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browser_run_task

Runs an entire web task in a real browser from plain-language instructions, reports progress, and pauses for human login, captcha, or payment before resuming.

Instructions

Carry out a whole web task in a real browser and report what happened. This takes minutes, not seconds, and reports progress as it goes. Describe the task in plain language; it works out the steps itself. If it hits a login, a captcha or a payment screen it stops and hands the browser to the person, then picks up where it left off once they are done. Needs a vision model configured (EMBIODY_VLLM_*); for a small job you can drive the browser yourself with browser_take_snapshot / browser_click / browser_input_text instead, which needs no model.

Input Schema

NameRequiredDescriptionDefault
taskYesThe web task the user wants done.
tabIdNoExisting tab ID. Pass it to resume the task in that tab.
historyNoResults of the steps executed so far.
windowIdNoExisting window ID. Pass it to resume the task in that window.
maxRoundsNo
humanReplyNoThe human's answer to the question the previous run paused on. Pass it together with windowId/tabId to carry the answer into the resumed run.
totalTimeoutMsNoOverall timeout for one task, in milliseconds. Defaults to 5 minutes.
closeTabWhenDoneNoClose the tab once the task finishes.
taskTreeOverviewNoTask tree as of the previous round.
currentDescriptionExNoSteps already executed locally in the previous round.
closeBrowserWindowWhenDoneNoClose the browser window this task runs in once it finishes. Other windows are unaffected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses duration ('takes minutes, not seconds'), progress reporting, autonomous step planning, mid-task pausing on login/captcha/payment with human handoff and resumption, and the vision-model dependency. It does not clarify failure behavior or what happens when maxRounds is exhausted, which keeps it below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each earning its place: purpose first, then duration expectation, then input style, then human handoff, then the model prerequisite and cheaper alternative. Nothing is repeated from the schema and the most decision-relevant facts are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an 11-parameter, long-running agentic tool with no annotations and no output schema, the description covers the important behavioral surface (duration, pausing, resumption, model requirement). The one gap is that with no output schema it never says what a completed run returns or how progress/errors are surfaced, which would matter for a caller parsing results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 91%, so the baseline is 3, but the description adds real meaning for the `task` parameter ('describe the task in plain language; it works out the steps itself') and explains the resume model that titles history/tabId/windowId/humanReply are used for. That goes beyond restating schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Carry out a whole web task in a real browser and report what happened.' It explicitly frames the tool as end-to-end task execution rather than a primitive action, which cleanly separates it from the many browser_click / browser_navigate siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('a whole web task'), when-not-to-use ('for a small job you can drive the browser yourself'), the alternative siblings by name (browser_take_snapshot / browser_click / browser_input_text), and the prerequisite (vision model via EMBIODY_VLLM_*). It also documents the human-handoff path for login/captcha/payment.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.