Al Jazeera 360

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

Annotations provide readOnlyHint=true, so the read-only nature is disclosed. The description adds context about what the audit checks (short/missing description, missing tags/thumbnails) beyond the annotation, but does not describe the report structure, pagination behavior, or any other operational details. Given the annotation coverage, this is adequate but not exceptional.

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 a clear one-sentence purpose, followed by an Arabic translation, a list of audit criteria, and a well-structured Args section. It is not excessively long, though the Arabic repetition adds some redundancy. Overall, it is efficient and organized.

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?

The tool has an output schema, so return values need not be described. The description covers parameters, audit criteria, and the target section. It is missing only minor operational details like pagination behavior beyond parameter defaults, but given the output schema and read-only annotation, it is sufficiently complete for an agent to call it correctly.

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 explain parameters. It does so effectively with an Args section that clarifies section_id (with examples), max_items, page, and page_size, including defaults. This adds meaning beyond the bare schema and enables correct invocation.

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 states a specific verb ('audit'), a specific resource ('metadata quality of videos in a section'), and explicitly lists the quality criteria (short/missing description, no tags, no thumbnail). This clearly distinguishes it from sibling tools like run_diagnostics or get_video_details, which serve different purposes.

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 explains what the tool does but offers no guidance on when to use it versus alternatives. It does not mention exclusions, prerequisites, or suggest other tools for related tasks. An agent would have to infer its appropriate use case.

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 declare readOnlyHint=true, and the description is consistent with that. The description doesn't add much behavioral depth beyond what annotations provide—it doesn't mention pagination, filters, or the structure of the returned content. It adds useful context about the available sections and the meaning of 'section_id', but for a read-only browse operation, the transparency is adequate but not rich.

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 and well-structured. It opens with a clear purpose sentence, provides the bilingual context, and then lists all valid sections in a bulleted list. The parameter explanation follows. While the list is long, it is necessary for usability. It avoids fluff and stays focused.

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 that the tool has a single parameter with a well-defined list of values, the description is nearly complete. It doesn't describe the return structure, but the presence of an output schema (not shown but indicated) likely covers that. It doesn't mention potential edge cases like invalid section IDs, but the list serves as a guide. For a simple browse operation, this is sufficient.

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 0% (the schema only provides the parameter name 'section_id' with no description). The description compensates by explaining that section_id is the section identifier and listing valid values (e.g., 'AJA', 'Documentaries'). This gives the agent concrete guidance on what to pass, which is essential given the lack of schema-level documentation.

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 states a clear verb ('Browse') and resource ('content in a specific section/channel'), and provides a comprehensive list of valid sections with bilingual labels. This clearly distinguishes it from siblings like 'list_sections' (which likely lists sections) and 'search_videos' (which searches across all content).

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 tool is for browsing a specific section, as opposed to searching or listing sections, but it does not explicitly state when not to use it or name alternatives. The inclusion of the full section list helps the agent know what values to pass, but it doesn't explicitly say 'use this instead of search_videos when you have a section known.'

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 readOnlyHint=true, so the tool is known to be safe and non-mutating. The description adds the return format but doesn't disclose potential costs, time, or limitations of analysis. With annotation coverage, this is a reasonable but not outstanding score.

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 neatly structured with a purpose statement, Args, and Returns sections. It is concise, front-loaded, and contains no filler or 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 a single optional parameter with a default and an output schema present, the description covers the key inputs and returns adequately. Missing explicit when-to-use guidance is a minor gap but not critical for a simple read-only 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 description coverage is 0%, and the schema only shows type and default. The description's Args section compensates by explaining that sections are comma-separated section IDs with default 'AJA,AJD', adding meaning 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?

The description opens with a specific verb and resource: 'Build a knowledge graph of entities (topics, countries, people) from content titles.' It clearly states the extraction process and output, distinguishing it from siblings like build_topic_clusters by focusing on entity network and co-occurrence.

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 about operating across sections and defaults for sections, but it never states when to choose this tool over alternatives like build_topic_clusters or compare_sections, nor any exclusions. The usage context is implied rather than explicit.

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 annotations declare readOnlyHint=true, and the description aligns by describing an analysis operation that returns a strategy rather than modifying content. The description adds useful context about the output (JSON with clusters and linking strategy) but does not disclose any other behavioral traits like rate limits or auth. Given the annotation coverage, this is adequate but not rich.

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 moderately concise but includes a promotional sentence ('This is the most powerful modern SEO strategy (2024-2026)') that adds no operational value. The structure is otherwise clear with a definition, list, and Args/Returns sections, but the fluff detracts from efficiency.

