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2234839

Martin MCP Toolbox

by 2234839

pollinations_generate_text

Generate AI text responses using Pollinations.AI API with customizable model, temperature, and sampling parameters for tailored outputs.

Instructions

Generate text using Pollinations.AI API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsonNoReturn response as JSON string (default: false)
seedNoSeed for reproducible results
modelNoModel for text generation (default: openai)
top_pNoNucleus sampling parameter (0.0-1.0)
promptYesText prompt for the AI
streamNoEnable streaming responses (default: false)
systemNoSystem prompt to guide AI behavior
privateNoPrevent response from appearing in public feed
temperatureNoControls randomness (0.0-3.0)
presence_penaltyNoPenalizes tokens based on presence
frequency_penaltyNoPenalizes tokens based on frequency
Behavior1/5

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

There are no annotations, so the description carries the full burden of disclosing behavioral traits. It only restates the action without mentioning rate limits, streaming behavior, privacy implications, or response format. This provides no transparency beyond the basic function.

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 concise sentence with no filler or redundant information. It is appropriately brief, though it could benefit from additional structure or elaboration given the tool's complexity.

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

Completeness1/5

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

With 11 parameters, no annotations, and no output schema, the description is severely incomplete. It fails to explain any behavioral context, return values, or usage scenarios, making it inadequate for an agent to understand the tool's full capabilities and side effects.

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?

Schema coverage is 100%, so all 11 parameters have descriptions in the schema. The tool description adds no additional meaning beyond what the schema already provides, 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.

Purpose5/5

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

The description clearly states the tool generates text using the Pollinations.AI API. This distinguishes it from sibling tools like pollinations_generate_image and pollinations_generate_audio, which generate other media types.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention any exclusions, prerequisites, or alternative tools, leaving the agent to infer usage solely from the tool name.

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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