# Apify Mcp Server Tools — Developer Tools & Integration (`oneary/apify-mcp-server-tools`) Actor Extract structured data and technical information from Apify Mcp Server Tools. Ideal for developers, analysts, and data enthusiasts. - **URL**: https://apify.com/oneary/apify-mcp-server-tools.md - **Developed by:** [Luan M.](https://apify.com/oneary) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Apify MCP Server Tools Expose Apify actors as MCP (Model Context Protocol) tools. Connect to Claude, Cursor, VS Code, Cline, and other MCP-compatible AI assistants. ### Features - **MCP-compatible**: Works with any MCP client (Claude Desktop, Cursor, VS Code, Cline) - **Actor integration**: Use any Apify actor as an AI tool - **Auto-discovery**: Generates MCP server manifest automatically - **Demo crawl**: Test data extraction on provided URLs - **Flexible input**: JSON, URL, or plain text query ### Input | Field | Type | Description | |-------|------|-------------| | actorIds | Array | Apify actors to expose as MCP tools (username/actor-name or ID) | | query | String | Input data (JSON string, URL, or search query) | | outputFormat | String | Output format (default: json) | | proxyConfiguration | Object | Proxy settings | ### Output | Field | Type | Description | |-------|------|-------------| | version | String | MCP protocol version | | mcpServers | Object | MCP server config (copy to Claude/Cursor config) | | mcpTools | Array | Tool definitions with usage prompts | | demoRunResults | Array | Demo crawl results from provided URLs | | usage | Object | Alternative ways to call the actors | ### Setup #### Claude Desktop Add to `claude_desktop_config.json`: ```json { "mcpServers": { "apify-mcp": { "command": "node", "args": ["path/to/this-actor/src/main.js"] } } } ```` #### VS Code / Cursor Add to `.vscode/mcp.json`: ```json { "servers": { "apify-mcp": { "type": "stdio", "command": "node", "args": ["path/to/this-actor/src/main.js"] } } } ``` ### Use Cases - AI-powered web research with Apify actors - Automated data extraction via chat - Integration with existing MCP workflows - Multi-actor pipeline orchestration ### Keywords mcp, model context protocol, apify, tools, ai assistant, claude, cursor, vs code, cline, function calling, tool use ### Pricing $5.00 / 1,000 results # Actor input Schema ## `actorIds` (type: `array`): List of Apify actor IDs or names to expose as MCP tools. Use format: username/actor-name or actor-ID ## `query` (type: `string`): Input data to pass to the selected actors (JSON string, URL, or search query) ## `outputFormat` (type: `string`): Desired output format ## `proxyConfiguration` (type: `object`): Proxy settings ## Actor input object example ```json { "actorIds": [ { "actorRef": "apify/web-scraper" } ], "query": "{\"startUrls\": [{\"url\": \"https://example.com\"}], \"maxPages\": 5}", "outputFormat": "json", "proxyConfiguration": { "useApifyProxy": true } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "actorIds": [ { "actorRef": "apify/web-scraper" } ], "query": "{\"startUrls\": [{\"url\": \"https://example.com\"}], \"maxPages\": 5}", "outputFormat": "json", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("oneary/apify-mcp-server-tools").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "actorIds": [{ "actorRef": "apify/web-scraper" }], "query": "{\"startUrls\": [{\"url\": \"https://example.com\"}], \"maxPages\": 5}", "outputFormat": "json", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("oneary/apify-mcp-server-tools").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "actorIds": [ { "actorRef": "apify/web-scraper" } ], "query": "{\\"startUrls\\": [{\\"url\\": \\"https://example.com\\"}], \\"maxPages\\": 5}", "outputFormat": "json", "proxyConfiguration": { "useApifyProxy": true } }' | apify call oneary/apify-mcp-server-tools --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oneary/apify-mcp-server-tools", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Apify Mcp Server Tools — Developer Tools & Integration", "description": "Extract structured data and technical information from Apify Mcp Server Tools. Ideal for developers, analysts, and data enthusiasts.", "version": "0.1", "x-build-id": "iTDKfnoPjeWZJqJf3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oneary~apify-mcp-server-tools/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oneary-apify-mcp-server-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oneary~apify-mcp-server-tools/runs": { "post": { "operationId": "runs-sync-oneary-apify-mcp-server-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oneary~apify-mcp-server-tools/run-sync": { "post": { "operationId": "run-sync-oneary-apify-mcp-server-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "actorIds", "query" ], "properties": { "actorIds": { "title": "Apify Actor IDs", "type": "array", "description": "List of Apify actor IDs or names to expose as MCP tools. Use format: username/actor-name or actor-ID" }, "query": { "title": "Search query or input data", "type": "string", "description": "Input data to pass to the selected actors (JSON string, URL, or search query)" }, "outputFormat": { "title": "Output format", "type": "string", "description": "Desired output format", "default": "json" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings", "default": { "useApifyProxy": true } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```