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

No annotations provided. Description indicates read-only extraction but does not disclose rate limits, authentication requirements, or privacy implications. Adequately notes the 'public accounts' constraint.

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 concise sentences: first states action and output, second adds value by contrasting with claimed usage, third states constraint. No fluff.

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?

Explains output (hashtags ranked by frequency) but does not specify the structure or count of returned items. With no output schema, had potential to be more complete.

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?

Only one parameter 'username' with schema description covering 100%. Description adds context about the tool's output but does not enhance the parameter definition beyond the schema.

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?

Clearly states it extracts hashtags from a public Instagram creator's last 12 posts and ranks them by frequency. Distinct from sibling tools like search_hashtags by focusing on a specific creator's actual usage.

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?

Specifies 'public accounts only' and limits to last 12 posts. Implicitly suggests use for analyzing a creator's hashtag strategy, but lacks explicit guidance on when not to use or alternatives among siblings.

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 discloses that it returns an array of media URLs in original slide order at native aspect ratio. However, it does not mention potential limitations (e.g., rate limits, authentication) or behavior if the URL is not a carousel.

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-loading the action and key details. Every sentence adds value without redundancy.

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 simple tool (1 parameter, no output schema), the description is complete. It explains what the tool does, the input requirements (public carousel post), and the output format (array of URLs in order).

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?

With 100% schema coverage, the baseline is 3. The description adds little beyond the schema's parameter description, as both specify the URL must be a public Instagram carousel post. The description does not provide additional semantic details like URL format expectations.

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 specifies that it downloads every slide of a public Instagram carousel post, including both photos and videos. It distinguishes itself from sibling tools like download_video (single video) and download_reels (reels) by focusing on carousel posts.

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 public carousel posts but does not explicitly state when not to use it or mention alternative tools. The context of sibling tools provides some guidance, but the description lacks explicit when-to-use/when-not-to-use instructions.

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, description bears full burden. Discloses return format, quality, and error condition (private accounts). No mention of rate limits or other side effects, but sufficient for a simple download tool.

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, front-loaded with purpose, no redundant information. Every sentence adds value.

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 one parameter and no output schema, description covers what is returned, format, quality, and error handling. Complete for this tool.

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 has 100% coverage for the single 'url' parameter. Description adds value with example URL format, aiding correct input.

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?

Clearly states 'Download an Instagram Reel from a public post URL' with specific output details (original 9:16 HD MP4, no watermark, no re-encoding). Distinct from siblings like download_story.

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 states public accounts only, private accounts return HTTP 422. Could mention alternative tools for other Instagram content but usage condition is clear.

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 provided, the description takes on full responsibility for behavioral transparency. It discloses the output format (MP4/JPG URLs), the 24-hour visibility window, and the HTTP 422 error for private accounts. It lacks details on rate limits or authentication but is adequate for a read-only retrieval tool.

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 extremely concise, using two sentences to convey the entire purpose, constraints, output, and error condition. Every sentence adds informational value without redundancy.

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 (one parameter, straightforward operation, no output schema required), the description is fully complete. It covers purpose, input constraints, output specs, and error handling, leaving no gaps.

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 schema covers one parameter (username) with 100% description coverage. The description adds value by clarifying that the leading @ is optional and emphasizing the username must be public, which enhances the schema's definition.

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 fetches active Instagram Stories for a public username, specifies the 24-hour window, and distinguishes it from siblings like download_reels by focusing specifically on Stories.

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 clear context on when to use (for public usernames) and the error condition for private accounts. Although it doesn't explicitly state when not to use, the sibling tools cover different use cases, making the guidance sufficient.

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 exist, so the description fully discloses behavior: Unicode substitution, character range (a-z, A-Z, 0-9), pass-through for others, and lists all 22 supported styles. Rich and accurate.

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 efficient sentences, front-loaded with purpose, no wasted words. Every sentence adds value.

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?

No output schema, but the description sufficiently explains return behavior (transformed text for one style, all styles if omitted). For this tool, the description is complete.

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. The description adds value by explaining the effect of omitting style ('get every style back') and summarizing the style list, but the schema already contains the full list.

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 converts plain text into Instagram-compatible Unicode fonts, explicitly distinguishing it from the listed siblings which focus on downloads and analytics, not text conversion.

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 specifies use for Instagram bio, name, and captions, clarifies it's not real fonts, and explains how to specify a style or omit for all. Lacks explicit when-not-to-use but is still clear.

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 bears full burden. It discloses the output (5 slots + heatmap) and data source, which is sufficient for a non-destructive data retrieval tool. It does not mention rate limits or permissions, but the behavior is 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 two sentences, front-loaded with the main purpose, and each sentence adds essential details (output specifics and supported markets). No superfluous 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 no output schema, the description fully explains what the output includes (5 prime slots with optimization signals, plus a 7-day heatmap) and lists all supported countries. For a simple tool with one parameter, this is complete.

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 schema already describes the country parameter with supported codes. The tool description repeats this list and adds context about the dataset, but does not add new meaning beyond the schema for the parameter itself.

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 it returns data-driven optimal Instagram posting windows for a country, using a specific verb and resource. It distinguishes from siblings like audit_creator_hashtags or download_carousel by focusing solely on posting time optimization.

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 when needing optimal posting times for a supported country and lists supported markets. However, it does not explicitly state when to avoid using the tool or mention alternatives, which would be helpful.

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 fully covers behavioral aspects: it returns entries with native-language Reels hook and hashtag set, uses verified lunar/astronomical dates, and lists available markets. It doesn't mention rate limits or authentication, but as a read operation, this 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-loading the main purpose and filters. The list of markets is detailed but necessary for usability. Every sentence adds value, though it could be slightly more concise by grouping markets differently.

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 has 5 parameters, no output schema, and no annotations, the description covers the essential functionality: return entries with filters. It explains the niche filter and market list. A minor gap is the lack of explicit return format, but it mentions the entry contents sufficiently.

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 baseline is 3. The description adds value by explaining that entries carry a Reels hook and hashtag set, and that dates use verified anchors. It also clarifies the 'niche' parameter matches the calendar entry's niches array, going beyond 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 returns Instagram content calendar entries for a market, with optional filters. It uses a specific verb 'Return' and resource 'content calendar entries', and distinguishes from sibling tools that focus on hashtags or downloads.

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 to use the tool (to get calendar entries for a market) and provides guidance on using 'GLOBAL' for cross-market dates. It doesn't explicitly state when not to use it or list alternatives, but the sibling tool names imply different use cases.

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?

The description explains key behaviors: it is a live search, not cached or fabricated, and that results vary by country and language. However, since no annotations are provided, the description could also address rate limits, authentication needs, or the exact nature of the output.

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 a single paragraph of three sentences, efficiently conveying purpose, behavior, and usage guidance without unnecessary words.

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?

While the description covers the main purpose and usage, it lacks detail about the return format (e.g., structure of the response, whether it includes both hashtags and counts). Since there is no output schema, the description should provide more completeness.

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 description adds meaning beyond the schema by providing examples for topic and explaining how locale influences results (country+language shaping the tag mix).

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 performs a live search of Instagram's public hashtag index and returns real post counts. It explicitly distinguishes from the sibling tool get_hashtag_niche by specifying use cases: free-form topics vs curated niches.

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 gives explicit guidance on when to use this tool ('when the user gives a free-form topic') and when to use the alternative ('get_hashtag_niche if they name one of the 25 curated seed niches'), along with context that country and language shape results.

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