Discover Awesome MCP Servers

dingtalk-mcp

Dingtalk MCP Server

Airthings Consumer MCP Server

Airthings Consumer MCP Server

Linear MCP Server

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MCP Unreal Server

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Basic MCP Server

A minimal Model Context Protocol server template demonstrating basic implementation of tools, resources, and prompts built with Smithery SDK.

Office 365 MCP Server by CData

Office 365 MCP Server by CData

Shaka Packager MCP Server

An MCP server that integrates Shaka Packager with Claude AI applications, enabling Claude to analyze, transcode, and package video files for streaming in formats like HLS and DASH.

Context Engineering MCP Platform

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.

BigQuery Validator

Enables validation and dry-run analysis of BigQuery SQL queries without execution. Provides cost estimates, schema previews, and syntax validation for BigQuery queries.

Readwise MCP

A local Model Context Protocol server that connects LLM clients (like Claude) to Readwise, enabling AI assistants to access and interact with your saved reading content.

MCP Beancount Tool

Enables interaction with local Beancount accounting ledgers through structured tools for viewing accounts, balances, and transactions, as well as inserting/removing transactions and answering natural-language questions via BeanQuery. Provides deterministic, validated, and auditable financial data operations with offline-first functionality.

MCP Data Server

Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.

Puch AI WhatsApp Integration MCP Server

Enables AI assistants to fetch web content, search for products on Google Shopping, and integrate with WhatsApp messaging. Serves as a bridge between Puch AI and external services through authenticated API endpoints.

us-legal-mcp

An MCP server that provides comprehensive US legislation.

ServiceNow MCP Server

Enables Claude to interact with ServiceNow instances through comprehensive API integration. Supports incident management, service catalog operations, change requests, knowledge base management, user administration, and agile project management with multiple authentication methods.

MCP Server Code Execution Mode

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

Obsidian MCP Server

Allows AI models to interact with Obsidian notes through the Local REST API, enabling creation, reading, updating, searching of notes, and Git-based automatic backups.

linux-mcp-server

Dukungan untuk menjalankan shell di Linux.

AVS Document Search System

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

EpicMe MCP

An application that demonstrates the future of user interactions through natural language with LLMs, enabling user registration, authentication, and data interaction exclusively via Model Context Protocol (MCP) tools.

Monad NFT Launch Tool

An MCP server that helps users create NFT collections, deploy smart contracts to the Monad blockchain, and generate mint websites with Claude AI integration.

MCP Chat

A command-line interface application that enables interaction with LLMs through document retrieval, command-based prompts, and extensible tool integrations using the Model Control Protocol architecture.

Azure AI Foundry MCP Server

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

Sentry MCP Server

Server Model Context Protocol (MCP) untuk Sentry

Directory Explorer MCP Server

A Model Context Protocol server that provides token-aware directory exploration and file analysis for Large Language Models, enabling intelligent codebase navigation with features like directory scanning, file content extraction, and token usage statistics.

