chrome-bridge-mcp

by deng1986

Overview Schema Source Related Servers Discussions Admin

ask_ai_page

Submit prompts to browser-based AI interfaces and read responses. Identifies login or CAPTCHA pages needing user action.

Instructions

Submit a prompt to a generic browser AI page and read the response. Login/CAPTCHA pages return needsUser=true.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "inputSelector": "<inputSelector>",
      "submitSelector": "<submitSelector>",
      "responseSelector": "<responseSelector>"
    },
    "name": "ask_ai_page"
  }
}

Input Schema

Name	Required	Description
`prompt`	Yes	Prompt to submit.
`inputSelector`	Yes	CSS selector for the prompt input.
`submitSelector`	Yes	CSS selector for the submit button.
`responseSelector`	Yes	CSS selector for the answer container.
`tabId`	No	Optional Chrome tab id. Defaults to the first page tab.
`timeoutMs`	No	Maximum time to wait for the response.
`blockedSelectors`	No	Selectors that mean human intervention is needed.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 23, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that login/CAPTCHA pages return needsUser=true, a key behavioral trait. However, it does not explain other behaviors like whether the tool modifies the page or waits for a response beyond timeoutMs.

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?

Two sentences packed with essential information: core function and a critical conditional return behavior. No fluff, every word earns its place.

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

Completeness3/5

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

Given 7 parameters (4 required) and no output schema, the description is minimal. It fails to explain the normal response format (likely text from responseSelector) or what happens on success vs failure beyond needsUser. More detail is needed for a tool with this complexity.

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% (all 7 parameters have descriptions). The tool description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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 clearly states 'Submit a prompt to a generic browser AI page and read the response.' This distinguishes it from sibling tools like google_ai_ask, which is specific to Google AI. The mention of login/CAPTCHA pages adds specific use-case differentiation.

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

Usage Guidelines3/5

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

The description implies usage for generic AI pages and mentions login/CAPTCHA conditions, but does not explicitly state when to use this tool versus alternatives like google_ai_ask, fill_text, or detect_human_intervention. No exclusions or when-not-to-use guidance is provided.

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