Generate text using Google Gemini AI within a secure Node.js sandbox environment. Provide prompts to create content, select models, and control response length for coding and development tasks.
Generate text using Google Gemini. Provide a prompt and optional model name.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Prompt to send to Gemini | |
| model | No | Gemini model name | models/gemini-2.0-flash-exp |
| maxTokens | No | Maximum tokens in the response |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states basic functionality without disclosing behavioral traits like rate limits, authentication needs, response formats, or potential errors. It mentions optional model selection but doesn't explain implications or defaults, leaving gaps in transparency.
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 extremely concise with two short sentences that directly state the tool's purpose and required inputs. Every word earns its place, and it's front-loaded with the core functionality, making it efficient and well-structured.
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?
Given the complexity of AI text generation, no annotations, and no output schema, the description is incomplete. It lacks details on response handling, error cases, model defaults (though schema covers this), and behavioral aspects like token limits or safety considerations, making it inadequate for full contextual understanding.
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 100%, so the schema already documents all parameters (prompt, model, maxTokens). The description adds minimal value by mentioning 'prompt and optional model name' but doesn't provide additional meaning beyond the schema, such as prompt best practices or model selection guidance.
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 clearly states the action ('Generate text') and the resource/technology ('using Google Gemini'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools (none of which are text generation tools), so it doesn't reach the highest score of 5.
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 description provides minimal guidance with 'Provide a prompt and optional model name,' but lacks explicit when-to-use instructions, alternatives, or context about when this tool is preferred over others. No sibling tools are text generators, so differentiation isn't needed, but general usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.