Image Generator

I understand you want me to generate and return an image using Together.ai. However, I am a text-based AI and do not have the capability to directly generate or return images. I can provide you with the necessary information and code snippets to do so yourself using the Together.ai API. To generate an image using Together.ai, you would typically need to: 1. **Sign up for a Together.ai account and obtain an API key.** This is essential for authenticating your requests. 2. **Choose a model.** Together.ai offers various image generation models. You'll need to select one that suits your needs. 3. **Use the Together.ai API to send a request with your desired parameters.** This includes the model you want to use, the prompt describing the image you want to generate, and any other relevant settings (e.g., image size, number of images). 4. **Receive the generated image data from the API.** This data is usually in the form of a URL pointing to the image or a base64 encoded string. 5. **Display or save the image.** You can then display the image in your application or save it to a file. Here's a basic example of how you might use the Together.ai API with Python (using the `requests` library): ```python import requests import json # Replace with your actual API key api_key = "YOUR_TOGETHERAI_API_KEY" # API endpoint for image generation api_url = "https://api.together.xyz/inference" # Request payload payload = { "model": "stabilityai/stable-diffusion-xl-base-1.0", # Example model "prompt": "A futuristic cityscape at sunset", "n_images": 1, # Number of images to generate "size": "512x512", # Image size "steps": 30 # Number of steps } headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } try: response = requests.post(api_url, headers=headers, data=json.dumps(payload)) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() # Check for errors in the response if "error" in data: print(f"Error from Together.ai: {data['error']}") else: # Assuming the API returns a URL to the generated image image_url = data["output"]["choices"][0]["image_url"] print(f"Image URL: {image_url}") # You can then use this URL to display or download the image # For example, to download the image: image_response = requests.get(image_url, stream=True) image_response.raise_for_status() with open("generated_image.png", "wb") as out_file: for chunk in image_response.iter_content(chunk_size=8192): out_file.write(chunk) print("Image downloaded successfully!") except requests.exceptions.RequestException as e: print(f"Request failed: {e}") except json.JSONDecodeError: print("Failed to decode JSON response.") except KeyError as e: print(f"Missing key in JSON response: {e}") except Exception as e: print(f"An unexpected error occurred: {e}") ``` **Important Notes:** * **Replace `"YOUR_TOGETHERAI_API_KEY"` with your actual API key.** * **Choose the appropriate model.** The example uses `stabilityai/stable-diffusion-xl-base-1.0`. Refer to the Together.ai documentation for a list of available models and their capabilities. * **Adjust the `prompt` and other parameters** in the `payload` to customize the image generation. * **Error Handling:** The code includes basic error handling to catch potential issues with the API request or response. You should implement more robust error handling in a production environment. * **Rate Limits:** Be aware of Together.ai's rate limits and usage policies. * **API Changes:** The Together.ai API may change over time. Always refer to the official documentation for the most up-to-date information. This code provides a starting point. You'll need to adapt it to your specific needs and integrate it into your application. Remember to consult the Together.ai documentation for detailed information on their API and available models. I can help you refine the prompt, choose a model, or troubleshoot any issues you encounter while implementing this. Just let me know what you need! **Indonesian Translation:** Saya mengerti Anda ingin saya menghasilkan dan mengembalikan gambar menggunakan Together.ai. Namun, saya adalah AI berbasis teks dan tidak memiliki kemampuan untuk menghasilkan atau mengembalikan gambar secara langsung. Saya dapat memberi Anda informasi dan cuplikan kode yang diperlukan untuk melakukannya sendiri menggunakan API Together.ai. Untuk menghasilkan gambar menggunakan Together.ai, Anda biasanya perlu: 1. **Mendaftar akun Together.ai dan mendapatkan kunci API.** Ini penting untuk mengotentikasi permintaan Anda. 2. **Pilih model.** Together.ai menawarkan berbagai model pembuatan gambar. Anda perlu memilih salah satu yang sesuai dengan kebutuhan Anda. 3. **Gunakan API Together.ai untuk mengirim permintaan dengan parameter yang Anda inginkan.** Ini termasuk model yang ingin Anda gunakan, perintah yang menjelaskan gambar yang ingin Anda hasilkan, dan pengaturan relevan lainnya (misalnya, ukuran gambar, jumlah gambar). 4. **Menerima data gambar yang dihasilkan dari API.** Data ini biasanya berupa URL yang menunjuk ke gambar atau string yang dikodekan base64. 5. **Menampilkan atau menyimpan gambar.** Anda kemudian dapat menampilkan gambar di aplikasi Anda atau menyimpannya ke file. Berikut adalah contoh dasar bagaimana Anda dapat menggunakan API Together.ai dengan Python (menggunakan pustaka `requests`): ```python import requests import json # Ganti dengan kunci API Anda yang sebenarnya api_key = "KUNCI_API_TOGETHERAI_ANDA" # Endpoint API untuk pembuatan gambar api_url = "https://api.together.xyz/inference" # Payload permintaan payload = { "model": "stabilityai/stable-diffusion-xl-base-1.0", # Contoh model "prompt": "Pemandangan kota futuristik saat matahari terbenam", "n_images": 1, # Jumlah gambar yang akan dihasilkan "size": "512x512", # Ukuran gambar "steps": 30 # Jumlah langkah } headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } try: response = requests.post(api_url, headers=headers, data=json.dumps(payload)) response.raise_for_status() # Munculkan HTTPError untuk respons buruk (4xx atau 5xx) data = response.json() # Periksa kesalahan dalam respons if "error" in data: print(f"Kesalahan dari Together.ai: {data['error']}") else: # Asumsikan API mengembalikan URL ke gambar yang dihasilkan image_url = data["output"]["choices"][0]["image_url"] print(f"URL Gambar: {image_url}") # Anda kemudian dapat menggunakan URL ini untuk menampilkan atau mengunduh gambar # Misalnya, untuk mengunduh gambar: image_response = requests.get(image_url, stream=True) image_response.raise_for_status() with open("gambar_yang_dihasilkan.png", "wb") as out_file: for chunk in image_response.iter_content(chunk_size=8192): out_file.write(chunk) print("Gambar berhasil diunduh!") except requests.exceptions.RequestException as e: print(f"Permintaan gagal: {e}") except json.JSONDecodeError: print("Gagal mendekode respons JSON.") except KeyError as e: print(f"Kunci hilang dalam respons JSON: {e}") except Exception as e: print(f"Terjadi kesalahan tak terduga: {e}") ``` **Catatan Penting:** * **Ganti `"KUNCI_API_TOGETHERAI_ANDA"` dengan kunci API Anda yang sebenarnya.** * **Pilih model yang sesuai.** Contohnya menggunakan `stabilityai/stable-diffusion-xl-base-1.0`. Lihat dokumentasi Together.ai untuk daftar model yang tersedia dan kemampuannya. * **Sesuaikan `prompt` dan parameter lainnya** di `payload` untuk menyesuaikan pembuatan gambar. * **Penanganan Kesalahan:** Kode menyertakan penanganan kesalahan dasar untuk menangkap potensi masalah dengan permintaan atau respons API. Anda harus menerapkan penanganan kesalahan yang lebih kuat di lingkungan produksi. * **Batas Kecepatan:** Waspadai batas kecepatan dan kebijakan penggunaan Together.ai. * **Perubahan API:** API Together.ai dapat berubah dari waktu ke waktu. Selalu lihat dokumentasi resmi untuk informasi terbaru. Kode ini menyediakan titik awal. Anda perlu menyesuaikannya dengan kebutuhan spesifik Anda dan mengintegrasikannya ke dalam aplikasi Anda. Ingatlah untuk berkonsultasi dengan dokumentasi Together.ai untuk informasi rinci tentang API mereka dan model yang tersedia. Saya dapat membantu Anda menyempurnakan perintah, memilih model, atau memecahkan masalah apa pun yang Anda temui saat menerapkan ini. Beri tahu saya apa yang Anda butuhkan!

nova-act-mcp

Sebuah server MCP yang menyediakan alat untuk mengendalikan peramban web menggunakan Amazon Nova Act SDK. Memungkinkan alur kerja otomatisasi peramban multi-langkah melalui agen MCP.