# MCP Server Data Extractor — Extract Tool Schemas & Resources (`yourwingman/mcp-server-data-extractor`) Actor Connect to any MCP server and extract tool schemas, resource definitions, and prompt templates. Essential for AI agent development, function calling setup, and LLM tool integration. - **URL**: https://apify.com/yourwingman/mcp-server-data-extractor.md - **Developed by:** [Wingman](https://apify.com/yourwingman) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## MCP Server Data Extractor Connect to any MCP (Model Context Protocol) server and extract tool schemas, resource definitions, and prompt templates. ### Why This Actor? MCP (Model Context Protocol) is an open protocol that enables AI models to interact with external tools and data sources securely. This actor helps you discover and inspect MCP server capabilities: - **AI Agent Developers**: Quickly see what tools an MCP server offers - **LLM Integrators**: Get the JSON schemas you need for function calling - **Tool Builders**: Verify your MCP server's tool definitions - **System Administrators**: Audit MCP server endpoints ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `mcpServerUrl` | string | — | URL of the MCP server endpoint | | `requestTimeout` | integer | 30 | Timeout per request (seconds) | | `includeResources` | boolean | true | List available resources | | `includePrompts` | boolean | true | List available prompts | | `serverHeaders` | object | {} | Custom HTTP headers (auth tokens, etc.) | ### Output Each dataset entry represents a tool, resource, or prompt definition: | Field | Type | Description | |-------|------|-------------| | `type` | string | Entry type: `tool`, `resource`, or `prompt` | | `name` | string | Name of the tool/resource/prompt | | `description` | string | Human-readable description | | `inputSchema` | string | JSON schema for input parameters | | `serverName` | string | Name of the MCP server | | `serverVersion` | string | Server version | | `uri` | string | Resource URI (resources only) | | `mimeType` | string | Resource MIME type (resources only) | ### Example ```json { "mcpServerUrl": "https://mcp.example.com/api" } ```` ### Protocol Support - JSON-RPC 2.0 over HTTP POST - MCP protocol version 2024-11-05 - Supports `tools/list`, `resources/list`, `prompts/list` # Actor input Schema ## `mcpServerUrl` (type: `string`): URL of the MCP server endpoint (e.g., https://mcp.example.com/mcp). ## `requestTimeout` (type: `integer`): Timeout for each JSON-RPC request to the MCP server. ## `includeResources` (type: `boolean`): Whether to list available resources from the server. ## `includePrompts` (type: `boolean`): Whether to list available prompts from the server. ## `serverHeaders` (type: `object`): Additional HTTP headers to send with requests (e.g., API keys, auth tokens). ## Actor input object example ```json { "mcpServerUrl": "https://mcp.example.com/mcp", "requestTimeout": 30, "includeResources": true, "includePrompts": true, "serverHeaders": {} } ``` # Actor output Schema ## `type` (type: `string`): Whether this is a tool, resource, or prompt definition ## `name` (type: `string`): Name of the tool, resource, or prompt ## `description` (type: `string`): Description of what this entry does ## `inputSchema` (type: `string`): JSON Schema for the tool/prompt input parameters ## `serverName` (type: `string`): Name of the MCP server ## `serverVersion` (type: `string`): Version of the MCP server ## `uri` (type: `string`): URI template for resources (for resource entries only) ## `mimeType` (type: `string`): MIME type of the resource content # 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 = { "mcpServerUrl": "https://mcp.example.com/mcp" }; // Run the Actor and wait for it to finish const run = await client.actor("yourwingman/mcp-server-data-extractor").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 = { "mcpServerUrl": "https://mcp.example.com/mcp" } # Run the Actor and wait for it to finish run = client.actor("yourwingman/mcp-server-data-extractor").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 '{ "mcpServerUrl": "https://mcp.example.com/mcp" }' | apify call yourwingman/mcp-server-data-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=yourwingman/mcp-server-data-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server Data Extractor — Extract Tool Schemas & Resources", "description": "Connect to any MCP server and extract tool schemas, resource definitions, and prompt templates. Essential for AI agent development, function calling setup, and LLM tool integration.", "version": "1.0", "x-build-id": "2F5HSrKTC0VZr1ViV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/yourwingman~mcp-server-data-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-yourwingman-mcp-server-data-extractor", "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/yourwingman~mcp-server-data-extractor/runs": { "post": { "operationId": "runs-sync-yourwingman-mcp-server-data-extractor", "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/yourwingman~mcp-server-data-extractor/run-sync": { "post": { "operationId": "run-sync-yourwingman-mcp-server-data-extractor", "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": [ "mcpServerUrl" ], "properties": { "mcpServerUrl": { "title": "MCP Server URL", "maxLength": 1000, "type": "string", "description": "URL of the MCP server endpoint (e.g., https://mcp.example.com/mcp)." }, "requestTimeout": { "title": "Request Timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "Timeout for each JSON-RPC request to the MCP server.", "default": 30 }, "includeResources": { "title": "Include Resources", "type": "boolean", "description": "Whether to list available resources from the server.", "default": true }, "includePrompts": { "title": "Include Prompts", "type": "boolean", "description": "Whether to list available prompts from the server.", "default": true }, "serverHeaders": { "title": "Custom Headers", "type": "object", "description": "Additional HTTP headers to send with requests (e.g., API keys, auth tokens).", "properties": {}, "default": {} } } }, "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 } } } } } } } } } } ```