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 single-parameter tool with an output schema, the description covers the purpose, the parameter format, and the return type. It does not mention prerequisites (e.g., that content must exist in the sections) but given the simplicity and the presence of an output schema, it is largely 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?

Despite the schema coverage signal of 0%, the description explicitly explains the only parameter 'sections' as 'Comma-separated section IDs' and provides the default value. This adds meaning beyond the schema, which only lists the property and default. The compensation is sufficient for a single parameter.

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 action ('Build') and the resource ('SEO Topic Clusters'), and elaborates with a concrete definition of pillar and supporting content. It is specific enough to distinguish from siblings like build_knowledge_graph or generate_sitemap, even without naming them explicitly. The verb-resource pairing is 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 provides no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or when not to use it. The only implied usage is that it analyzes content, but there is no explicit context about choosing this over other SEO 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?

Annotations already declare readOnlyHint=true, and the description aligns by describing a read-only analysis of sections. The description adds the all-sections scope and comparison dimensions, but does not disclose output format or any operational behavior; this is acceptable because the annotation covers the main safety-relevant trait.

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 English sentence is front-loaded and specific. The Arabic translation repeats much of the first sentence, but the final clause adds a useful analytical purpose, so the redundancy is minor rather than disqualifying.

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 zero-parameter, read-only comparison tool with an output schema, the description is complete: it names the data being comparedhra and the intended analytical use. No prerequisites, side effects, or return-value details are necessary.

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 has zero parameters, so there is nothing for the description to document about inputs. The baseline of 4 applies because no parameter-level compensation is needed.

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 'Compare' and a clear resource 'all sections' with three concrete comparison dimensions: content count, quality distribution, and freshness. It does not explicitly name a sibling tool, but the all-sections scope and dimensions are enough to distinguish it from list/browse tools.

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 Arabic sentence provides a clear intended use case: helping identify neglected versus strong-performing sections. However, it does not give explicit exclusions or name alternative tools, so the guidance is contextual but not exhaustive.

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 readOnlyHint=true, so the safety profile is known. The description adds behavior beyond this by specifying the output format ('JSON with evergreen/news classification, SEO priority scores, and recommendations') and the classification logic. This is more context than a simple read-only flag provides, though it does not mention edge cases or failure modes.

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 core purpose and uses a clean Args/Returns structure. The bullet list of prioritization targets adds useful context but is not strictly necessary for invoking the tool, so a small amount of trimming could tighten it without losing essential call 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?

For a read-only classification tool with one optional parameter, the description is largely complete: it explains the input, the output shape, and the intended use of the results. The main gap is that it does not define what 'AJA,AJD' or 'section IDs' mean, but defaults and obvious domain context mitigate this.

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%, but the description's Args section compensates: 'sections: Comma-separated section IDs (default: AJA,AJD)' explains both the format and the default value. The only gap is that the description does not clarify what section IDs refer to or how they map to content sections.

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+resource: 'Identify evergreen content (timeless value) vs time-sensitive news content.' This clearly distinguishes the tool from siblings like get_trending_content, which focuses on current trends, and generate_seo_content, which generates rather than classifies content.

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 explains the value of evergreen content and lists SEO tasks it should be prioritized for (schema markup, internal linking, etc.), which implies when to use the tool. However, it never explicitly states when not to use it or names alternative tools, leaving usage selection to inference.

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 discloses that FAQ data is created based on the video title and description and that the return value includes FAQ questions/answers plus JSON-LD schema. Since readOnlyHint=true already signals no side effects, this adds useful behavioral context without contradicting 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?

The description is well-structured with a clear purpose statement, benefit context, and Args/Returns sections. It is slightly verbose for a single-parameter tool, but each part earns its place and the key action 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 simple one-parameter, read-only generation tool with an output schema present, the description is adequately complete. It covers what the tool does, what inputs it needs, and what it returns, leaving no critical gaps for correct 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 description provides a basic explanation for video_id ('The video ID to generate FAQ schema for'), but this largely restates the parameter name and type. With 0% schema description coverage, the description does not fully compensate by explaining where to find the ID or any expected format.

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 generates FAQ Schema (JSON-LD) for a video, naming the specific deliverable and purpose. It distinguishes itself from sibling tools like generate_series_schema by focusing on video-specific FAQ data and AI discoverability.

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 explains the benefit ('helps content appear in AI answers...') but gives no explicit guidance about when to use this tool versus alternatives like generate_series_schema or generate_sitemap, and no exclusion criteria. Usage context is only implied.

