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play_video

Stream and play videos directly from a provided link using the MCP server VideoDB Director. Ideal for quick access to video content based on user input.

Instructions

Play the video of the given stream link

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stream_linkYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The asynchronous handler function that implements the core logic of the 'play_video' tool. It takes a stream_link parameter, opens the VideoDB player in the default web browser, and returns a success message.
    async def play_video(stream_link: str) -> dict[str, Any]:
        webbrowser.open(f"https://console.videodb.io/player?url={stream_link}")
        return {"message": "Opening VideoDB in browser"}
  • The @mcp.tool decorator registers the 'play_video' function as an MCP tool, specifying its name and description. This integrates it into the FastMCP server.
    @mcp.tool(
        name="play_video",
        description="Play the video of the given stream link",
    )
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 the action ('Play') but doesn't explain what 'Play' entails—e.g., does it open a player, stream in background, require specific permissions, or have rate limits? This leaves key behavioral traits undefined.

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 directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to grasp quickly with zero waste.

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 has an output schema (which covers return values), the description's minimalism is somewhat acceptable. However, with no annotations and low schema coverage, it lacks completeness for a tool that performs an action like 'Play'—missing behavioral context and parameter details leaves gaps.

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

Parameters2/5

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

The schema description coverage is 0%, so the description must compensate. It mentions 'stream_link' but doesn't add meaning beyond the schema's basic type and title—no details on format, valid URLs, or examples. This fails to adequately clarify the parameter's semantics.

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 ('Play') and the resource ('video of the given stream link'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'call_director' or 'doc_assistant', which appear unrelated, so it doesn't fully distinguish itself in context.

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, such as whether it's for streaming specific formats or in certain contexts. It lacks explicit instructions on prerequisites, exclusions, or comparisons with sibling tools, leaving usage ambiguous.

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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