Discover Awesome MCP Servers

Obsidian MCP Server

Enables MCP clients to interact with Obsidian vaults via filesystem operations and optional REST API integration for advanced UI commands. It features multi-vault auto-discovery, concurrent-safe file handling, and comprehensive tools for searching, reading, and managing vault content.

MCP Server for Mem.ai

Enables AI assistants to intelligently save, organize, and retrieve content through Mem.ai's knowledge management platform. Supports creating notes, collections, and AI-powered content processing with automatic organization.

market-data-mcp

Real-time financial market data MCP server. Stocks, crypto, technicals, sentiment, FDA calendar. No API keys required.

Vercel Functions MCP Server Template

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and fetching weather data to demonstrate basic tool implementation and API integration patterns.

safe-ssh-mcp

A secure SSH MCP server that enables execution of read-only diagnostic commands over SSH, preventing modifications to remote systems.

Nerq MCP Server

Provides trust scores, comparisons, and search for software entities, AI tools, packages, and MCP servers using Nerq's Trust Score system.

observe-mcp

An MCP server for querying Observe.com logs using OPAL, with tools for dataset discovery, schema inspection, and log searching.

io.github.MauricioPerera/kite-lite

MCP server for lightweight web browsing: fetch and parse pages, extract structured content, run isolated JavaScript, and render to SVG/PNG/PDF with a minimal CDP interface.

UK Mortgage Repayment MCP

Estimates UK repayment mortgage payments, including monthly payment, total interest, and overpayment impact, for use in ChatGPT.

lig-room

Enables AI agents to search for available desk space, submit room reservations, and let admins approve or reject booking requests through a scoped MCP API.

Arkana

An MCP server that provides 294 malware analysis tools behind an AI-driven interface, enabling natural language investigation of binaries.

RepoFather

Scans repositories to produce a readiness score and actionable fixes across positioning, tryability, community, MCP, and monetization, plus generates launch assets and sponsor-tier ideas.

Google Search MCP Server

A Model Context Protocol server that provides web and image search capabilities through Google's Custom Search API, allowing AI assistants like Claude to access current information from the internet.

GitHub MCP Server

Enables users to interact with GitHub via natural language requests, executing API calls and returning structured responses.

CT2 MCP Server

Centralizes multi-agent team workflows, providing Kanban, audit timeline, scorecards, and event hooks, integrated natively with Hermes Dashboard via the Model Context Protocol.

Whoop MCP Server

Connects Whoop health data to Claude via an MCP server, enabling retrieval of recovery, sleep, strain, and workout metrics through natural language tools.

hashloom

Treats software units as content-addressed contracts, enabling efficient agent regeneration loops with cached verification and tiny context packets.

one-roof

Unified UK property search across major portals with deduplication and open-data enrichment, enabling natural-language queries for listings, sold prices, EPC, crime, schools, and market stats.

memgate

Access control, conflict resolution, and audit for shared agent memory. Policy-gated memory tools over Postgres + pgvector, exposed via MCP.

chrome-agent-mcp

Enables AI agents to fully control Google Chrome: navigate, click, fill forms, inspect DevTools, and manage tabs with parallel execution and session isolation.

firefly-iii-mcp

Deploy a Firefly III MCP server on Cloudflare Workers to let AI assistants interact with your Firefly III personal finance data, including accounts, transactions, budgets, and reports, with configurable tool presets.

PostgreSQL MCP Server

Enables LLMs to interact deeply with PostgreSQL databases—query data, manage schema, analyze performance, and administer the database.

@theyahia/voximplant-mcp

MCP server for Voximplant API enabling calls, SMS, recordings, scenarios, and rules management with 11 tools and 2 skills.

Task API MCP

Enables AI clients to create and manage tasks via a local REST API by converting natural language into HTTP requests through the Model Context Protocol.

Prompt Bookmarks

Enables users to organize, search, and manage a shared library of prompts across AI tools via the Model Context Protocol. It supports hierarchical folder organization, tagging, and template variable substitution for dynamic prompt generation.

Android Puppeteer

Enables AI agents to interact with Android devices through visual UI element detection and automated interactions. Provides comprehensive Android automation capabilities including touch gestures, text input, screenshots, and video recording via uiautomator2.

hive-exp

An MCP server enabling AI agents to record, query, and share structured problem-solving experiences with human review and confidence decay.

Build

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.

Iris MCP Server

A multi-backend gateway that enables access to various services like Google Drive and Notion through a single MCP connector. It currently provides comprehensive Google Drive integration for reading, writing, and managing files and folders.