AgentQL MCP Server

Overview

The AgentQL MCP Server bridges the gap between AI assistants and real‑world web data by exposing a single, well‑defined tool—. This tool empowers assistants to pull structured information directly from any publicly accessible URL, guided by a natural‑language prompt that describes the desired fields and format. For developers, this means eliminating manual scraping pipelines or building custom parsers; instead, a conversational AI can request precise data in one go and receive clean JSON ready for downstream processing.

By integrating AgentQL’s extraction engine into the MCP framework, the server offers a reliable and scalable way to enrich AI workflows. Developers can embed this capability into IDEs, chat interfaces, or automation scripts with minimal configuration. The server handles authentication via an API key, routing requests to AgentQL’s cloud service, and returning results that adhere to the MCP tool contract. This seamless plug‑in model keeps the AI’s context lightweight while delegating heavy lifting to a specialized extraction service.

Key features include:

• Prompt‑driven field selection – The argument lets users specify exactly which data points to retrieve, enabling highly targeted queries without exposing raw scraping logic.
• URL‑based extraction – Any reachable web page can be processed, making the tool suitable for e‑commerce listings, news articles, or public datasets.
• JSON output – Results are returned in a structured format that can be directly consumed by downstream tools or stored for analytics.
• MCP compatibility – The server follows the standard MCP specification, so it can be added to any client that supports MCP (Claude Desktop, VS Code, Cursor, Windsurf, etc.) with a single configuration entry.

Real‑world scenarios that benefit from AgentQL include: automated market research where an assistant gathers competitor pricing; content curation bots that pull metadata from blog posts; or internal knowledge bases that stay up‑to‑date by regularly extracting data from partner sites. In each case, the assistant can ask for “extract product names and prices from this URL” and receive a ready‑to‑use dataset, drastically reducing development time.

Because the extraction logic resides on AgentQL’s infrastructure, developers avoid the overhead of maintaining headless browsers or handling anti‑scraping measures. The MCP server acts as a lightweight wrapper, ensuring that the AI’s conversational flow remains uninterrupted while still accessing rich, structured web data. This combination of ease of integration, precise control over extracted fields, and robust cloud backing makes AgentQL a standout choice for any project that needs reliable web data extraction within an AI‑first workflow.