Discover Awesome MCP Servers

Metaplex MCP Server

Metaplex MCP 服务器方便访问 Metaplex 文档和仓库详情，使用户能够通过 MCP 协议搜索和交互各种 Metaplex 资源。

Claude MCP Trello

一个模型上下文协议服务器，提供与 Trello 看板交互的工具，从而实现对卡片、列表和活动的无缝管理，同时处理速率限制和类型安全。

MCP Scholarly Server

使用户能够通过特定关键词在 arXiv 等平台上搜索学术文章，并计划未来整合更多学术数据库。

MCP Image Recognition Server

提供图像识别功能，使用 Anthropic Claude Vision 和 OpenAI GPT-4 Vision API，支持多种图像格式，并提供通过 Tesseract OCR 进行可选的文本提取。

Notion Knowledge Base MCP Server

一个 MCP 服务器，连接到你的 Notion 知识库，允许你使用 Cline 扩展直接从 VSCode 查询和检索信息。

Redash MCP Server

将 Redash 与 Claude 等 AI 助手集成的模型上下文协议 (MCP) 服务器，允许它们通过自然语言查询数据、管理可视化效果以及与仪表板交互。

Ghost MCP Server

集成了 Ghost Admin API，允许对 Ghost CMS 功能进行编程化管理，包括文章、页面、标签、作者、会员和图像上传，从而提供高效的内容管理能力。

MCP Reddit Server

一个模型上下文协议服务器，使像 Claude 这样的人工智能助手能够浏览和分析 Reddit 内容，包括搜索子版块、检索带有评论的帖子详情以及查看热门帖子。

OpenCTI MCP Server

一个模型上下文协议服务器，用于促进与 OpenCTI 的集成，允许用户通过标准化接口查询和检索网络威胁情报数据。 (Alternative, slightly more formal translation:) 一个模型上下文协议 (Model Context Protocol) 服务器，旨在促进与 OpenCTI 的集成，从而使用户能够通过标准化的接口查询和检索网络威胁情报数据。

supabase-mcp

一个 MCP 服务器，提供与 Supabase 数据库、存储和边缘函数交互的工具。

MCP Google Workspace Server

使用 MCP 协议与 Gmail 和 Google 日历进行交互，支持多个 Google 帐户、电子邮件管理以及通过自然语言进行日历操作。

FastMCP

FastMCP 是一个综合性的 MCP 服务器，允许安全和标准化地向 LLM 应用暴露数据和功能，并提供资源、工具和提示管理，以实现高效的 LLM 交互。

MCP Atlassian

一个 MCP 服务器，它使 AI 代理能够通过标准化接口与 Atlassian 产品（Confluence 和 Jira）进行交互，以实现内容管理、问题跟踪和项目管理。

Coinmarket

CoinMarketCap API 集成，用于获取加密货币列表和报价。

protonmail-mcp

这个 MCP 服务器提供使用 Protonmail 的 SMTP 服务发送电子邮件的功能。它允许 Claude Desktop 和 Cline VSCode 扩展程序使用您的 Protonmail 凭据代表您发送电子邮件。

Confluence Communication Server

支持通过 CQL 搜索和页面内容获取，从 Confluence 查询和检索内容，使 Claude 能够无缝访问存储在 Confluence 工作区中的信息。

Gmail MCP Server

提供全面的 Gmail 集成，具备 LLM 处理能力，允许用户通过模型上下文协议读取、搜索、过滤电子邮件和处理附件。

Steel Puppeteer

一个模型上下文协议服务器，它使用 Puppeteer 使大型语言模型 (LLM) 能够自动操作网页浏览器，从而可以在真实的浏览器环境中进行导航、屏幕截图、表单填写和 JavaScript 执行。

Twitter MCP Server

一个 MCP 服务器，使 Claude 能够与 Twitter 互动，允许发布推文和搜索 Twitter 内容。

Crew AI MCP Server

使用 CrewAI 框架启用 AI 代理和任务管理，允许用户在自动化工作流程环境中创建和运行代理和任务。

MCP Documentation Server

一个智能文档服务器，通过 Claude Desktop 集成提供 AI 辅助的代码改进和文档管理。

Linear MCP Server

一个模型上下文协议（Model Context Protocol）服务器，它允许 Claude 与 Linear 的 API 交互，以管理团队、问题、项目和周期。