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 readOnlyHint annotation already covers the safety profile, so the description adds useful extra context: the tool works 'entirely from public API data' and requires 'no AI API', implying no external paid calls and no side effects. It also explains that it derives keywords from the video title, categories, and series, which is a meaningful behavioral detail. It does not cover invalid-video-ID error behavior, but that is a minor gap given the annotation and output schema.

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 main purpose and then uses a scannable bullet list for the outputs, followed by a compact Args section. The bilingual English/Arabic intro repeats the same 'free, no AI API' claim and adds length without new information, but it is not bloated enough to harm readability. Overall it is well-organized and appropriately sized.

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 that an output schema exists, the lengthy output list in the description is a helpful supplement rather than a necessity. The description covers purpose, platform, cost/dependency behavior, output scope, and the single parameter, which is complete for a low-complexity tool. The main missing piece is explicit guidance on how this tool relates to the sibling schema-generation tools.

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 only parameter, video_id, is explained in the Args block with a concrete example (953659), which is sufficient to clarify the expected input even though the schema description coverage is 0%. The example also implicitly communicates that the ID is numeric. It could add guidance on where to obtain the ID or any validity constraints, but for a single obvious integer parameter this is adequate.

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?

Opens with 'Generate complete SEO content for a video on Al Jazeera 360', a specific verb, resource, and platform, then enumerates concrete outputs such as meta title, meta description, focus keyword, JSON-LD schema, and audit notes. The purpose is clearly distinguishable from sibling tools like generate_faq_schema and generate_series_schema, though the description does not explicitly contrast itself with them.

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 the tool is intended for single-video SEO content creation ('for a video') and emphasizes no cost or AI API dependency, which helps an agent understand when it is appropriate. However, it gives no explicit when-to-use or when-not-to-use guidance and does not mention alternatives such as generate_faq_schema, generate_series_schema, or generate_sitemap. Most usage context must be inferred from the tool name and one-parameter interface.

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 readOnlyHint=true and the title 'Generate Series Schema', so the agent knows this is a safe read-only generation operation. The description adds the useful context that it leverages typedTags episodeInformation for enrichment, which is genuine behavioral context beyond the annotation. No contradiction; the description is consistent with readOnlyHint.

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 purpose is front-loaded in the first sentence with the source-of-data in the second. The Args section is compact and informative. Minor redundancy — 'the most powerful Schema type' plus the overall length is slightly more than strictly needed for a one-parameter tool, but nothing is wasted.

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 single-parameter tool with an output schema (covering return values) and readOnly annotation, the description is nearly complete. It explains the purpose, data source, and how to obtain the parameter. The only gap is not explicitly naming sibling schema generators to disambiguate — minor given the clear purpose framing.

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 carries the full burden — and it delivers. The Args block explicitly tells the agent what series_id is ('The series/show ID') and exactly where to obtain it ('get from get_video_details episodeInformation.seriesInformation.id'). This is high-value semantic guidance that entirely compensates for the schema's silent parameter.

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 ('Generate'), resource (TVSeries + TVEpisode JSON-LD Schema), and the source of structured data (episodeInformation from typedTags). The 'most powerful Schema type for video series in Google Search' framing gives it a distinct identity among schema siblings like generate_faq_schema and generate_sitemap. However, it doesn't explicitly name sibling tools to contrast against, caps it at 4.

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 when to use it — for video series schema in Google Search — but provides no explicit when-not guidance or named alternatives. With siblings like generate_faq_schema and generate_sitemap present, the absence of explicit routing ('use generate_faq_schema for FAQs') leaves selection partly to inference. Adequate but not explicit.

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 carry readOnlyHint=true, and the description adds output composition details (titles, descriptions, thumbnails, publish dates). It does not describe pagination behavior or edge cases, but with read-only safety already declared this is adequate.

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 useful purpose is front-loaded and the Args block is clearly organized. The Arabic translation repeats some of the English content, but the additional Arabic sentence contributes field-level output details, so the redundancy is minor.

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 straightforward read-only generation tool with only four optional parameters, the description plus the existing output schema and readOnlyHint is nearly complete. The only small gap is that 'page'/page_size pagination scope is not fully disambiguated from max_per_section.

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%, yet the Args section fully compensates by defining every parameter's format and meaning: comma-separated section IDs or 'all', max videos per section, page number, and page size. This is exactly what an agent needs beyond bare property titles.

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 names a specific verb and deliverable: generating a complete Video Sitemap XML for Al Jazeera 360, ready for Google Search Console. This clearly sets it apart from siblings like generate_faq_schema and generate_series_schema because the artifact and platform are explicit.

