post_dataforseo_ai_gemini_llm_responses_liveLive Gemini LLM Responses endpoint allows you to retrieve structured responses from a specific Gemini AI model, based on the input parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / properties / body / items / properties / message_chain / descriptionPrevious value: -"conversation history optional field array of message objects representing previous conversation turns; each object must contain role and message parameters: role string with either user or ai role; message string with message content (max 500 characters); you can specify the maximum of 10 message objects in the array; example: \"message_chain\": [{\"role\":\"user\",\"message\":\"Hello, what’s up?\"},{\"role\":\"ai\",\"message\":\"Hello! I’m doing well, thank you. How can I assist you today?\"}]"New value: +"conversation history optional field array of message objects representing previous conversation turns; each object must contain: role string with either user or ai role; message string with message content (max 500 characters); you can specify maximum of 10 message objects in the array; Note: for Perplexity models, messages must strictly alternate between user and AI roles (user → ai); example: \"message_chain\": [{\"role\":\"user\",\"message\":\"Hello, what’s up?\"},{\"role\":\"ai\",\"message\":\"Hello! I’m doing well, thank you. How can I assist you today?\"}]"Input schema / properties / body / items / properties / message_chain / items / properties / message / descriptionAdded value: +"message text"Input schema / properties / body / items / properties / message_chain / items / properties / role / descriptionAdded value: +"role of the user from whom the message originates"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare openWorldHint=true, destructiveHint=true, and idempotentHint=false, so the safety profile is covered by structured data. The description adds nothing beyond that: no cost implications (the web_search schema note mentions pricing), no rate limits, no auth requirements, and no statement of what 'live' implies versus batch.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler, which is appropriately terse. It is arguably under-specified rather than bloated, but as pure conciseness it is clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a complex nested request body (prompt, model_name, message_chain, temperature, reasoning, token limits) and a POST with destructive/non-idempotent annotations. One generic sentence is not enough to orient an agent; output schema existence excuses explaining return values, but nothing else about this multi-field request is covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Top-level schema description coverage is 0% – the sole parameter 'body' is an array of request objects with no description at the parameter level. The description says only 'based on the input parameters,' which adds no meaning; it never explains that body accepts a list of prompts/tasks. The nested item fields are richly documented in the schema, but the description itself does not compensate for the top-level gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource ('retrieve structured responses from a specific Gemini AI model'), so the agent knows this is a generation/retrieval call against Gemini. It implicitly distinguishes itself from the ChatGPT/Claude/Perplexity live-response siblings by naming Gemini, but never explicitly contrasts them or explains the live-vs-scraper distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use guidance, no exclusions, and no mention of the alternatives (the ChatGPT/Claude/Perplexity live-response endpoints or the Gemini models endpoint). The only routing hint ('you can receive the list of available LLM models by making a separate request...') lives in the nested schema, not the description.
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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