Discover Awesome MCP Servers

Claude MCP Trello

Server Protokol Konteks Model yang menyediakan alat untuk berinteraksi dengan papan Trello, memungkinkan pengelolaan kartu, daftar, dan aktivitas yang lancar sambil menangani pembatasan laju dan keamanan tipe.

MCP Scholarly Server

Memungkinkan pengguna untuk mencari artikel akademik di platform seperti arXiv menggunakan kata kunci tertentu, dengan rencana untuk mengintegrasikan lebih banyak database ilmiah di masa mendatang.

MCP Image Recognition Server

Menyediakan kemampuan pengenalan gambar menggunakan API Anthropic Claude Vision dan OpenAI GPT-4 Vision, mendukung berbagai format gambar dan menawarkan ekstraksi teks opsional melalui Tesseract OCR.

Notion Knowledge Base MCP Server

Sebuah server MCP yang terhubung ke basis pengetahuan Notion Anda, memungkinkan Anda untuk meminta dan mengambil informasi langsung dari VSCode menggunakan ekstensi Cline.

Redash MCP Server

Server Model Context Protocol (MCP) yang mengintegrasikan Redash dengan asisten AI seperti Claude, memungkinkan mereka untuk menanyakan data, mengelola visualisasi, dan berinteraksi dengan dasbor melalui bahasa alami.

Ghost MCP Server

Terintegrasi dengan Ghost Admin API untuk memungkinkan pengelolaan terprogram fitur Ghost CMS, termasuk posting, halaman, tag, penulis, anggota, dan unggahan gambar, menyediakan kemampuan manajemen konten yang efisien.

MCP Reddit Server

Sebuah server Protokol Konteks Model yang memungkinkan asisten AI seperti Claude untuk menjelajahi dan menganalisis konten Reddit, termasuk mencari subreddit, mengambil detail postingan beserta komentar, dan melihat postingan yang sedang tren.

OpenCTI MCP Server

Server Protokol Konteks Model yang memfasilitasi integrasi dengan OpenCTI, memungkinkan pengguna untuk meminta dan mengambil data intelijen ancaman siber melalui antarmuka yang terstandardisasi.

supabase-mcp

Sebuah server MCP yang menyediakan alat untuk berinteraksi dengan database, penyimpanan, dan fungsi edge Supabase.

MCP Google Workspace Server

Memungkinkan interaksi dengan Gmail dan Google Kalender menggunakan protokol MCP, mendukung banyak akun Google, manajemen email, dan operasi kalender melalui bahasa alami.

FastMCP

FastMCP adalah server MCP komprehensif yang memungkinkan paparan data dan fungsionalitas yang aman dan terstandarisasi ke aplikasi LLM, menawarkan sumber daya, alat, dan manajemen prompt untuk interaksi LLM yang efisien.

MCP Atlassian

Sebuah server MCP yang memungkinkan agen AI untuk berinteraksi dengan produk Atlassian (Confluence dan Jira) untuk manajemen konten, pelacakan masalah, dan manajemen proyek melalui antarmuka yang terstandardisasi.

Coinmarket

Integrasi API CoinMarketCap untuk mengambil daftar dan kuotasi mata uang kripto.

protonmail-mcp

Server MCP ini menyediakan fungsionalitas pengiriman email menggunakan layanan SMTP Protonmail. Server ini memungkinkan baik Claude Desktop maupun ekstensi Cline VSCode untuk mengirim email atas nama Anda menggunakan kredensial Protonmail Anda.

Confluence Communication Server

Memungkinkan kueri dan pengambilan konten dari Confluence melalui pencarian CQL dan pengambilan konten halaman, memungkinkan Claude untuk mengakses informasi yang disimpan di ruang kerja Confluence dengan lancar.

Gmail MCP Server

Menyediakan integrasi Gmail yang komprehensif dengan kemampuan pemrosesan LLM, memungkinkan pengguna untuk membaca, mencari, memfilter email, dan menangani lampiran melalui Model Context Protocol.

Steel Puppeteer

Server Protokol Konteks Model yang memungkinkan LLM (Model Bahasa Besar) untuk mengotomatiskan peramban web menggunakan Puppeteer, memungkinkan navigasi, tangkapan layar, pengisian formulir, dan eksekusi JavaScript dalam lingkungan peramban yang sebenarnya.

Twitter MCP Server

Sebuah server MCP yang memungkinkan Claude berinteraksi dengan Twitter, memungkinkan untuk memposting tweet dan mencari konten Twitter.

