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NoLang MCP Server

by team-tissis

list_generated_videos

Retrieve a paginated list of AI-generated videos created through the NoLang API for easy management and access to your video content.

Instructions

Return a paginated list of videos you have generated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYesCurrent page number
videosYesList of video summaries
has_nextYesTrue if there is another page of results
total_videosYesTotal number of videos matching the criteria

Implementation Reference

  • The handler function that executes the list_generated_videos tool logic, fetching videos via API and formatting results.
    async def list_generated_videos(args: ListVideosArgs) -> ListVideosResult:
        try:
            response = await nolang_api.list_videos(args.page)
            summaries = [
                VideoSummary(video_id=v.video_id, created_at=v.created_at, prompt=v.prompt or "") for v in response.results
            ]
            return ListVideosResult(
                total_videos=response.total_count,
                page=args.page,
                has_next=response.has_next,
                videos=summaries,
            )
        except httpx.HTTPStatusError as e:
            raise RuntimeError(format_http_error(e)) from e
  • Registers the list_generated_videos tool in the FastMCP server.
    @mcp.tool(
        name="list_generated_videos",
        description="Return a paginated list of videos you have generated.",
    )
  • Pydantic schema for input arguments to the list_generated_videos tool.
    class ListVideosArgs(BaseModel):
        """Arguments for listing videos."""
    
        model_config = ConfigDict(extra="forbid")
    
        page: int = Field(default=1, description="Page number to retrieve", ge=1)
  • Pydantic schema for the output result of the list_generated_videos tool.
    class ListVideosResult(BaseModel):
        model_config = ConfigDict(extra="allow")
    
        total_videos: int = Field(..., description="Total number of videos matching the criteria")
        page: int = Field(..., description="Current page number")
        has_next: bool = Field(..., description="True if there is another page of results")
        videos: List[VideoSummary] = Field(..., description="List of video summaries")
  • Helper method in NoLangAPI client that retrieves the paginated list of generated videos from the API.
    async def list_videos(self, page: int = 1) -> VideoListResponse:
        response_data = await self._get("/videos/", params={"page": page})
        return VideoListResponse(**response_data)
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 mentions pagination, which is useful, but lacks details on authentication needs, rate limits, error handling, or what 'videos you have generated' specifically entails (e.g., user-specific scope). This leaves significant gaps for a tool with potential data access implications.

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 purpose. There is no wasted verbiage, making it highly concise and well-structured for quick understanding.

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 (1 parameter) and the presence of an output schema (which handles return values), the description is somewhat complete but lacks critical context. It doesn't address usage guidelines or behavioral aspects like authentication, which are important for a tool that lists user-generated videos.

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 description coverage is 0%, so the description must compensate, but it adds no parameter information beyond implying pagination. The schema defines a 'page' parameter with a description, but the tool description doesn't explain this or other potential parameters, resulting in minimal added value.

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 action ('Return a paginated list') and resource ('videos you have generated'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'list_video_settings' or 'wait_video_generation_and_get_download_url', which prevents a score of 5.

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 sibling tools like 'list_video_settings' for settings or 'wait_video_generation_and_get_download_url' for status checking, leaving 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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