AI & MODELS · MCP

AgentQL MCP

Give AI agents a semantic eye on the live web through AgentQL over MCP

Overview

Connecting an autonomous agent to AgentQL through the Model Context Protocol lets it query live web pages in natural language and receive clean structured data mid-task. The agent can locate elements, extract fields, and read the current state of a page during a browser session, turning open-ended web research and automation into reliable, structured steps.

What agents can do

Extract structured data

Agents issue a semantic query against a live page and get back typed JSON — prices, listings, tables, contact fields — without writing selectors.

Locate interactive elements

Agents resolve the right button, input, or link by description so they can act on the correct control across changing layouts.

Read live page state

Agents inspect the current rendered page to confirm results, detect changes, and decide their next step.

Navigate multi-step flows

Agents chain queries across pagination, search, and form steps to gather data that spans several page transitions.

Agentic workflows we build

Autonomous research agents

We build agents that use AgentQL over MCP to gather structured facts from many sources on demand — pulling comparable specs, prices, or profiles into a single answer instead of returning raw HTML.

Self-maintaining monitoring agents

Agents watch target pages, extract the fields that matter, and flag changes, adapting to redesigns automatically because AgentQL resolves elements by meaning rather than fixed paths.

Task-completing web automations

We give agents AgentQL to identify and act on the right controls, so they can log in, search, fill forms, and complete real web tasks end to end within a governed browser session.

INTEGRATIONBuilding with AgentQLSee the integration →THE PRACTICEAI & Machine LearningExplore the service →

FAQ

What can an agent actually do with AgentQL through MCP?

It can query a live page in natural language to extract structured data, locate specific interactive elements by description, and read the rendered page state — the building blocks for both research and hands-on web automation, returned as JSON the agent can reason over.

Why route web access through AgentQL instead of raw HTML?

Raw HTML forces the model to parse noisy markup and guess at structure. AgentQL returns clean, typed fields keyed to what the agent asked for, which cuts token usage, reduces errors, and keeps working when target sites change their layout.

How do you keep agent-driven web actions safe and governed?

We run agents in monitored browser sessions with scoped permissions, schema validation on every extraction, and guardrails around which sites and actions are allowed, so autonomous flows stay auditable and within bounds.

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