Summarize a YouTube video with Claude and return the summary text, in any of 100+ output languages (language param, BCP-47). Pay-per-call via x402 (USDC on Base) — no account or API key. Cheaper than pulling the full transcript into your own context. A bad URL or a video without captions returns an error and is not charged.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| language | No | en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses pay-per-call via x402, no account/API key needed, error handling (no charge for failures), and language support. It omits idempotency or caching behavior, but the key behavioral traits are covered.
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 concise (4 sentences), front-loaded with the primary purpose, and each sentence adds unique value: summarization action, payment model, cost comparison, and error handling. No redundant or extraneous content.
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 tool's simplicity (2 params, no output schema), the description covers payment, error handling, language support, and differentiation from siblings. Missing details about return format (e.g., plain text or structured) and synchronous nature, but overall reasonably complete for agent usage.
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?
Schema description coverage is 0%, so the description must add meaning. It explains that language uses BCP-47 format and supports 100+ languages, and implies url is a YouTube video URL. However, it does not specify whether url should be full URL or just video ID, leaving ambiguity. The description partially compensates for the lack of schema descriptions.
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 summarizes a YouTube video using Claude and returns summary text in multiple languages, distinguishing it from sibling tools like get_youtube_transcript (which returns raw transcript) and list_new_channel_videos (listing metadata).
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 context on when to use this tool: it's cheaper than fetching a transcript separately, and it highlights failure cases (bad URL or no captions) that result in no charge. However, it does not explicitly list scenarios where alternatives would be better (e.g., if full control over summarization or offline processing is needed).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: fetching transcripts, listing new channel videos, and summarizing videos. The descriptions clearly differentiate them with no overlap in functionality.
All tool names follow a consistent verb_noun pattern: get_youtube_transcript, list_new_channel_videos, summarize_youtube_video. No mixing of conventions.
Three tools cover the core capabilities needed for a video summarizer: transcript retrieval, channel monitoring, and summarization. The count is well-scoped and not excessive.
The tool set fully covers the stated purpose: obtaining transcript data, monitoring for new videos, and generating summaries. No obvious gaps for the intended use case.