Geoapify MCP Server

使用 Geoapify API 将地址转换为 GPS 坐标，并创建地图可视化，允许 Claude 用户从位置列表中生成 GeoJSON 数据和地图图像。

datadog

Okay, here's how you can access monitor and cluster logs from Datadog, broken down into steps and considerations: **1. Accessing Monitor Logs:** * **From the Monitor Page:** 1. **Navigate to Monitors:** In the Datadog UI, go to "Monitors" -> "Manage Monitors". 2. **Find the Monitor:** Locate the specific monitor you're interested in. You can use the search bar, filters (e.g., by tag, name, status), or browse the list. 3. **Monitor Status and Events:** Click on the monitor's name. This will take you to the monitor's details page. Here you'll see: * **Monitor Status:** The current status of the monitor (OK, Alert, Warning, No Data). * **Events Timeline:** A timeline of events related to the monitor. This is where you'll find when the monitor triggered, when it recovered, and any associated messages. * **Event Details:** Click on a specific event in the timeline to see more details. This often includes: * The time the event occurred. * The message associated with the event (which might contain information about the cause of the alert). * Links to related logs, metrics, or traces (if configured). This is a crucial part for troubleshooting. * **Using the Event Explorer:** 1. **Navigate to Event Explorer:** In the Datadog UI, go to "Events" -> "Explorer". 2. **Filter by Monitor:** Use the search bar or filters to narrow down the events to those related to your specific monitor. You can filter by: * `monitor:<monitor_name>` (replace `<monitor_name>` with the name of your monitor) * `monitor_id:<monitor_id>` (replace `<monitor_id>` with the ID of your monitor) You can find the monitor ID on the monitor's details page. * `status:<alert|warning|ok|no data>` to filter by the status of the monitor. 3. **Analyze Events:** The Event Explorer allows you to see a stream of events related to your monitor. You can: * Sort events by time. * View the event message. * Click on an event to see more details. * Use facets on the left-hand side to further refine your search. * **Linking Monitors to Logs (Important for Effective Troubleshooting):** * **Use Tags:** The most effective way to link monitors to logs is to use consistent tagging. When you create your monitor, add tags that are also present in your logs. For example, if your monitor is for a specific service, tag both the monitor and the logs from that service with `service:my-service`. * **Use Log Patterns in Monitor Messages:** If your monitor message includes specific patterns that appear in your logs (e.g., an error code, a transaction ID), you can use these patterns to search for related logs. * **Use the `{{log.id}}` variable in monitor messages:** If you are creating monitors based on log patterns, you can include the `{{log.id}}` variable in the monitor message. This will include the unique ID of the log message that triggered the monitor, making it very easy to find the exact log in the Log Explorer. **2. Accessing Cluster Logs (Kubernetes, ECS, etc.):** * **Ensure Log Collection is Configured:** The first and most important step is to make sure you've properly configured Datadog to collect logs from your cluster. This typically involves: * **Installing the Datadog Agent:** The Datadog Agent needs to be running on your cluster nodes (or as a DaemonSet in Kubernetes). * **Configuring Log Collection:** You need to tell the Datadog Agent where to find the logs. This usually involves configuring the Agent to monitor specific log files or to collect logs from standard output/standard error of your containers. Datadog provides specific integrations for Kubernetes, ECS, and other container orchestration platforms. Follow the official Datadog documentation for your specific platform. * **Using the Datadog Operator for Kubernetes (Recommended):** For Kubernetes, the Datadog Operator simplifies the deployment and management of the Datadog Agent and related resources. It can automatically configure log collection based on your Kubernetes resources. * **Using the Log Explorer:** 1. **Navigate to Log Explorer:** In the Datadog UI, go to "Logs" -> "Explorer". 