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

No annotations are provided, so the description must disclose behavioral traits. It states 'Creates a timestamped snapshot' but omits potential failures, rate limits, or async behavior. Minimal info on side effects or limitations.

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 concise one-line action, bullet-pointed use cases, sample prompts, and a single param line. No unnecessary text, front-loaded with the main purpose.

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 one-parameter tool with an output schema, the description covers main purpose and usage scenarios. It lacks mention of what the output contains (e.g., archive URL) and any prerequisites like authentication, but overall is fairly complete given the tool's simplicity.

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 single parameter 'url' is described as 'The URL of the webpage to archive.' Given 0% schema description coverage, this adds needed semantics. However, it could be more detailed (e.g., format requirements, supported protocols).

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 'Archive a webpage on archive.today (archive.is) for permanent preservation' and lists specific use cases like preserving news articles or product pages. This distinguishes it from sibling tools like visit_page or google_search.

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 sample prompts and suggests when to use (e.g., preserving articles, social media posts), but does not include exclusion criteria or mention alternative tools for similar tasks. Lacks explicit 'when not to use' 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?

With no annotations, the description carries the burden. It mentions 'Fetches and stores latest items', indicating a side effect. However, it does not disclose potential delays, network usage, whether repeated calls are safe, or any rate limits. It provides moderate transparency but lacks depth.

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 relatively concise but includes sample prompts that are not essential. The structure places purpose first, followed by examples and parameter details. It could be more concise by removing the sample prompts, but it is not overly verbose.

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 description covers the basic operation and parameter. However, given the availability of an output schema (not shown but present), it does not explain what happens if no new items, error handling, or how it interacts with related tools. For a simple tool it is adequate but not comprehensive.

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 0% description coverage, so the description must compensate. It explains the single parameter 'source_type' with a list of valid values (news, reddit, etc.) and the default behavior (empty checks all). This adds meaningful semantics beyond the schema's type string.

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: 'Check all (or specific) subscriptions for new content. Fetches and stores latest items.' The verb 'check' is specific to fetching new items, and the resource 'subscriptions' is clear. It distinguishes from sibling tools like 'get_feed_items' (which likely retrieves stored items) and 'list_subscriptions' (which lists feeds).

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 sample prompts that hint at usage but does not explicitly state when to use this tool versus alternatives like 'get_feed_items' or 'subscribe'. No guidance on when not to use or prerequisites (e.g., must have subscriptions).

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 explains the conversion process (using FFmpeg), lists supported formats, and notes default output path behavior. It does not disclose potential limitations like encoding flags or error handling, but covers the core behavior adequately.

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 header, format list, common conversions, sample prompts, and parameter list. It is thorough yet concise, with every sentence adding 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 the tool's complexity (4 parameters, conversion functionality), the description covers purpose, formats, examples, and parameters comprehensively. An output schema exists, so return values are not needed. 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?

The schema has 0% coverage, but the description lists all parameters in the Args section, explaining output_path default and quality levels. This adds meaning beyond the schema properties and compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts audio/video files between formats using FFmpeg, lists supported formats, and provides common conversion examples. This distinguishes it from siblings like extract_video_clip or transcribe_local.

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 sample prompts and lists common conversions, which implicitly guides when to use the tool. However, it does not explicitly state when not to use it or compare with alternatives, though the sibling list provides context.

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, so description carries full burden. It discloses buffer addition and clip saving location (~/clips/). Could mention file overwrite or permission requirements, but current details are sufficient for typical usage.

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?

Description is well-structured with a clear purpose, usage context, sample prompts, and parameter list. Front-loaded with key information. A bit lengthy but each 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 5 parameters and no annotations, the description covers essential aspects: source, timestamps, buffer, output. Output schema exists, so return value details are not needed. Complete for its purpose.

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 description adds value by explaining each parameter in the Args section, including defaults for buffer_seconds and output_filename. This compensates for missing 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?

Description clearly states the tool extracts a video clip by topic from a YouTube video or local file. Verb 'extract' and resource are specific, and it distinguishes from siblings like transcribe_video and convert_media.

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 'Used after transcribe_video' and describes the workflow where the LLM reads transcript to find timestamps. Also provides sample prompts, giving clear when-to-use 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?

No annotations provided; description misses key behavioral traits like marking emails as read or network dependencies. Mentions auto-detection but not side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections, though sample prompts add length. Every sentence adds value; front-loaded with provider info.

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?