Crew AI MCP Server

Mengaktifkan agen AI dan manajemen tugas menggunakan kerangka kerja CrewAI, memungkinkan pengguna untuk membuat dan menjalankan agen dan tugas dalam lingkungan alur kerja otomatis.

MCP Documentation Server

Server dokumentasi pintar yang menyediakan peningkatan kode berbantuan AI dan manajemen dokumentasi melalui integrasi Claude Desktop.

Geoapify MCP Server

Mengonversi alamat menjadi koordinat GPS dan membuat visualisasi peta menggunakan Geoapify API, memungkinkan pengguna Claude untuk menghasilkan data GeoJSON dan gambar peta dari daftar lokasi.

Linear MCP Server

Sebuah server Protokol Konteks Model yang memungkinkan Claude untuk berinteraksi dengan API Linear untuk mengelola tim, isu, proyek, dan siklus.

datadog

Okay, here's how you can access monitor and cluster logs from Datadog, broken down into steps and considerations: **Accessing Monitor Logs** Monitors in Datadog trigger alerts based on specific conditions. Accessing logs related to these monitors helps you understand why a monitor triggered and troubleshoot the underlying issue. 1. **From the Monitor's Alert:** * **Navigate to the Monitor:** Find the monitor that triggered the alert. You can usually do this from the "Events" page, the "Monitors" page, or from a notification (email, Slack, etc.). * **View the Alert Details:** Click on the alert event. This will take you to a detailed view of the alert. * **Look for Log Links:** The alert details often include links directly to relevant logs. These links are usually generated based on tags or attributes associated with the monitor and the data it's monitoring. Look for phrases like "View Logs," "Related Logs," or links to the Log Explorer. These links are the *easiest* way to find the logs relevant to that specific alert. 2. **Using the Log Explorer:** * **Navigate to the Log Explorer:** In the Datadog UI, go to `Logs` -> `Explorer`. * **Filter by Monitor Name/ID:** The most effective way to find logs related to a specific monitor is to filter by the monitor's name or ID. Add a filter like `monitor:"<your_monitor_name>"` or `monitor_id:<your_monitor_id>`. Replace `<your_monitor_name>` and `<your_monitor_id>` with the actual values. You can find the monitor ID in the monitor's settings. * **Filter by Tags:** Monitors often have tags associated with them (e.g., `environment:production`, `service:web`). Use these tags to further refine your log search. For example: `environment:production service:web monitor:"My Web Service Monitor"`. * **Filter by Time:** Set the time range in the Log Explorer to the period around when the monitor triggered. This will help you focus on the logs that are most likely related to the alert. * **Search for Keywords:** If you have an idea of what might be causing the issue, search for relevant keywords in the logs. For example, if the monitor is alerting on high CPU usage, search for "CPU," "high," "usage," or related terms. * **Use Facets:** The Log Explorer's facets (on the left-hand side) are very helpful. They allow you to quickly filter logs based on attributes like hostname, service, status, etc. Use them to narrow down your search. 3. **Using Dashboards:** * **Create a Dashboard:** If you frequently need to access logs related to specific monitors, create a dashboard that includes a log widget. * **Configure the Log Widget:** Configure the log widget to filter logs based on the monitor's name, ID, tags, or other relevant attributes. This will give you a dedicated view of the logs related to that monitor. **Accessing Cluster Logs (e.g., Kubernetes, ECS)** Accessing cluster logs is crucial for understanding the health and performance of your containerized applications. 1. **Ensure Log Collection is Configured:** * **Datadog Agent:** The Datadog Agent needs to be properly configured to collect logs from your cluster. This typically involves deploying the agent as a DaemonSet (in Kubernetes) or configuring it to run on your ECS instances. * **Log Configuration:** You need to configure the agent to collect the specific logs you're interested in. This usually involves specifying the log file paths or using auto-discovery features to automatically detect container logs. Refer to the Datadog documentation for your specific cluster environment (Kubernetes, ECS, etc.). The documentation will provide detailed instructions on how to configure log collection. 