Open Google Image Generator MCP

by miracorhan

Overview Schema Source Related Servers Discussions Admin

Python

Local

tool_live_generate

Generates text from a prompt using Gemini streaming and returns the full accumulated response.

Instructions

Generate text using Gemini streaming. The full streamed response is accumulated and returned.

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": {
      "params": "<params>"
    },
    "name": "tool_live_generate"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.3.0Oct 18, 2026

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It does add one genuinely useful behavioral fact — that the stream is accumulated server-side and returned whole rather than as incremental chunks — but it omits auth requirements, latency, rate limits, error behavior, and the default model actually used.

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?

Two sentences with no filler, and the key behavioral fact is front-loaded after the purpose statement. It is lean to the point of under-specification for a tool that exposes a model-tier choice.

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?

No output schema and no annotations mean the description must explain return shape and side effects, which it does only partially. For a generation tool with a tier selector, the agent gets no guidance on tier choice, expected latency, or failure modes.

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

Parameters2/5

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

Schema description coverage is reported at 0% at the top level, so the description would need to compensate, and it says nothing about prompt, model, or model_tier. The model_tier semantics and the 'Live API-only models are not supported' restriction appear only in the nested schema, not in the description.

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 states a specific verb and resource ('Generate text using Gemini streaming'), which distinguishes it from the image/video/audio generation siblings. It does not, however, differentiate it from the other text-producing tools such as tool_batch_generate or tool_run_pipeline.

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?

There is no when-to-use guidance, no prerequisites, and no named alternative. An agent cannot tell from the description whether to prefer this over tool_batch_generate for multiple prompts or tool_list_available_models to pick a model first.

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