon cameras remotely via the Canon Camera Control API, using FastMCP for streamable HTTP transport.

mcp-database-server

A modular MCP server that enables interaction with multiple database types including PostgreSQL, MySQL, SQLite, Redis, MongoDB, and LDAP. It provides tools for executing queries, managing SQL commands, and exploring database schemas with configurable read-only security.

MCP Prompt Optimizer

This MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.

TAKO MCP Server for Okta

Enables AI assistants to securely query and manage Okta resources for IAM and security administration. It supports dual-mode operation for standard tool interaction or autonomous agent workflows with sandboxed code execution.

random-number-server

An MCP server that generates random numbers by using national weather data as entropy seeds. It provides a unique way to generate random values through weather API integration within the Model Context Protocol.

Amazon Business Integrations MCP Server

Provides AI-enabled access to Amazon Business API documentation, sample code, and troubleshooting resources. Enables developers to search and retrieve API documentation, generate integration code, and get guided solutions for common errors during the API integration process.

PinePaper MCP Server

Enables AI assistants to create and animate graphics in PinePaper Studio using natural language, supporting text, shapes, behavior-driven animations, procedural backgrounds, and SVG export.

HaloPSA MCP Server

Enables AI assistants to interact with HaloPSA data through secure OAuth2 authentication. Supports SQL queries against the HaloPSA database, API endpoint exploration, and direct API calls for comprehensive PSA data analysis and management.

Cirvoy-Kiro MCP Integration

Enables seamless task synchronization between Kiro IDE and the Cirvoy project management platform. It provides tools to create, list, and update tasks directly within the IDE using the Model Context Protocol.

SpherePay MCP Server

Enables users to manage the SpherePay payment platform, including customer onboarding, bank and wallet management, and cross-chain transfers. It provides tools for executing financial workflows, tracking transfers, and configuring virtual accounts for fiat-to-stablecoin conversions.

Substack MCP Server

Enables interaction with Substack publications through natural conversation, allowing users to create posts with cover images, publish notes, manage content, and retrieve profile information.