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 provides clear context (sitemap generation for Al Jazeera 360, submission to Google Search Console, all sections by default) without explicitly saying when not to use it. It does not name alternative tools, but the target use case is obvious from the first sentence.

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?

Given readOnlyHint=true is annotated, the description correctly implies a read-only operation (calculating scores) without contradiction. It adds the scoring criteria breakdown, which provides behavioral insight into how the score is computed, exceeding what annotations provide. However, it doesn't disclose details like rate limits or data scope beyond the description.

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: a clear purpose statement, a scoring criteria list, and the Args section are all front-loaded and easy to parse. Every sentence adds value—no filler or redundancy. The format is scannable for an agent.

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 an output schema, the description need not explain return values, but it does summarize the return as 'JSON with scores, distribution, top/bottom performers, and recommendations,' which adds context. With readOnlyHint annotation, no safety disclosure needed. The description covers the scoring logic and key parameters, making it complete enough for an agent to use the tool effectively, though it doesn't fully describe pagination context.

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 explain parameters. It does explain section_id and max_items in Args, but page and page_size are only in the schema without added meaning. The description adds semantic context for two of four parameters, partially compensating 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 computes a 0-100 AI Discoverability Score for videos, measures how well content is discoverable by AI systems, and lists specific criteria. It distinguishes itself from siblings like audit_metadata_quality and get_series_seo_map by focusing on AI discoverability scoring with a clear numeric output.

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 its use for evaluating content discoverability by AI, but does not explicitly state when to use it versus alternatives like audit_metadata_quality or get_series_seo_map. It's clear it's a scoring tool, but no explicit 'use when' or 'not for' guidance is given.

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 readOnlyHint=true, and the description aligns with that by describing a mapping/scanning operation. It adds some behavioral context by saying content is scanned from typedTags and that max_items controls items per section, but it does not disclose details like pagination behavior or output shape beyond what the output schema likely provides.

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 compact and front-loaded: a one-sentence purpose, a one-sentence use-case context, and a bulleted Args list. Every sentence adds value and there is no filler.

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?

With four optional parameters, a read-only annotation, and an output schema present, the description covers all necessary invocation details: parameter meanings, defaults, filtering behavior, and intended use cases. Nothing essential is missing for an agent to call the tool correctly.

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 carries the full burden for parameters. It explains all four parameters with defaults, semantics, and even examples for country names in Arabic, including the empty-country behavior. This fully compensates for the schema's lack of 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 maps all content by Related Country from typedTags, which is a specific verb, resource, and scope. It is distinguishable from siblings like get_series_seo_map and get_searchable_tags_map by its country-focused grouping, though it does not explicitly name a sibling to differentiate itself.

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 concrete use cases: geo-targeted SEO, international content strategy, and country-specific landing pages. It does not mention when not to use the tool or name alternatives, but the context is clear enough for an agent to select it appropriately.

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 establish readOnlyHint=true, and the description reinforces a non-destructive operation through words like 'get', 'report', and 'lists'. Beyond annotations, it adds useful behavior: the tool scans typedTags data and has a max_items scan limit, which informs the agent about scope. A minor gap is that 'Lists all content' could be more explicit about how max_items caps or truncates the scan.

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 compact, front-loaded with the core purpose, and uses a short Args block that maps directly to the two schema properties. Every sentence adds value; there is no repetition of the schema or annotation fields.

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?

With only two optional parameters, an output schema present, and a read-only annotation, the description covers the essential behavior, parameter semantics, and intended use case. Nothing critical is missing for an agent to select and invoke this tool correctly.

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 carries the documentation burden for both parameters. It provides concrete meaning for 'genre' with Arabic examples and the 'leave empty for all genres' behavior, and for 'max_items' with 'per section to scan' and a default. This compensates well, though the exact effect of max_items on result completeness could be slightly clearer.

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 uses a specific verb-resource pairing ('Get a full report of content by genre or sub-genre') and immediately clarifies the inputs and output use case ('SEO metadata for building category pages'). The mention of 'typedTags data' adds a distinguishing data source that separates it from generic search or browse siblings.

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 intended use case is clear: generating category pages from genre/sub-genre content and SEO metadata. It does not explicitly name sibling alternatives or state when not to use this tool, but the 'using typedTags data' and 'for building category pages' framing provides enough contextual 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?

The readOnlyHint annotation already marks it as a safe read operation, and the description adds useful behavioral context by mentioning that it scans multiple sections with a per-section item limit (max_items). It does not contradict the 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?