2. **Filter by Cluster:** Use the search bar or facets to filter the logs to those from your cluster. Common filters include: * `kubernetes.cluster.name:<cluster_name>` (for Kubernetes) * `ecs.cluster.name:<cluster_name>` (for ECS) * `host:<hostname>` (to filter by specific nodes in the cluster) * `source:<source_name>` (if you've configured a specific source for your cluster logs) * `service:<service_name>` (to filter by specific services running in the cluster) * `container_name:<container_name>` (to filter by specific containers) 3. **Analyze Logs:** The Log Explorer provides powerful tools for analyzing your cluster logs: * **Search:** Use the search bar to find specific keywords, error messages, or patterns. * **Facets:** Use the facets on the left-hand side to filter and group your logs. * **Time Series:** Create time series graphs based on log data (e.g., count the number of error logs over time). * **Live Tail:** View a live stream of logs as they are generated. * **Log Patterns:** Identify common log patterns to help you understand the behavior of your applications. * **Using Dashboards:** 1. **Create or Edit a Dashboard:** In the Datadog UI, go to "Dashboards" -> "New Dashboard" or edit an existing dashboard. 2. **Add Log Widgets:** Add widgets to your dashboard that display log data. You can use: * **Log Stream Widget:** Displays a stream of logs based on your query. * **Log Count Widget:** Displays the number of logs that match your query over a specific time period. * **Top List Widget:** Displays the top values for a specific attribute in your logs (e.g., the top error messages). 3. **Configure the Widget:** Configure the widget to filter the logs to those from your cluster and to display the information you're interested in. **Example: Kubernetes Log Access** Let's say you want to see the logs from a specific pod in your Kubernetes cluster. 1. **Ensure the Datadog Agent is running as a DaemonSet in your Kubernetes cluster.** This is the recommended way to collect logs. 2. **Verify that the Datadog Agent is configured to collect logs from your containers.** The Datadog Operator can automate this. 3. **In the Log Explorer, use the following filters:** * `kubernetes.cluster.name:<your_cluster_name>` * `kubernetes.pod.name:<your_pod_name>` * `kubernetes.namespace.name:<your_namespace>` **Important Considerations:** * **Log Volume:** Collecting logs from a large cluster can generate a significant amount of data. Consider using log filtering and sampling to reduce the volume of logs you're collecting. * **Security:** Be careful about what information you're logging. Avoid logging sensitive data such as passwords or API keys. Use log masking or redaction to protect sensitive information. * **Retention:** Datadog has log retention policies. Make sure you understand the retention policies and that you're retaining logs for as long as you need them. * **Cost:** Datadog's pricing is based on log volume. Be aware of the cost implications of collecting logs from your cluster. * **Structured Logging:** Using structured logging (e.g., JSON) makes it much easier to query and analyze your logs in Datadog. Encourage your developers to use structured logging in their applications. **In summary, accessing monitor and cluster logs in Datadog requires proper configuration of the Datadog Agent, understanding of the Log Explorer and Event Explorer, and the use of appropriate filters and queries. Linking monitors to logs through tagging and log patterns is crucial for effective troubleshooting.** I've provided a comprehensive guide. Let me know if you have any specific questions or scenarios you'd like me to elaborate on.

Penrose MCP Server

使用 Penrose 的领域特定语言，通过自然语言促进数学图表的创建，从而可以定义数学类型、关系和视觉表示规则。

Microsoft SQL Server MCP Server (MSSQL)

一个易于使用的桥梁，让像 Claude 和 Cursor IDE 这样的 AI 助手可以直接查询和探索 Microsoft SQL Server 数据库。无需任何编程经验！

olostep-mcp

快速且经济高效地搜索谷歌、各个网站并抓取其内容。⚡️

MCP Google Custom Search Server

一个模型上下文协议（Model Context Protocol）服务器，它使大型语言模型（LLMs）能够通过一个标准化的接口，使用谷歌的自定义搜索 API 进行网络搜索。 Or, a slightly more formal translation: 一个模型上下文协议服务器，该服务器允许大型语言模型（LLMs）通过标准化的接口，利用谷歌自定义搜索 API 执行网络搜索。

omniparser-autogui-mcp

屏幕图形用户界面的自动操作。