yt-mcp

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

With no annotations, the description must carry the full burden. It discloses that it returns 'stats' and helps confirm identity, but it does not mention potential side effects, permission requirements, or failure behaviors. This is a reasonable but minimal baseline for a read-style 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 compact sentences, with the main function front-loaded and a brief usage tip following. Every word earns its place; no fluff or repetition.

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 low complexity, one parameter, and presence of an output schema, the description is sufficient. It explains the input varieties and a practical use case, though it does not mention edge cases like ambiguous names or not-found behavior.

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% and the parameter description already lists handle, URL, id, and name. The tool description repeats this information without adding new semantic detail, so it meets the baseline but adds no extra value.

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 opens with a specific verb and resource: 'Resolve a channel handle, URL, id or name to a channel with its stats.' This clearly distinguishes it from sibling tools that focus on videos (e.g., search_channel_videos, list_channel_videos).

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 second sentence provides context: 'Useful on its own to confirm which channel a name refers to before searching.' This implies when to use it, but it does not explicitly name alternatives or state when not to use it.

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?

No annotations are provided, so the description carries the full burden. It explains the output contents, including the nature of the links list, but does not disclose any limitations, error conditions, or permission requirements. This is a read-only informational tool, so moderate transparency is acceptable.

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 two sentences, front-loaded with the core purpose, and every sentence adds value. The clarification about the links list is useful and concise.

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 simple nature and the presence of an output schema, the description is sufficient. It communicates the key output types and clarifies a potentially ambiguous field (links). It does not detail 'engagement counts' but that is minor and likely covered by the output schema.

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 input schema already provides a clear description for the single 'video' parameter (ID or URL). The description does not add further parameter-level details, but with 100% schema coverage, the baseline of 3 applies.

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 retrieves a video's full description, extracted links, tags, and engagement counts. This specific verb+resource+details structure distinguishes it from sibling tools like get_video_transcript.

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 implies usage for retrieving video metadata but does not explicitly state when to use this over alternatives or mention exclusions. The context is clear but lacks direct guidance on choosing among related tools.

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?

With no annotations, the description carries the burden and does it well: it discloses the output format (timestamped Markdown links), instructs the agent to preserve links verbatim, and states it reads public caption tracks with zero API quota. It doesn't mention failure cases such as missing captions, but core behavioral traits are transparent.

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 front-loaded with the primary purpose and output format, and each sentence adds value: linking behavior, verbatim requirement, range parameters, and cost. The example link is useful though slightly long; overall it is well-structured without bloat.

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 an output schema exists and all five parameters are documented, the description is nearly complete: it explains output semantics, parameter usage, cost impact, and limitations to public tracks. It omits only edge-case behavior such as missing or auto-generated captions, which is a minor gap for this complexity level.

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?

Input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds usage context for start_seconds/end_seconds but doesn't materially elaborate on languages or chunk_seconds beyond the schema, so no higher score is warranted.

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?

Description uses a specific verb+resource ('Get a video's transcript') and immediately characterizes the output as timestamped, quotable lines with clickable Markdown links. This clearly differentiates it from siblings like get_video_details, which retrieves metadata, and the channel-list tools, since the purpose is uniquely transcript 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?

It gives clear operational context: narrow long videos with start_seconds/end_seconds, reproduce links verbatim, and reads public caption tracks at no API quota. It doesn't explicitly name sibling alternatives or say when not to use it, but the use case is unmistakable and siblings are unlikely to be confused with it.

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?

With no annotations, the description carries the burden of disclosing behavior. It mentions ordering (newest first) and date-range filtering, which are useful. However, it does not address safety, auth, or error handling, though the read-only nature is implied by 'List'.

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 two sentences: a clear purpose statement and a helpful example. No wasted words, and the example is 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?

For a list tool with 4 parameters and an output schema, the description is fairly complete. It covers purpose, ordering, and filtering. It does not discuss pagination, but max_results is in the schema, and the output schema covers return format.

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 schema already documents all parameters. The description adds a concrete example using published_after='1y', which reinforces the schema but does not add substantial new meaning.

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 lists a channel's uploads newest first, with optional date filtering. The verb 'List' is specific, and the resource is 'a channel's uploads', which distinguishes it from search_channel_videos.

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 implies this is for retrieving all uploads for a given channel, with an example showing usage. It does not explicitly name alternatives or exclusions, but the context is clear enough to select this over a search tool.

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?

No annotations are provided, so the description carries the transparency burden. It discloses the cost: 'Costs 100 YouTube quota units per 50 results,' which is a behavioral trait not visible in the schema. However, it does not mention return format, error handling, or read-only nature beyond the implication of 'search,' so it is not a 5.

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 two sentences, front-loaded with the core purpose, and each sentence earns its place: one for what it does, one for cost and alternative. No fluff or repetition of schema-contained information.

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 complexity (6 params, output schema present, no annotations), the description covers purpose, cost, and an alternative, and the schema handles parameter semantics. It lacks only minor operational context such as error conditions, but the combination of schema and description is sufficient for selection and invocation.

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 input schema provides 100% coverage with descriptions for all 6 parameters, so the baseline is 3. The description adds only a usage example, not extra parameter-level detail, so it neither improves nor harms parameter understanding.

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 opens with 'Search one channel's videos by keyword,' which is a specific verb+resource+scope statement. It distinguishes from the sibling tool list_channel_videos by explicitly noting the alternative for keywordless recent uploads, making the purpose unambiguous.

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 explicitly states when NOT to use this tool: 'When you want recent uploads and have no keyword, use list_channel_videos instead — it is 100x cheaper.' It gives a concrete use-case example (latest iPhone review on MKBHD) and compares cost to the alternative.

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