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tusharpatil2912

Pollinations Multimodal MCP Server

listTextModels

Discover available text generation models to select the right one for your content creation needs within the Pollinations multimodal toolkit.

Instructions

List available text models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'listTextModels' tool. It fetches the list of available text models from the Pollinations Text API at '/models' endpoint, parses the JSON response, and returns it formatted as an MCP response using createMCPResponse and createTextContent.
    async function listTextModels(params) {
        try {
            const url = buildUrl(TEXT_API_BASE_URL, "models");
            const response = await fetch(url);
    
            if (!response.ok) {
                throw new Error(
                    `Failed to list text 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 text models:", error);
            throw error;
        }
    }
  • The registration definition for the 'listTextModels' tool within the textTools export array. It specifies the tool name, description, empty input schema ({}), and references the handler function. This array is imported and spread into toolDefinitions, then registered via server.tool() in src/index.js.
    ["listTextModels", "List available text models", {}, listTextModels],
  • src/index.js:87-87 (registration)
    The MCP server tool registration loop that applies server.tool() to each tool definition in toolDefinitions, which includes the 'listTextModels' tool via spread from textTools.
    toolDefinitions.forEach((tool) => server.tool(...tool));
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List') but doesn't describe what 'available' means (e.g., filtered by permissions, region, or cost), whether it's a read-only operation, potential rate limits, or the format of the returned list. For a tool with zero annotation coverage, this is a significant gap 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.

Conciseness5/5

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.

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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of model names, IDs, capabilities), any behavioral constraints, or how it fits into workflows with siblings like generateText. For a tool that likely returns critical configuration data, this leaves the agent under-informed.

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. A baseline of 4 is assigned because the schema fully handles parameters, and the description doesn't need to compensate for any gaps.

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 text models'), making the purpose immediately understandable. It distinguishes from most siblings (e.g., generateText, listImageModels) by specifying 'text models' rather than other resource types. However, it doesn't explicitly differentiate from listAudioVoices or listImageModels in terms of when to choose between them, which prevents 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), typical use cases (e.g., before generating text to see options), or comparisons to siblings like listImageModels. This leaves the agent without context for tool selection.

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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