Skip to main content
Glama

zapcap_mcp_create_task

Create a video processing task with customizable subtitles, styling, and effects for automated video enhancement.

Instructions

Create video processing task with full customization options

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function that implements the zapcap_mcp_create_task tool logic, sending a POST request to the ZapCap API to create a video processing task with customizable subtitle and style options.
    def zapcap_mcp_create_task(request: CreateTask) -> Dict[str, Any]:
        headers = {
            "x-api-key": get_api_key(),
            "Content-Type": "application/json"
        }
        
        data = {
            "templateId": request.template_id,
            "autoApprove": request.auto_approve,
            "language": request.language,
            "renderOptions": {
                "subsOptions": {
                    "emoji": request.emoji,
                    "emojiAnimation": request.emoji_animation,
                    "emphasizeKeywords": request.emphasize_keywords,
                    "animation": request.animation,
                    "punctuation": request.punctuation,
                    "displayWords": request.display_words
                },
                "styleOptions": {
                    "top": request.position_top,
                    "fontUppercase": request.font_uppercase,
                    "fontSize": request.font_size,
                    "fontWeight": request.font_weight,
                    "fontColor": request.font_color,
                    "fontShadow": request.font_shadow,
                    "stroke": request.stroke,
                    "strokeColor": request.stroke_color
                },
                "highlightOptions": {
                    "randomColourOne": request.highlight_color_1,
                    "randomColourTwo": request.highlight_color_2,
                    "randomColourThree": request.highlight_color_3
                }
            }
        }
        
        # Add B-roll settings only if enabled
        if request.enable_broll:
            data["transcribeSettings"] = {
                "broll": {
                    "brollPercent": request.broll_percent
                }
            }
        
        with httpx.Client() as client:
            response = client.post(
                f"https://api.zapcap.ai/videos/{request.video_id}/task",
                headers=headers,
                json=data
            )
        
        response.raise_for_status()
        return response.json()
  • Pydantic BaseModel defining the input parameters and validation schema for the zapcap_mcp_create_task tool.
    class CreateTask(BaseModel):
        video_id: str = Field(description="Video ID from upload")
        template_id: str = Field(description="Template ID")
        auto_approve: bool = Field(default=True, description="Auto approve the task")
        language: str = Field(default="en", description="Language code")
        enable_broll: bool = Field(default=False, description="Enable B-roll (requires video > 8-10 seconds)")
        broll_percent: int = Field(default=30, description="B-roll percentage (0-100)")
        # Subtitle options
        emoji: bool = Field(default=True, description="Enable emoji in subtitles")
        emoji_animation: bool = Field(default=True, description="Enable emoji animation")
        emphasize_keywords: bool = Field(default=True, description="Emphasize keywords")
        animation: bool = Field(default=True, description="Enable subtitle animation")
        punctuation: bool = Field(default=True, description="Include punctuation")
        display_words: int = Field(default=1, description="Number of words to display")
        # Style options
        position_top: int = Field(default=60, description="Subtitle position from top")
        font_uppercase: bool = Field(default=True, description="Use uppercase font")
        font_size: int = Field(default=30, description="Font size")
        font_weight: int = Field(default=900, description="Font weight")
        font_color: str = Field(default="#ffffff", description="Font color")
        font_shadow: str = Field(default="l", description="Font shadow (s/m/l)")
        stroke: str = Field(default="s", description="Stroke style")
        stroke_color: str = Field(default="#000000", description="Stroke color")
        # Highlight colors
        highlight_color_1: str = Field(default="#2bf82a", description="First highlight color")
        highlight_color_2: str = Field(default="#fdfa14", description="Second highlight color")
        highlight_color_3: str = Field(default="#f01916", description="Third highlight color")
  • FastMCP decorator that registers the zapcap_mcp_create_task function as an MCP tool.
    @mcp.tool(description="Create video processing task with full customization options")
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. It states 'Create' implies a write operation but lacks details on permissions, side effects, rate limits, or what 'full customization options' entails beyond the schema. This is insufficient for a mutation tool with complex parameters.

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 that is front-loaded with the core action. There is no wasted verbiage, making it highly concise and well-structured for quick comprehension.

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 complexity (many parameters with 0% schema coverage, no annotations, but an output schema exists), the description is inadequate. It doesn't explain the customization options, prerequisites, or behavioral traits, leaving significant gaps despite the output schema handling return values.

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

Parameters1/5

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

Schema description coverage is 0%, meaning all parameters are undocumented in the schema. The description only mentions 'full customization options' without explaining any parameters, failing to compensate for the lack of schema documentation and leaving the agent with no semantic understanding of inputs.

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 ('Create') and resource ('video processing task'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'zapcap_mcp_upload_video' or 'zapcap_mcp_monitor_task', which handle different aspects of the workflow.

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. It doesn't mention prerequisites (e.g., needing an uploaded video or template), dependencies on sibling tools, or scenarios where it's appropriate, leaving the agent without context for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bogdanminko/zapcap-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server