The description is compact and front-loaded: the first sentence states the core action and result, and the Args section is minimal and scannable. Every sentence earns its place with no 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?

With an output schema present and a readOnly annotation, the description covers the essential input semantics and the tool's scanning behavior, which is sufficient for correct invocation. It leaves lookup-failure behavior unspecified, but that is a minor gap given 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?

Although the schema has no property descriptions (0% coverage), the description's Args block explains both parameters: host_name accepts Arabic or English names, and max_items sets the per-section scan cap with a default of 20. This meaningfully compensates for the schema gap, though it omits edge-case details.

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 uses a specific verb with a specific resource: 'Get a complete profile for a show host/presenter' and enumerates the included data (content, genres, shows hosted, SEO-ready host page schema). This clearly distinguishes it from sibling tools like get_series_details or get_video_details, which target different entities.

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 conveys the intended use case—retrieving an aggregated host/presenter profile—so an agent can infer when to call it. However, it does not explicitly mention alternatives or state when not to use it, leaving sibling differentiation mostly to the tool name and listed content.

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 readOnlyHint annotation already declares the tool as safe and read-only. The description adds useful default behavior for section_id ('AJA') and count (10) beyond the raw schema, but it does not disclose ordering, pagination, or empty-result behavior. With annotations covering the main safety profile, this is adequate but not particularly rich.

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 short and front-loaded with the core purpose, followed by a clean Args block. The Arabic sentence repeats the English purpose, which adds some redundancy for an AI agent but remains harmless. Overall, it is concise and well-structured.

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?

This is a simple two-parameter, read-only retrieval tool with an output schema present, so the description does not need to explain return values. The description covers the operation, section scope, and count meaning. It lacks minor behavioral details like ordering or pagination, but for this tool the overall context is sufficient.

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 has no property descriptions, so the Args block is the primary source of semantic meaning. It clearly explains that section_id selects the section and count controls the number of episodes, and it restates defaults. The explanation is complete for both parameters, though it does not go into deeper constraints such as max count or accepted section formats.

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 uses a specific verb and resource: 'Get the latest episodes from a specific section on Al Jazeera 360.' It clearly states the operation and scope possibility. However, it does not explicitly differentiate itself from sibling tools like get_season_episodes or get_trending_content, so it does not fully earn a 5.

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 makes it clear that this tool is for retrieving latest episodes by section, and the Args block adds useful context about section_id and count. It does not mention alternatives or when not to use this tool, so the agent is left to infer how it differs from related episode and browsing 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?

Annotations mark this as read-only, and the description adds useful behavioral context: it scans sections based on max_items, ranks tags, and also returns Related Countries and Genres distributions. This goes beyond the readOnlyHint and gives the agent a realistic sense of what the operation does internally. A minor ambiguity remains about how 'all Searchable Tags' relates to the max_items scan limit, but the added context is substantive.

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 compact and front-loaded: the main purpose appears in the first sentence, followed by one contextual use case and a tightly formatted Args block. There is no filler, tautology, or repetition of the tool name.

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?

With an output schema present, return-value details don't need to be in the description, and annotations already handle the read-only safety signal. Parameter semantics are complete. The only gap is the slight ambiguity between 'all Searchable Tags from the entire catalog' and max_items limiting items per section to scan, which could confuse an agent about the actual coverage of 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 0%, so the description carries the full burden, and it succeeds: every parameter is explained in plain terms. max_items is the per-section scan depth, top_n caps returned tags, while page and page_size handle pagination. This fully compensates for the bare schema and adds real meaning beyond the parameter names and defaults.

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 states a specific verb and resource: 'Extract and rank all Searchable Tags from the entire catalog.' It also frames the output as 'the exact keywords the audience searches for,' which clearly sets it apart from sibling tools like get_series_seo_map or get_trending_topics. The scope and purpose are 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 'ideal for SEO keyword strategy' line gives a clear intended context, but the description never explicitly says when to use this tool versus siblings such as get_series_seo_map, get_trending_topics, or get_country_content_map. There are no exclusions or conditional routing hints, so usage remains implied rather than directly guided.

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 readOnlyHint=true, and the description adds useful behavioral context: season_id comes from get_series_details and max_episodes caps the result count with a default of 20. The phrase 'all episodes' is slightly at odds with that cap, but the cap is disclosed in the Args section, so this is not a serious 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?

The English text is front-loaded with purpose and prerequisite, followed by a clean Args block. The Arabic duplicate is redundant for an agent, but the overall structure is scannable and not padded with filler.

