Scrapingdog MCP Server

Google AI Mode API

google_ai_mode

Fetch Google AI Mode's conversational answer for a query, with options for geo-targeting, language, and adult-content filtering.

Instructions

Retrieve Google's AI Mode conversational answer for a query.

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": {
      "query": "<query>"
    },
    "name": "google_ai_mode"
  }
}

Input Schema

Name	Required	Description
`html`	No	Return raw HTML instead of parsed JSON. (API default: false)
`safe`	No	Adult-content filter. (API default: off)
`query`	Yes	The query to search in Google AI Mode.
`country`	No	Two-letter ISO country code to geo-target results (e.g. us, gb, in, de). (API default: us)
`language`	No	Result language code (e.g. en, es, fr, de). (API default: en)
`location`	No	Search origin location. Incompatible with uule.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Aug 1, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It implies a read-only retrieval action but does not mention response format, authentication requirements, rate limits, or any side effects. The phrase 'conversational answer' gives a hint but not enough detail about the actual output structure.

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

Conciseness4/5

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

The description is a single, front-loaded sentence with no wasted words. It efficiently states the core action and resource. While it is terse, that is not a structural flaw; the under-specification is penalized in other dimensions.

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

Completeness2/5

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

Despite complete schema coverage, the tool lacks an output schema and annotations. The description gives no indication of what the retrieved 'conversational answer' looks like (e.g., text, JSON structure, citations). This is a critical gap for an agent to correctly process the tool's result. Usage guidance is also absent, making the overall context incomplete.

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?

The input schema has 100% coverage with each parameter (query, html, safe, country, language, location) having a meaningful description. The tool description itself adds no extra parameter context, but the schema fully compensates, so a baseline score of 3 is appropriate.

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

Purpose4/5

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

The description uses a specific verb 'Retrieve' with a clear resource ('Google's AI Mode conversational answer') and a query object. It is clear and unambiguous, though it does not explicitly contrast with the sibling tool google_ai_overview, which may have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as google_search or google_ai_overview. There is no mention of scenarios, exclusions, or preferred use cases, so the agent gets no decision support.

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