YouTube Transcript MCP Server

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 tool retrieves a list with metadata, but doesn't describe key behaviors such as error handling (e.g., for invalid video IDs), rate limits, authentication needs, or performance characteristics. This leaves significant gaps for an AI agent to understand how the tool operates in practice.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured and appropriately sized, with a clear purpose statement followed by Args and Returns sections. Each sentence earns its place by defining the tool's function and parameters. It could be slightly more concise by integrating the parameter explanation into the main text, but overall it's efficient and front-loaded.

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

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

Given the complexity (a tool interfacing with external services like YouTube and yt-dlp), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return value structure (e.g., what metadata fields are included), error conditions, or dependencies. This makes it inadequate for an AI agent to fully understand the tool's behavior and outputs.

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

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

The description adds basic semantics for the single parameter: 'video_id: YouTube video ID or URL.' This clarifies that the input can be either an ID or a full URL, which is useful since the schema has 0% description coverage and only provides a generic title. However, it doesn't elaborate on format constraints (e.g., URL patterns, ID length) or examples, so it only partially compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get list of available transcript languages for a YouTube video using yt-dlp.' It specifies the verb ('Get'), resource ('available transcript languages'), and technology context ('using yt-dlp'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like get_transcript or search_transcript, 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.

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 get_transcript (which might fetch transcripts) or search_transcript (which might search within transcripts), nor does it specify prerequisites or exclusions. The only implied usage is for obtaining language metadata, but this is too vague for effective tool selection.

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