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pony-browser-mcp

pony-browser-mcp

by pssrh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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browser_solve_captcha

Detect and solve CAPTCHAs on the current page using auto-click, AI vision, and human fallback to clear reCAPTCHA, hCaptcha, Turnstile, and FunCaptcha challenges.

Instructions

Detect and solve CAPTCHAs on the current page. Auto-detects reCAPTCHA v2/v3, hCaptcha, Cloudflare Turnstile, and FunCaptcha. Tries auto-click first (often clears reCAPTCHA v2 when signed into Google), then returns a screenshot for AI vision analysis, then falls back to asking the user. Returns detection info and solving status.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "browser_solve_captcha"
  }
}

Input Schema

NameRequiredDescriptionDefault
cellsNoGrid cell indices to click (0-indexed, left-to-right, top-to-bottom) for image challenges. E.g. [2, 5, 7] to click cells 3, 6, 8.
actionNoAction to take. "detect" scans for CAPTCHAs. "click_checkbox" clicks the reCAPTCHA checkbox. "click_grid" clicks specific grid cells (pass cells param). "ask_human" shows overlay to user. Default: "detect"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the multi-step approach: auto-click first, screenshot for AI vision, then ask the user. It also names supported CAPTCHA types and notes that it returns detection info and solving status. There is slight ambiguity around how the screenshot and user fallback flow between calls, but the core behavior is transparent.

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?

The description is four sentences, each earning its place: it names the operation, lists supported CAPTCHA types, outlines the solving flow with a practical detail about Google sign-in, and states the return value. It is front-loaded and free of filler.

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?

The description covers supported CAPTCHA types, the solving strategy, fallback behavior, and return value, which is strong given there is no output schema. It does not specify the exact shape of detection info or solving status, but the high-level description combined with the well-documented optional parameters is sufficient for an agent to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already explains both parameters and the action enum clearly. The tool description provides useful narrative context about solving strategy but adds no additional parameter-level meaning beyond what the schema already states.

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?

The description opens with 'Detect and solve CAPTCHAs on the current page', clearly specifying the verb, resource, and scope. It names the specific CAPTCHA types handled, which differentiates it from every generic browser interaction sibling such as browser_click or browser_screenshot.

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

Usage Guidelines4/5

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

The description gives clear context for when to use the tool: whenever a CAPTCHA appears, and it explains the solving order from auto-click to AI vision to human fallback. It does not explicitly name alternatives or state when not to use it, but the specialized purpose makes the intended use obvious.

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

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