2. **Using the Log Explorer (Similar to Monitor Logs):** * **Navigate to the Log Explorer:** `Logs` -> `Explorer`. * **Filter by Cluster Name/ID:** If you have multiple clusters, filter by the cluster name or ID. The attribute name for the cluster will depend on your setup (e.g., `kube_cluster_name`, `ecs_cluster_name`). * **Filter by Namespace (Kubernetes):** If you're using Kubernetes, filter by the namespace to focus on logs from a specific application or team. Use the `kube_namespace` attribute. * **Filter by Pod/Container Name:** Filter by the pod name (`kube_pod_name`) or container name (`container_name`) to focus on logs from a specific container. * **Filter by Service:** Filter by the service name to see logs related to a particular service. * **Filter by Host:** Filter by the host to see logs related to a particular host. * **Use Kubernetes Metadata:** Datadog automatically enriches Kubernetes logs with metadata like pod labels, annotations, and deployment names. Use these metadata fields to filter and analyze your logs. * **Example:** `kube_cluster_name:"my-production-cluster" kube_namespace:"my-app" kube_pod_name:"my-app-pod-12345"` 3. **Using Dashboards:** * **Create Cluster-Specific Dashboards:** Create dashboards that are tailored to your cluster environment. * **Include Log Widgets:** Add log widgets to your dashboards to display cluster logs. * **Use Template Variables:** Use template variables to make your dashboards more flexible. For example, you could create a template variable for the namespace, so you can easily switch between different namespaces. 4. **Using the Container View:** * **Navigate to the Container View:** In the Datadog UI, go to `Infrastructure` -> `Containers`. * **Select a Container:** Select the container you're interested in. * **View Logs:** The container view often provides a direct link to the logs for that container. **Important Considerations:** * **Log Volume:** Collecting logs from a large cluster can generate a significant amount of data. Make sure you have sufficient log retention and that you're not collecting unnecessary logs. Consider using log sampling or filtering to reduce the volume of logs. * **Log Format:** Ensure that your logs are in a format that Datadog can easily parse. Structured logging (e.g., JSON) is generally preferred. * **Security:** Be careful about what information you log. Avoid logging sensitive data like passwords or API keys. * **Retention Policies:** Understand your Datadog log retention policies. Logs are typically retained for a limited time. * **Agent Configuration:** Properly configuring the Datadog Agent is critical for successful log collection. Refer to the Datadog documentation for detailed instructions. * **Context Tags:** Use context tags to add relevant information to your logs. This will make it easier to filter and analyze your logs. For example, you could add tags for the application name, environment, or user ID. * **Log Pipelines:** Use Datadog's log pipelines to process and enrich your logs. You can use pipelines to filter, redact, and transform your logs before they are indexed. **Example Scenario (Kubernetes):** Let's say you have a Kubernetes cluster and you want to troubleshoot an issue with a specific pod. 1. **Identify the Pod:** You know the name of the pod is `my-app-pod-12345` and it's running in the `my-app` namespace. 2. **Go to the Log Explorer:** Navigate to `Logs` -> `Explorer`. 3. **Add Filters:** Add the following filters: * `kube_cluster_name:"your-cluster-name"` (Replace `your-cluster-name` with your actual cluster name) * `kube_namespace:"my-app"` * `kube_pod_name:"my-app-pod-12345"` 4. **Set Time Range:** Set the time range to the period when the issue occurred. 5. **Analyze Logs:** Examine the logs to identify any errors, warnings, or other relevant information. Use the facets to further filter the logs. By following these steps, you should be able to effectively access and analyze monitor and cluster logs in Datadog to troubleshoot issues and gain insights into your applications and infrastructure. Remember to consult the official Datadog documentation for the most up-to-date information and best practices.

Penrose MCP Server

Memfasilitasi pembuatan diagram matematika menggunakan bahasa alami melalui bahasa khusus domain Penrose, memungkinkan definisi tipe matematika, hubungan, dan aturan representasi visual.

Microsoft SQL Server MCP Server (MSSQL)

Jembatan yang mudah digunakan yang memungkinkan asisten AI seperti Claude dan Cursor IDE untuk langsung meminta dan menjelajahi database Microsoft SQL Server. Tidak diperlukan pengalaman coding!

olostep-mcp

Mencari di Google, situs web individual, dan mengikis kontennya. Cepat dan hemat biaya. ⚡️

MCP Google Custom Search Server

Sebuah server Protokol Konteks Model yang memungkinkan LLM (Model Bahasa Besar) untuk melakukan pencarian web menggunakan Google Custom Search API melalui antarmuka yang terstandardisasi.

omniparser-autogui-mcp

Operasi otomatis GUI di layar.

DICOM MCP Server

Server pengujian konektivitas yang memungkinkan operasi jaringan DICOM (seperti C-ECHO) melalui Protokol Konteks Model Claude, dengan dukungan untuk manajemen konfigurasi node.