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MCP Server Playwright

MCP Server Playwright - Claude Desktop 用のブラウザ自動化サービス

🏆 LinkedIn DI MCP Server

Audiense Digital Intelligence LinkedIn MCPサーバーは、Model Context Protocol（MCP）に基づいたサーバーであり、Claudeやその他のMCP互換クライアントがあなたのAudienseアカウントを通じてDI LinkedInと連携することを可能にします。

Initial thoughts

Okay, I understand. You want to convert OpenAPI specifications into tools that are ready to be used with an MCP (presumably Management Component Platform) server. Here's a breakdown of the process and considerations, along with some potential tools and approaches: **Understanding the Goal** The core idea is to take an OpenAPI (formerly Swagger) specification, which describes a REST API, and generate code or configuration that allows an MCP server to interact with and manage that API. This typically involves: 1. **API Discovery:** The MCP needs to understand the API's endpoints, methods (GET, POST, PUT, DELETE, etc.), request parameters, and response formats. The OpenAPI specification provides this information. 2. **Code Generation (Optional):** You might generate client-side code (e.g., in Python, Java, Go) that the MCP can use to make API calls. This code would handle things like serialization/deserialization of data, authentication, and error handling. 3. **Configuration:** You might generate configuration files (e.g., YAML, JSON) that tell the MCP how to interact with the API. This could include details like the API's base URL, authentication credentials, rate limits, and monitoring settings. 4. **Integration:** The generated code or configuration needs to be integrated into the MCP's workflow. This might involve deploying the generated code as a microservice, loading the configuration into the MCP's management console, or using an MCP API to register the new API. **Tools and Approaches** Here are several tools and approaches you can use to achieve this: * **OpenAPI Generator:** This is a very popular and versatile tool. * **Functionality:** It can generate client SDKs, server stubs, API documentation, and configuration files from an OpenAPI specification. It supports a wide range of languages and frameworks. * **How to Use:** 1. **Install:** `npm install @openapitools/openapi-generator-cli -g` (or use Docker, Maven, Gradle, etc.) 2. **Generate Code:** `openapi-generator-cli generate -i <openapi_spec.yaml> -g <generator_name> -o <output_directory>` * `<openapi_spec.yaml>`: Path to your OpenAPI specification file. * `<generator_name>`: The name of the generator to use (e.g., `python`, `java`, `go`, `spring`, `typescript-fetch`). Choose a generator that's appropriate for your MCP's environment and the language you want to use. Consider `generic-server` or `dynamic-html` for configuration files or documentation. * `<output_directory>`: The directory where the generated code/configuration will be placed. * **Example:** `openapi-generator-cli generate -i my_api.yaml -g python -o python_client` (This generates a Python client library.) * **Swagger Codegen (Legacy):** This is the predecessor to OpenAPI Generator. While still functional, OpenAPI Generator is generally preferred because it's actively maintained and has more features. The usage is very similar to OpenAPI Generator. * **Stoplight Studio:** A visual API design and development platform. * **Functionality:** Allows you to design, document, and test APIs. It can also generate code and documentation from OpenAPI specifications. * **How to Use:** Import your OpenAPI specification into Stoplight Studio. Use the built-in code generation features to create client SDKs or server stubs. * **Postman:** A popular API client and testing tool. * **Functionality:** Can import OpenAPI specifications and generate collections of API requests. These collections can be used for testing and documentation. Postman also has a code generation feature. * **How to Use:** Import your OpenAPI specification into Postman. Generate a collection of API requests. Use the code generation feature to create code snippets in various languages. * **Custom Scripting:** You can write your own scripts (e.g., in Python, Node.js) to parse the OpenAPI specification and generate the necessary code or configuration. This gives you the most control over the process but requires more effort. Libraries like `PyYAML` (Python) or `js-yaml` (Node.js) can help you parse the YAML or JSON OpenAPI specification. **Steps to Integrate with MCP** 1. **Choose a Tool/Approach:** Select the tool or approach that best fits your MCP's capabilities and your development skills. OpenAPI Generator is a good starting point. 2. **Generate Code/Configuration:** Use the chosen tool to generate the necessary code or configuration files from your OpenAPI specification. 3. **Adapt the Output (If Necessary):** The generated code or configuration might need to be adapted to fit your MCP's specific requirements. For example, you might need to modify the generated code to use the MCP's logging framework or authentication mechanisms. 4. **Deploy/Integrate:** Deploy the generated code or configuration to your MCP server. This might involve: * Deploying the generated code as a microservice. * Loading the configuration into the MCP's management console. * Using an MCP API to register the new API. 5. **Test:** Thoroughly test the integration to ensure that the MCP can correctly interact with the API. **Example Scenario (Using OpenAPI Generator and Python)** Let's say you have an OpenAPI specification called `my_api.yaml` and you want to generate a Python client library for your MCP. 1. **Generate the Python Client:** ```bash openapi-generator-cli generate -i my_api.yaml -g python -o python_client ``` 2. **Install the Generated Library:** ```bash cd python_client pip install . ``` 3. **Use the Library in Your MCP Code:** ```python import my_api_client # Assuming the generated library is named my_api_client # Configure the client (e.g., set the API key) configuration = my_api_client.Configuration() configuration.api_key['api_key'] = 'YOUR_API_KEY' # Create an API client instance api_client = my_api_client.ApiClient(configuration) my_api = my_api_client.DefaultApi(api_client) # Assuming a default API class # Make an API call try: response = my_api.get_data(param1='value1') # Replace with your actual API call print(response) except my_api_client.ApiException as e: print(f"Exception when calling API: {e}") ``` **Important Considerations** * **Authentication:** How does the MCP authenticate with the API? The generated code or configuration needs to handle this. Consider API keys, OAuth 2.0, or other authentication mechanisms. * **Error Handling:** How should the MCP handle errors from the API? The generated code should include appropriate error handling logic. * **Rate Limiting:** Does the API have rate limits? The MCP needs to respect these limits to avoid being blocked. * **Monitoring:** How will you monitor the API's performance and availability? The MCP should include monitoring capabilities. * **MCP-Specific Requirements:** The specific requirements of your MCP will influence the choice of tools and the integration process. Consult the MCP's documentation for details. **In summary, the process involves using a tool like OpenAPI Generator to create code or configuration from your OpenAPI specification, adapting the output to your MCP's needs, and then deploying and testing the integration.** Remember to consider authentication, error handling, rate limiting, and monitoring. Good luck!

