Wikipedia MCP Server (Apify Actor) Model Context Protocol (MCP) server for Wikipedia, packaged as an Apify Actor. Provides LLMs with real-time access to Wikipedia information. ## 🏃‍♂️ Quickstart (Local) sh # Install dependencies pip install -r requirements.txt # Run the Actor (starts the MCP server via stdio) python src/main.py ## 🛠️ Apify Actor Usage This Actor launches the Wikipedia MCP server using stdio transport, suitable for Claude Desktop and other MCP clients. ### Client connection example json { "mcpServers": { "wikipedia": { "type": "stdio", "command": "python", "args": ["src/main.py"] } } } ### Environment variables - WIKIPEDIA_LANGUAGE (optional): Set the Wikipedia language (e.g., en, ja). ## 💸 Pay-Per-Event (PPE) Mapping See .actor/pay_per_event.json for tool pricing. Example (extremely cheap): json { "actor-start": { "eventPriceUsd": 0.00001 }, "search_wikipedia": { "eventPriceUsd": 0.00001 }, "get_article": { "eventPriceUsd": 0.00001 }, "get_summary": { "eventPriceUsd": 0.00001 }, "get_sections": { "eventPriceUsd": 0.00001 }, "get_links": { "eventPriceUsd": 0.00001 }, "get_related_topics": { "eventPriceUsd": 0.00001 }, "summarize_article_for_query": { "eventPriceUsd": 0.00001 }, "summarize_article_section": { "eventPriceUsd": 0.00001 }, "extract_key_facts": { "eventPriceUsd": 0.00001 } } ## 📝 Features - Search Wikipedia articles - Retrieve article content, summaries, sections, links, related topics - Multi-language support ## 🔗 Resources - MCP Protocol docs - Wikipedia MCP server upstream - Apify MCP servers ## 🏷️ Credits - Wikipedia MCP server by rudra-ravi - Apify Actorization by Apify ## 🧪 Testing sh python src/main.py # Or run via Apify CLI: apify run ## 🛡️ Security See MseeP.ai Security Assessment Badge The template supports multiple charging approaches that you can customize based on your needs: #### 1. Generic MCP charging Charge for standard MCP operations with flat rates: json { "actor-start": { "eventTitle": "MCP server startup", "eventDescription": "Initial fee for starting the Actor MCP Server", "eventPriceUsd": 0.1 }, "tool-call": { "eventTitle": "MCP tool call", "eventDescription": "Fee for executing MCP tools", "eventPriceUsd": 0.05 }, "resource-read": { "eventTitle": "MCP resource access", "eventDescription": "Fee for accessing full content or resources", "eventPriceUsd": 0.0001 }, "prompt-get": { "eventTitle": "MCP prompt processing", "eventDescription": "Fee for processing AI prompts", "eventPriceUsd": 0.0001 } } #### 2. Domain-specific charging (arXiv example) Charge different amounts for different tools based on computational cost: json { "actor-start": { "eventTitle": "arXiv MCP server startup", "eventDescription": "Initial fee for starting the arXiv MCP Server Actor", "eventPriceUsd": 0.1 }, "search_papers": { "eventTitle": "arXiv paper search", "eventDescription": "Fee for searching papers on arXiv", "eventPriceUsd": 0.001 }, "list_papers": { "eventTitle": "arXiv paper listing", "eventDescription": "Fee for listing available papers", "eventPriceUsd": 0.001 }, "download_paper": { "eventTitle": "arXiv paper download", "eventDescription": "Fee for downloading a paper from arXiv", "eventPriceUsd": 0.001 }, "read_paper": { "eventTitle": "arXiv paper reading", "eventDescription": "Fee for reading the full content of a paper", "eventPriceUsd": 0.01 } } #### 3. No charging (free service) Comment out all charging lines in the code for a free service. ### How to implement charging 1. Define your events in .actor/pay_per_event.json (see examples above). This file is not actually used at Apify platform but serves as a reference. 2. Enable charging in code by uncommenting the appropriate lines in src/mcp_gateway.py: python # For generic charging: await charge_mcp_operation(actor_charge_function, ChargeEvents.TOOL_CALL) # For domain-specific charging: if tool_name == 'search_papers': await charge_mcp_operation(actor_charge_function, ChargeEvents.SEARCH_PAPERS) 3. Add custom events to src/const.py if needed: python class ChargeEvents(str, Enum): # Your custom events CUSTOM_OPERATION = 'custom-operation' 4. Set up PPE model on Apify: - Go to your Actor's Publication settings - Set the Pricing model to Pay per event - Add your pricing schema from pay_per_event.json ### Authorized tools This template includes tool authorization - only tools listed in src/const.py can be executed: Note: The TOOL_WHITELIST dictionary only applies to tools (executable functions). Prompts (like deep-paper-analysis) are handled separately and don't need to be added to this list. Tool whitelist for MCP server Only tools listed here will be present to the user and allowed to execute. Format of the dictionary: {tool_name: (charge_event_name, default_count)} To add new authorized tools, add an entry with the tool name and its charging configuration. python TOOL_WHITELIST = { ChargeEvents.SEARCH_PAPERS.value: (ChargeEvents.SEARCH_PAPERS.value, 1), ChargeEvents.LIST_PAPERS.value: (ChargeEvents.LIST_PAPERS.value, 1), ChargeEvents.DOWNLOAD_PAPER.value: (ChargeEvents.DOWNLOAD_PAPER.value, 1), ChargeEvents.READ_PAPER.value: (ChargeEvents.READ_PAPER.value, 1), } To add new tools: 1. Add charge event to ChargeEvents enum 2. Add tool entry to TOOL_WHITELIST dictionary with format: tool_name: (event_name, count) 3. Update pricing in pay_per_event.json 4. Update pricing at Apify platform Unauthorized tools are blocked with clear error messages. ## 🔧 How it works This template implements a MCP gateway that can connect to a stdio-based, Streamable HTTP, or SSE-based MCP server and expose it via Streamable HTTP transport. Here's how it works: ### Server types 1. Stdio server (StdioServerParameters): - Spawns a local process that implements the MCP protocol over stdio. - Configure using the command parameter to specify the executable and the args parameter for additional arguments. - Optionally, use the env parameter to pass environment variables to the process. Example: python server_type = ServerType.STDIO MCP_SERVER_PARAMS = StdioServerParameters( command='uv', args=['run', 'arxiv-mcp-server'], env={'YOUR_ENV_VAR': os.getenv('YOUR-ENV-VAR')}, # Optional environment variables ) 2. Remote server (RemoteServerParameters): - Connects to a remote MCP server via HTTP or SSE transport. - Configure using the url parameter to specify the server's endpoint. - Set the appropriate server_type (ServerType.HTTP or ServerType.SSE). - Optionally, use the headers parameter to include custom headers (e.g., for authentication) and the auth parameter for additional authentication mechanisms. Example: python server_type = ServerType.HTTP MCP_SERVER_PARAMS = RemoteServerParameters( url='https://mcp.apify.com', headers={'Authorization': 'Bearer YOUR-API-KEY'}, # Replace with your authentication token ) Note: SSE transport is also supported by setting server_type = ServerType.SSE. - Tips: - Ensure the remote server supports the transport type you're using and is accessible from the Actor's environment. - Use environment variables to securely store sensitive information like tokens or API keys. #### Environment variables: Environment variables can be securely stored and managed at the Actor level on the Apify platform. These variables are automatically injected into the Actor's runtime environment, allowing you to: - Keep sensitive information like API keys secure. - Simplify configuration by avoiding hardcoded values in your code. ### Gateway implementation The MCP gateway (create_gateway function) handles: - Creating a Starlette web server with Streamable HTTP (/mcp) endpoint - Managing connections to the underlying MCP server - Forwarding requests and responses between clients and the MCP server - Handling charging through the actor_charge_function (Actor.charge in Apify Actors) - Tool authorization: Only allowing whitelisted tools to execute - Access control: Blocking unauthorized tool calls with clear error messages Key components: - create_gateway: Creates an MCP server instance that acts as a gateway - charge_mcp_operation: Handles charging for different MCP operations - TOOL_WHITELIST: Dictionary mapping tool names to (event_name, count) tuples for authorization and charging ### MCP operations The MCP gateway supports all standard MCP operations: - list_tools(): List available tools - call_tool(): Execute a tool with arguments - list_prompts(): List available prompts - get_prompt(): Get a specific prompt - list_resources(): List available resources - read_resource(): Read a specific resource Each operation can be configured for charging in the PPE model. ## 📚 Resources - What is Anthropic's Model Context Protocol? - How to use MCP with Apify Actors - Apify MCP server - Apify MCP server documentation - Apify MCP client - MCP Servers hosted at Apify - Model Context Protocol documentation - Apify SDK documentation ## Getting started For complete information see this article. To run the Actor use the following command: bash apify run ## Deploy to Apify ### Connect Git repository to Apify If you've created a Git repository for the project, you can easily connect to Apify: 1. Go to Actor creation page 2. Click on Link Git Repository button ### Push project on your local machine to Apify You can also deploy the project on your local machine to Apify without the need for the Git repository. 1. Log in to Apify. You will need to provide your Apify API Token to complete this action. bash apify login 2. Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under Actors -> My Actors. bash apify push ## Documentation reference To learn more about Apify and Actors, take a look at the following resources: - Apify SDK for JavaScript documentation - Apify SDK for Python documentation - Apify Platform documentation - Join our developer community on Discord