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 simple read-only, two-parameter tool with an output schema, the description supplies the purpose, prerequisite call, and parameter semantics needed to invoke it correctly. The only minor gap is not explicitly reconciling 'all episodes' with the default max_episodes cap, though that cap is documented in Args.

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%, but the description fully compensates by explaining both parameters: season_id is the ID obtained from get_series_details, and max_episodes is the maximum number of episodes to return with its default. This adds real meaning beyond the bare integer types in 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?

The description opens with a precise operation: 'Get all episodes in a specific season.' It names the resource (season episodes), the key identifier (season_id), and clearly separates this from series-level tools like get_series_details and get_latest_episodes.

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 instructs the agent to call get_series_details first to obtain season IDs, giving a clear prerequisite and usage order. It does not list exclusions or alternative tools, so it stops short of a full 5.

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 readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the 'including all seasons' detail, which is useful but minimal. It doesn't describe the return format or any additional behavior, though the output schema exists to cover that. This is acceptable but not rich.

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—two short sentences plus the args line. It front-loads the purpose and includes the example efficiently. Every word earns its place with 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?

For a simple single-parameter read-only tool, the description is largely complete. The output schema handles return values, and the example clarifies the parameter. It could mention what 'detailed information' encompasses, but given the low complexity, it's adequate.

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 only specifies series_id as an integer with no description (0% coverage), so the description's example ('e.g., 2355 for الدحيح') adds concrete value by clarifying the expected value. It also explicitly calls it 'numeric ID,' reinforcing the type. This compensates well for the schema's lack of description.

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 detailed series information, including all seasons, and provides an example ID. It identifies the resource (series) and the verb (get), but it doesn't distinguish from sibling tools like get_series_seo_map or get_series_seo_map, which might also target series details. Without that differentiation, it's clear but not fully distinct.

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 like get_season_episodes or get_series_seo_map. It doesn't mention any conditions, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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 readOnlyHint=true, so the safety profile is covered. The description adds that the tool builds a map including specific elements, which hints at the output scope but does not describe return format, pagination, or any side effects. Given the annotation coverage, this is acceptable but minimal.

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 bilingual, which adds length but serves a broader audience. The core purpose is front-loaded, and the Args section is clearly separated. No unnecessary filler sentences; each part earns its place.

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 an output schema (indicated in context), the description doesn't need to explain return values. It covers the main input, how to obtain it, and the tool's purpose. It lacks explicit error handling or limitations, but for a simple read-only map builder this is sufficient.

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 0%, so the description must compensate. The Args section explicitly states series_id is the series ID and points to get_series_details or search_videos as sources, which is valuable beyond the raw schema. This directly aids correct invocation.

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 builds a complete SEO map for a series, listing included elements (seasons, episodes, internal links, schema). While it doesn't explicitly name a sibling tool, the scope is distinct enough from related tools like generate_series_schema or generate_sitemap that an agent can infer its purpose.

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 Arabic section notes the tool is useful for improving internal linking and series discoverability in search results, providing a clear use case. It doesn't explicitly exclude alternatives or name when not to use it, but the context is sufficient for a single-parameter read-only 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?

Annotations already declare readOnlyHint=trueways, so the read-only safety profile is covered. The description adds useful context about the response scope (featured shows, most-watched videos, latest episodes, editorial picks), but it does not disclose behaviors like sorting, pagination, or regional/language defaults. This is adequate but not rich.

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 short, starts with the core action, and uses a compact enumerated list of result categories. The English/Arabic duplication adds some redundancy for an automated agent but does not inflate the description beyond a few lines.

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?

This is a simple zero-parameter read-only tool with an output schema present, so the description does not need to explain return types. It states the content categories and the read-only annotation covers safety. It is complete for invocation, though a brief usage-routing sentence would make it stronger.

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 has zero parameters and 100% schema description coverage, so there are no parameter details for the description to clarify. The baseline of 4 applies because the agent has nothing to resolve about arguments.

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 uses a specific verb ('Get') and names a concrete resource ('trending and most-watched content on Al Jazeera 360'), then enumerates the returned categories: featured shows, most-watched videos, latest episodes, and editorial picks. It is clear and action-oriented, but it does not explicitly contrast with sibling tools like get_trending_topics or get_latest_episodes.

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?

No guidance is given on when to use this tool versus alternatives. Sibling tools such as get_trending_topics, browse_section, and get_latest_episodes exist, but the description provides no conditions, exclusions, or routing hints. Usage must be inferred entirely from the content-list description.

