# Wolfram MCP Server (`agentify/wolfram-mcp-server`) Actor The Wolfram MCP Service provides immediate consumer-level access to Wolfram's sophisticated computations and trusted knowledge base. - **URL**: https://apify.com/agentify/wolfram-mcp-server.md - **Developed by:** [agentify](https://apify.com/agentify) (community) - **Categories:** MCP servers, AI, Agents - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 tool requests 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 ### Wolfram MCP Server The Wolfram MCP Service provides immediate consumer-level access to Wolfram's sophisticated computations and trusted knowledgebase. This Apify Actor exposes Wolfram's hosted MCP service ([wolfram.com/artificial-intelligence/mcp-service](https://www.wolfram.com/artificial-intelligence/mcp-service/)) over a streamable HTTP endpoint so any MCP client can connect via an Apify URL with Bearer auth. **About this MCP Server:** To understand how to connect to and utilize this MCP server, please refer to the official Model Context Protocol documentation at [mcp.apify.com](https://mcp.apify.com). ### Connection URL MCP clients can connect to this server at: ```text https://mcp-servers--wolfram-mcp-server.apify.actor/mcp ```` ### Client Configuration To connect to this MCP server, use the following configuration in your MCP client: ```json { "mcpServers": { "wolfram": { "url": "https://mcp-servers--wolfram-mcp-server.apify.actor/mcp", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` **Note:** Replace `YOUR_APIFY_TOKEN` with your actual Apify API token. You can find your token in the [Apify Console](https://console.apify.com/account/integrations). ### 🚩 Claim this MCP server All credits to the original authors of https://www.wolfram.com/artificial-intelligence/mcp-service/ To claim this server, please write to . *** ### Available Tools | Tool | Description | |------|-------------| | `WolframContext` | Semantic search over Wolfram resources. Call this first in a new conversation (or whenever the topic shifts) to ground subsequent tool calls with up-to-date relevant information. Input is natural-language context, not a search query. | | `WolframLanguageEvaluator` | Evaluate Wolfram Language code in a kernel. Supports symbolic (`Solve[...]`), numerical (`N[Pi, 50]`), and unit-aware (`UnitConvert[...]`) computations. Accepts `\[FreeformPrompt]["..."]` for natural-language input that expands like a macro before evaluation. Optional `timeConstraint` (seconds, default 60). | | `WolframAlpha` | Natural-language Wolfram|Alpha queries for computational answers across chemistry, physics, geography, history, art, astronomy, and more. | #### ✨ Example Usage ##### Symbolic & numerical math > "Solve `x^2 + 3x - 4 == 0` for x, then give me the numerical value of `Pi` to 50 digits." ##### Real-world data & units > "Convert 100 miles to kilometers, and tell me the current population of Tokyo." ##### Live computational knowledge > "What's the distance from Earth to Mars right now, and what's the molecular weight of caffeine?" ### References To learn more about Apify and Actors, take a look at the following resources: - [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js) - [Apify SDK for Python documentation](https://docs.apify.com/sdk/python) - [Apify Platform documentation](https://docs.apify.com/platform) - [Apify MCP Server](https://docs.apify.com/platform/integrations/mcp) - [Webinar: Building and Monetizing MCP Servers on Apify](https://www.youtube.com/watch?v=w3AH3jIrXXo) - [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU) # Actor input Schema ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("agentify/wolfram-mcp-server").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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("agentify/wolfram-mcp-server").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call agentify/wolfram-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=agentify/wolfram-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wolfram MCP Server", "description": "The Wolfram MCP Service provides immediate consumer-level access to Wolfram's sophisticated computations and trusted knowledge base.", "version": "0.0", "x-build-id": "4ABVTeqZXAZdVgJav" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/agentify~wolfram-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-agentify-wolfram-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/agentify~wolfram-mcp-server/runs": { "post": { "operationId": "runs-sync-agentify-wolfram-mcp-server", "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/agentify~wolfram-mcp-server/run-sync": { "post": { "operationId": "run-sync-agentify-wolfram-mcp-server", "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", "properties": {} }, "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 } } } } } } } } } } ```