vertex.gemini.generateGenerates creative copy and long-form text from prompts with Gemini for Quantum ADS MCP marketing workflows, enabling content creation while accounting for Vertex generation costs.
Generate text with Gemini (creative copy/long-form). Incurs Vertex generation cost.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | gemini-3.8-flash | |
| prompt | Yes | ||
| max_tokens | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool incurs Vertex generation cost, which is useful, but does not describe latency, quotas, safety filtering, determinism, permissions, or side effects expected for a generative AI call.
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?
The description is two short sentences with no wasted text and front-loads the core action. It is appropriately concise, though the brevity contributes to missing important usage and parameter context.
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 is a generative AI operation with no annotations, 0% schema description coverage, and multiple siblings. The description mentions cost but omits parameter meaning, alternatives, and operational constraints, leaving it under-specified for correct invocation.
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?
Schema description coverage is 0% and all three parameters ('model', 'prompt', 'max_tokens') lack schema descriptions. The description adds no meaning about model choices, prompt formatting, token limits, or defaults, so it fails to compensate for the undocumented parameters.
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 states a specific verb and resource ('Generate text with Gemini') and adds a use-case scope ('creative copy/long-form'). It does not explicitly distinguish the tool from the sibling 'vertex.gemini.generate_ad_copy', so it falls short of full sibling differentiation.
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?
The parenthetical '(creative copy/long-form)' implies when this tool is appropriate, but there is no explicit guidance about alternatives such as 'vertex.gemini.generate_ad_copy' or when not to use this tool. Usage is suggested rather than clearly routed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.