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 readOnlyHint=true, and the description's 'Analyze... extract' wording is consistent with a read-only operation. It adds modest behavioral context by specifying the outputs (topics, keywords, themes), but it does not disclose data source, recency window, or limitations. This is adequate given the annotation, but not rich.

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 short and front-loaded with the main purpose, followed by a concise Args section. The bilingual repetition is somewhat redundant but not bloated, and the extra Arabic use-case sentence adds value for Arabic-speaking agents. It stays within a reasonable size.

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?

With only one optional parameter, a readOnlyHint annotation, and an output schema present, the description covers the essential invocation details: what the tool does, what top_n means, and a use case. It does not explain the source of 'trending content' or compare with siblings, but given the low complexity this is not a critical gap.

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 carries the burden for top_n. It provides a clear semantic: 'Number of top topics to return (default: 20)', which goes beyond the schema's bare 'Top N' title and default. This fully compensates for the single parameter.

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: 'Analyze trending content and extract the most common topics, keywords, and themes.' This clearly states the tool's function and differentiates it from sibling get_trending_content, which likely returns content rather than an analysis. It does not explicitly name the sibling, so it stops short of a 5.

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 Arabic sentence 'مفيد لاستراتيجية المحتوى وتحديد الكلمات المفتاحية المستهدفة' gives a use case (content strategy and target keyword identification), but it does not say when to prefer this tool over siblings like get_trending_content or get_series_seo_map, nor does it give exclusions. Usage is implied rather than explicit.

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 readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the specific fields returned, which is useful context. However, it doesn't disclose any additional behavioral traits like whether the video must be available in the user's region, whether it returns metadata for unpublished videos, or any rate limits. With annotations covering the safety profile, a 3 is appropriate.

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 and front-loaded with the core purpose. The bilingual repetition (English and Arabic) is somewhat redundant for an AI agent but not harmful. The Args section is clear and minimal. It earns a 4 because the bilingual content adds length without much additional value for an AI agent, but the structure is otherwise efficient.

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?

The tool has an output schema, so return values are already documented structurally. The description adds the key fields (title, description, duration, quality, categories, episode info, watch URL) which helps the agent know what to expect. With one simple parameter, readOnlyHint annotation, and output schema present, the description is nearly complete. The only gap is not specifying where the video_id comes from or any prerequisites, but this is minor for such a simple 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 description coverage is 0%, so the description must compensate. It does: it explains that video_id is 'The numeric ID of the video' and provides an example (953659). This adds meaning beyond the bare schema type 'integer'. The description also lists what the returned details include, which helps the agent understand the parameter's role. However, it doesn't specify the ID's source (e.g., from search results or URLs), which would be slightly more helpful.

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: getting detailed information about a specific video on Al Jazeera 360, and lists the specific fields returned (title, description, duration, quality, categories, episode info, watch URL). This distinguishes it from siblings like get_series_details and get_latest_episodes, which target different resources.

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 context: use this when you need details about a specific video by its numeric ID. It doesn't explicitly state when not to use it or name alternatives, but the clear resource focus (video vs series vs episodes) provides adequate guidance. The bilingual text reinforces the intended use case.

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 readOnlyHint=true, so the safety profile is known. The description adds behavioral detail beyond annotations: it states that the tool 'Returns section IDs with their Arabic names', which informs the agent about the output format. It also implies 'all available' sections, indicating no filtering or pagination, which is useful context. No contradictions with 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?

The description is concise in each language, with two short sentences. It front-loads the purpose and immediately explains the return value and relationship to browse_section. The bilingual duplication adds length but is intentional for an Arabic platform; still, it slightly reduces conciseness for English-only agents. Otherwise, every sentence earns its place.

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?

The tool is simple (no parameters) and has an output schema, so the return structure is defined externally. The description fully covers the purpose, the output semantics (section IDs and Arabic names), and the intended downstream usage (browse_section). Nothing an agent needs to call this tool correctly is missing, and the relationship to siblings is clear.

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 has zero parameters, and the schema already reflects that with an empty properties object (100% schema coverage). Per the rubric, a zero-parameter tool receives a baseline of 4 because there is nothing to explain beyond what the schema provides. The description correctly focuses on the output and usage rather than parameters.

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 action: 'List all available sections and channels on Al Jazeera 360.' It identifies the specific resource (sections and channels) and the platform, and explicitly ties its output to the sibling tool 'browse_section', making its purpose distinct from other listing tools. The bilingual text reinforces the meaning without ambiguity.

