Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and open-world, so safety is covered. The description adds genuinely non-obvious behavior: the median is computed only over the videos returned, so the limit matters for score fairness, and dates may be approximate. It does not discuss pagination or rate limits, but for a read-only listing that is a minor gap.

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?

Three short blocks — what it does, when to use it, what it returns — with the outlier definition front-loaded. Every sentence carries information; nothing is restated from the schema verbatim.

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?

There is no output schema, and the description compensates by summarizing the return payload (channel stats plus per-video views, outlier multiple, date, duration). Combined with the six documented parameters and the median caveat, an agent has everything needed to call it and interpret results.

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 coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: the 30+ recommendation for a stable median ties the `limit` parameter to result quality, and the minOutlier caveat is restated in context. This helps an agent choose values rather than just syntax.

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?

States a specific verb and resource ('List a channel's videos') and immediately adds the differentiating feature — the outlier score defined inline as views relative to the channel median. This clearly separates it from youtube_search (query-based) and youtube_fetch (single-video retrieval) without opening a schema.

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 'Best for:' line enumerates three concrete jobs (finding breakout videos, reviewing recent uploads, building a list to fetch), the last of which implicitly routes to youtube_fetch. It stops short of an explicit exclusion rule naming the alternative tool and its trigger condition, so it is clear context rather than full routing guidance.

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

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so safety is covered. The description adds genuinely new behavioral context: it probes live network paths from this machine and reports timings, which tells the agent this is a real-time environment check rather than a cached lookup.

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?

Two sentences, zero filler. The scope enumeration is front-loaded and the usage trigger is the closing sentence, which is exactly the right ordering for a diagnostic tool.

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?

For a no-param diagnostic with no output schema, the description covers what is checked and when to invoke it. It only lightly gestures at the return shape ("with timings") without saying what a partial or total failure report looks like, a small gap given there is no output schema to fill it.

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 tool takes zero parameters, so the baseline is 4. The description correctly adds nothing about parameters, and no parameter meaning is left ambiguous.

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?

Specific verb ("Check") plus an enumerated scope: search, video details, transcripts, watch page, channels, yt-dlp, ffmpeg. It is unmistakably a diagnostic tool, cleanly distinguishable from youtube_search, youtube_fetch, and youtube_channel.

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?

Gives an explicit trigger condition: "Run it when other youtube_* tools fail," which names the sibling tools it complements. It stops short of stating when not to run it (e.g. before any failure, or as a routine preflight), so it is clear context without full exclusions.

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

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only/idempotent/non-destructive, and the description goes further with latency (~10–20 s for the first frames, stream lookup) and partial-failure semantics ('per-video failures are reported inline, never dropped'). It also flags cost tradeoffs (frame width = more tokens, frame-heavy calls across a few videos) and truncation behavior, which the agent cannot infer from annotations.

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?

Well front-loaded and organized under Best for / Returns / Tips, with each tip carrying actionable content. Slightly verbose for the payoff, with a little redundancy (batching stated in the opening line and again in the multi-video tip), which keeps it short of a 5.

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?

For a 15-parameter tool with no output schema, the description supplies the return shape (per-video header with title, channel, publish date, views, likes, duration, plus [m:ss]-stamped transcript lines) and the failure/truncation model. An agent has everything needed to compose a correct call and interpret the result.

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 100%, so the baseline is 3; the description still adds interpretive value by explaining how start/end apply to both transcript and frames, the frames-vs-frameAt distinction, and the ?t= start fallback. It does not add meaning for the more peripheral knobs (commentsLimit, transcriptLangs, descriptionChars, matches) beyond what the schema already says.

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?

Specific verb+resource (read YouTube videos) with the full list of retrievable parts (transcript, frames, details, chapters, comments, thumbnail) and batching scope. The phrase 'after youtube_search' explicitly distinguishes it from the search sibling, so the agent can route without opening schemas.

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?

Gives concrete when-to-use scenarios (post-search reading, same slice across many videos, quoting with timestamps) and decision rules for alternatives: use query instead of full transcripts for topic finding, and re-call with start=<stop second> when a transcript is cut off. This is close to a decision tree, not just a context hint.

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

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds crucial behavioral context beyond that: merged de-duplicated results across queries, transcript highlights only for top results (rate-limit aware, default 5), approximate date filtering, and the note that highlights cost a transcript download per video due to YouTube rate limits. This is rich operational detail.

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?

Front-loaded with purpose and return value, then structured as 'Best for', 'Returns', and 'Query tips'. Every section is useful, though the query tips section is slightly long. Overall efficient and scannable.

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?

For a 16-parameter search tool with no output schema, the description provides enough context to call it correctly: it explains result merging, highlight mechanics, rate-limit implications, and when to use youtube_fetch instead. The only gap is that the return shape (fields like views/day, key moments) is described in prose rather than schema, but that's adequate. An agent has what it needs.

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 coverage is 81%, so the schema carries most parameter documentation. The description adds value by explaining the query array can mix strings and objects with hl/gl for localization, and by suggesting highlightQuery can differ from search queries. It doesn't explain every parameter (e.g., no details on sort or transcriptLangs), but the schema handles those adequately at 81% coverage.

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?

States a specific verb (search) and resource (YouTube videos), and uniquely distinguishes itself from siblings by emphasizing transcript highlights and timestamped links. An agent can immediately tell this is the broad discovery tool vs. youtube_fetch for deep retrieval.

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?

Explicitly lists what it's best for (topic finding, creator claims, example collection), provides concrete query construction tips, and names the alternative (youtube_fetch) with the exact condition to switch. Covers when to use filters instead of more results.

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