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ilhankilic

YaparAI MCP Server

by ilhankilic

list_templates

Browse 448+ ready-made AI templates for tasks like logo creation, product photography, portrait retouching, and ad generation. Filter by category, media type, or search to find templates with predefined inputs.

Instructions

Browse 448+ ready-made AI templates.

Discover pre-built workflows for common tasks: logo creation, product photography, portrait retouching, ad generation, and more. Each template has predefined inputs — just fill in prompts and images. No credits charged for browsing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFilter by category slug (e.g., "logo", "product", "portrait")
media_typeNoFilter by output type ("image", "video")
searchNoSearch templates by name/description
featuredNoShow only featured templates

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler function for the list_templates MCP tool. Accepts optional filters (category, media_type, search, featured), builds query params, and delegates to the HTTP client.
    async def list_templates(
        category: str | None = None,
        media_type: str | None = None,
        search: str | None = None,
        featured: bool = False,
    ) -> dict:
        """
        Browse 448+ ready-made AI templates.
    
        Discover pre-built workflows for common tasks: logo creation,
        product photography, portrait retouching, ad generation, and more.
        Each template has predefined inputs — just fill in prompts and images.
        No credits charged for browsing.
    
        Args:
            category: Filter by category slug (e.g., "logo", "product", "portrait")
            media_type: Filter by output type ("image", "video")
            search: Search templates by name/description
            featured: Show only featured templates
    
        Returns:
            Dict with templates list (name, slug, description, credit_cost,
            category, media_type, thumbnail_url).
        """
        client = YaparAIClient()
        params = {}
        if category:
            params["category"] = category
        if media_type:
            params["media_type"] = media_type
        if search:
            params["search"] = search
        if featured:
            params["featured"] = "true"
        return await client.list_templates(params or None)
  • Input parameters (category, media_type, search, featured) and return type (dict) for list_templates, effectively serving as the schema.
    async def list_templates(
        category: str | None = None,
        media_type: str | None = None,
        search: str | None = None,
        featured: bool = False,
    ) -> dict:
        """
        Browse 448+ ready-made AI templates.
    
        Discover pre-built workflows for common tasks: logo creation,
        product photography, portrait retouching, ad generation, and more.
        Each template has predefined inputs — just fill in prompts and images.
        No credits charged for browsing.
    
        Args:
            category: Filter by category slug (e.g., "logo", "product", "portrait")
            media_type: Filter by output type ("image", "video")
            search: Search templates by name/description
            featured: Show only featured templates
    
        Returns:
            Dict with templates list (name, slug, description, credit_cost,
            category, media_type, thumbnail_url).
        """
        client = YaparAIClient()
        params = {}
        if category:
            params["category"] = category
        if media_type:
            params["media_type"] = media_type
        if search:
            params["search"] = search
        if featured:
            params["featured"] = "true"
        return await client.list_templates(params or None)
  • Registration of list_templates as an MCP tool via FastMCP's tool() decorator.
    mcp.tool(list_templates)
  • HTTP client method that sends a GET request to /v1/comfy-templates with optional query parameters.
    async def list_templates(self, params: dict | None = None) -> dict:
        """List ComfyUI templates."""
        return await self._request("GET", "/v1/comfy-templates", params=params)
Behavior3/5

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

No annotations provided, so the description carries the burden. It discloses that no credits are charged for browsing, indicating a safe read operation. However, it does not detail pagination, sorting, or any other behavioral traits.

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?

Three focused sentences: count, use cases, and key behavioral note (free). No wasted words, front-loaded with purpose.

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

Completeness4/5

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

With an output schema present, the description adequately covers the tool's purpose and a key behavioral aspect. It could mention that the result is a list of templates, but overall it is complete for a browsing tool.

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 baseline 3. The description does not add significant meaning beyond the schema's parameter descriptions. It mentions 'predefined inputs' but this refers to templates, not parameters.

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 it is for browsing AI templates, with specific examples of use cases like logo creation and product photography. It distinguishes itself from siblings like execute_template and get_template_detail by focusing on discovery.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (for discovering templates) and notes that browsing is free. It does not explicitly mention alternatives, but the context of sibling tools makes it clear when this is appropriate.

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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