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 says the output is 'for use with browse_section', which clearly indicates when to use it (as a prerequisite to browsing a specific section). It does not explicitly state when not to use it or mention alternatives, but the context is strong enough that an agent can infer its role among the siblings. A minor omission is lack of explicit exclusions, hence a 4.

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 readOnlyHint=true already signaling a non-mutating operation, the description adds useful environment-specific behavior: it opens a player in interactive views and returns a watch link elsewhere. It does not fully explain output details or failure cases, but the output schema likely covers those, so the added context is meaningful without contradiction.

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 compact and front-loads the core purpose, then explains behavior, usage, and the argument. The bilingual repetition doubles length but is appropriate for an Arabic/English product; no unnecessary fluff is present.

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 single-parameter tool with an output schema, the description covers what the tool does, how it behaves in different environments, when to use it, and where the argument value comes from. Nothing an agent needs to invoke it correctly is missing.

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 0%, so the description must compensate, and it does: video_id is described as 'The numeric ID of the video' and its provenance is given. This adds real meaning beyond the bare integer type, though it remains somewhat terse and gives no format or range details.

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 uses a specific verb and resource ('Play a video from Al Jazeera 360') and clearly distinguishes environments: interactive MCP apps open the official player, elsewhere it returns the watch link. This makes the tool's purpose unmistakable and separates it from sibling tools like search_videos or get_video_details.

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 explicitly states when to use the tool: 'Use when the user asks to watch or play something.' It also tells the agent where the video_id comes from ('from search_videos, browse_section, etc.'). It does not enumerate alternatives or exclusions, but for a playback tool the guidance is sufficiently 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?

Annotations already declare readOnlyHint=true, so the description doesn't need to cover safety. It adds that the tool runs checks via an 'interactive view' and shows results, and notes the report parameter is internal. This is useful but not rich; it doesn't detail the exact check steps or output format, though the output schema likely covers that.

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 purpose and usage, but includes a full bilingual repetition (English and Arabic) that adds length without new information. Still, it's concise and well-structured, with the Args section clearly separated.

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 simple diagnostic tool with no required parameters and an output schema present, the description covers purpose, usage, and the only parameter. It doesn't discuss prerequisites or limitations, but given the tool's simplicity, this is adequate.

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 0% schema description coverage, the description must compensate. It does: it explains the 'report' parameter is internal, filled by the interactive view, and instructs the agent to leave it empty. This adds meaning beyond the schema's bare title and default.

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 states a specific action ('Check whether this AI app can play Al Jazeera 360 videos inside the chat') and includes key details like DRM, official player embedding, and fullscreen. It clearly distinguishes from sibling tools like play_video by framing itself as a diagnostic check rather than a playback action.

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 explicitly says 'Use when the user says a video does not play in the chat' and instructs to call it with no arguments, noting the interactive view runs checks. This provides clear contextual guidance without ambiguity, even if alternatives aren't named.

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 readOnlyHint=true, lowering the bar for safety disclosure. The description adds useful behavioral context (platform scope, Arabic/English support, optional content-type filtering) but omits result ordering, pagination behavior, or failure modes. Consistent with the calibration example, annotations carry the safety profile and the description adds moderate value without contradiction.

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 purpose is front-loaded and the Examples and Args sections are well organized, but the entire English opening is duplicated verbatim in Arabic, roughly doubling length with no additional information for an agent-consuming definition. The bilingual content belongs in the annotation title, not repeated in full.

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 3-parameter tool with an output schema present, the description covers query semantics, language support, filter values, and result caps — enough for correct invocation. Gaps are minor: no pagination or ordering disclosure and no guidance on alternative discovery tools, leaving a slightly incomplete picture for an agent facing the sibling set.

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 compensate, and it largely does: the Args section gives content_type real meaning ('VOD' for single videos, 'SERIES' for series only, None for all) that the bare string-or-null schema lacks, and query examples clarify the search term semantics. Only max_results adds little beyond the schema's own default.

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 opening sentence states a specific verb and resource: 'Search for videos, documentaries, and programs on Al Jazeera 360.' The bilingual search capability and content-type filtering are clearly stated, and concrete examples ('فلسطين', 'غزة', 'الدحيح') make the scope unmistakable. No sibling tool is a search tool, so this is implicitly distinguished from browse_section, get_trending_content, and get_latest_episodes.

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?

Usage is implied through examples and the stated bilingual/filter capabilities, but the description never says when to use this tool versus alternatives such as browse_section or get_trending_content for discovery without a keyword. There is no when-not-to-use guidance or explicit alternative routing, so an agent must infer the selection rule.

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