Financial Analysis MCP Server

鏡 (Kagami)

Ghost MCP Server

鏡 (Kagami)

Weather MCP Server

サンプル MCP サーバー実装 (天気予報取得) (Sanpuru MCP sābā jissō (tenki yohō shutoku)) This translates to: * **サンプル (Sanpuru):** Sample * **MCP サーバー (MCP sābā):** MCP Server * **実装 (jissō):** Implementation * **天気予報 (tenki yohō):** Weather Forecast * **取得 (shutoku):** Fetching/Acquisition * **(天気予報取得) (tenki yohō shutoku):** (Weather Forecast Acquisition) - This is in parentheses to indicate it's further specifying the purpose of the implementation. Therefore, the translation accurately conveys the meaning of "Sample MCP server implementation for fetching weather forecasts."

glif-mcp

鏡 (Kagami)

ConnectWise Manage MCP Server

ConnectWise Manage API連携のためのモデルコンテキストプロトコル（MCP）サーバー

Spring AI MCP Server 示例项目

generator-mcp

新しい MCP サーバーを迅速に作成するための Yeoman Generator

dice-mcp-server

Airbnb MCP Server (Enhanced)

Airbnbの検索とリスティング詳細のための強化されたMCPサーバー

MCP Server - Offers Prototype

Perplexity MCP Server

Claude CodeとClaude Desktopで使用するためのPerplexityのMCPサーバー。検索と推論の能力が向上します。

ntfy-mcp-server

MCP Servers for Cursor AI - README

私はMCP（Model Context Protocol）サーバーに関する多くの情報をスクレイピングしました。Cursor AIとClaude Desktopとの統合も含みます。これにより、このフォルダをお好みのIDEに追加して、MCPサーバーを正しく作成する方法を把握するためのコンテキストインデックス情報を持たせることができます。

gameanalytics-server MCP Server

GameAnalytics の Model Context Protocol (モデルコンテキストプロトコル) 統合のための MCP サーバー

Simple MCP Server Example

モデルコンテキストプロトコルサーバーの簡単な例です。

MCP Server Readability Parser (Python / FastMCP)

鏡 (Kagami)

Git

Gitリポジトリを読み込み、検索し、操作するためのツール

simple_mcp_server

簡単な MCP サーバーのテスト

ChatSum

Okay, I understand. To query and summarize our chat messages effectively, I need a way to access them. Since I am a large language model, I don't have a persistent memory of past conversations. Therefore, to fulfill your request, you would need to provide me with the chat history. You can do this by: 1. **Copying and pasting the relevant parts of our conversation into the prompt.** This is the most direct way. 2. **If the conversation is stored in a file, you can upload the file (if the platform allows) or copy and paste the contents.** Once you provide the chat history, please also specify: * **What kind of query are you looking for?** (e.g., "Find all instances where I asked you to translate something," "What did you say about summarizing text?") * **What kind of summary do you want?** (e.g., "A brief overview of the entire conversation," "A summary of the key points related to translation.") After you provide the chat history and specify your query and summary requirements, I will do my best to analyze the text and provide you with the information you need. I'm ready when you are!