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tusharpatil2912

Pollinations Multimodal MCP Server

listImageModels

Discover available AI image generation models to select the right one for your creative projects.

Instructions

List available image models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for listImageModels that fetches the list of available image models from the Pollinations API and returns it in MCP format.
    async function listImageModels(params) {
        try {
            const url = buildUrl(IMAGE_API_BASE_URL, "models");
            const response = await fetch(url);
    
            if (!response.ok) {
                throw new Error(`Failed to list models: ${response.statusText}`);
            }
    
            const models = await response.json();
    
            // Return the response in MCP format
            return createMCPResponse([createTextContent(models, true)]);
        } catch (error) {
            console.error("Error listing image models:", error);
            throw error;
        }
    }
  • Registration of the listImageModels tool within the imageTools export array, with no input schema defined.
        ["listImageModels", "List available image models", {}, listImageModels],
    ];
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It implies a read-only operation but doesn't specify whether it requires authentication, returns paginated results, includes metadata (e.g., model capabilities), or has rate limits. This leaves significant gaps for a tool that likely interacts with an external API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to scan and understand immediately.

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

Completeness3/5

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

Given the tool's low complexity (0 parameters, no output schema) and lack of annotations, the description is minimally adequate but incomplete. It covers the basic purpose but misses behavioral context (e.g., authentication needs, return format), which is important for a tool that likely fetches dynamic data from an external service.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could theoretically mention implicit parameters (e.g., filtering options), earning a baseline score just below perfect.

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 clearly states the verb ('List') and resource ('available image models'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'listAudioVoices' or 'listTextModels' beyond specifying the resource type, which keeps it from a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication status), compare it to siblings like 'generateImage' for model selection, or indicate typical use cases (e.g., before generating images).

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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