Complex tool with 6 parameters and no explicit return value description despite having an output schema. Does not explain output format, leaving agent guessing.

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 0% description coverage, but description explains all 6 parameters in detail with examples for search. Compensates fully for schema gaps.

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 emails via IMAP and lists supported providers. It distinguishes from diverse sibling tools like google_search or ocr_image.

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?

Provides sample prompts and provider-specific instructions (e.g., Gmail app passwords). Lacks explicit exclusions or when-not-to-use guidance, but context 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?

No annotations provided, so description must fully disclose behavior. It mentions generating an image and output path but does not discuss file overwriting, error states, or system impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections for use cases, sample prompts, and args. Concise without being terse; each 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?

Tool is simple; description covers inputs and usage. Output schema exists (not shown) so return values are likely clear. Sufficient for a QR generation 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 no descriptions (0% coverage), but the description explains all three parameters (data, output_path, size) with defaults, compensating for schema gaps.

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 generates QR codes from various data types (text, URLs, Wi-Fi, etc.). Differentiates from all sibling tools (none do QR generation).

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?

Provides use cases and sample prompts that indicate when to use it. No explicit exclusions, but siblings are all different, so no confusion.

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 provided; description is straightforward but does not disclose side effects, read-only nature, or other behavioral traits beyond the basic operation.

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?

Concise and structured: main sentence, sample prompts, then Args section. Every part 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?

Explains parameters adequately and has output schema available. Lacks detail on return format but is sufficient for a simple list 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?

With 0% schema description coverage, the description adds valuable meaning: source_name examples, source_type list, and limit default. This compensates for the schema gaps.

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 the verb 'Get', resource 'recent items from feed subscriptions', and filters by 'source' and 'type'. The description distinguishes from siblings like 'search_feeds' and 'list_subscriptions'.

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?

Provides sample prompts that demonstrate when to use the tool, giving clear context. However, it does not explicitly state when not to use or mention alternatives.

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 provided, so description must cover behavior. It indicates a search operation with result count limits. Lacks details on read-only nature, rate limits, or result structure, but is adequate for a simple query 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?

Description is well-structured: purpose statement, example prompts, then parameter details. Concise but includes relevant examples; front-loaded with purpose. Minor redundancy in examples.

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 values need not be described. Covers main functionality and parameter constraints. Could mention result format or sorting, but acceptable given tool simplicity.

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 description adds critical meaning: 'query' explained as the search string, 'num_results' with default and max. This compensates well for the missing 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?

Clearly states it searches Google Books for books, textbooks, and publications. Includes sample prompts that illustrate the tool's scope, and the purpose is distinct from siblings like google_search or google_scholar.

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 explicit guidance on when to use this tool vs alternatives. Sample prompts imply usage for book-related queries, but no 'when not to use' or comparison to siblings is provided.

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 carries full burden. It correctly indicates a read-only lookup operation. While it does not detail rate limits or authentication, the behavior is straightforward and non-destructive. The existence of an output schema partially compensates for omitted return details.

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 very concise: two sentences plus a list of sample prompts and an args section. Every part adds value without redundancy. It is well-structured and easy to parse.

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 lookup tool with one parameter and an existing output schema, the description adequately covers purpose, parameter formatting, and examples. It does not discuss error handling or data freshness, but these are minor omissions given the tool's simplicity.

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 0% coverage, so the description must compensate. It explicitly defines the 'query' parameter with format ('Stock ticker with exchange') and examples (e.g., 'AAPL:NASDAQ'), and allows company names. This adds significant meaning beyond the bare 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 clearly states the verb ('look up') and the resource ('stock prices, market data, and company information') on Google Finance. Sample prompts reinforce the purpose and distinguish it from sibling tools like google_search, which are for general web searches.

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 through sample prompts (e.g., 'What's Apple's stock price?'), but it does not explicitly state when to use this tool versus alternatives like google_search. No exclusion criteria or prerequisites are mentioned.

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 must convey behavioral traits. It only states 'search' without mentioning rate limits, data freshness, caching, or error handling. The agent lacks important operational context.

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 with a lead sentence, sample prompts, and a clear args section. No wasted sentences; information is front-loaded and well-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?

Given 4 parameters and an output schema, the description covers the essential use case and input formats. It lacks details on edge cases or return value interpretation, but the output schema may fill that 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?

With 0% schema description coverage, the description adds crucial meaning for each parameter: origin, destination, date, and return_date with examples. This compensates well for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search Google Flights for flight options, prices, and travel times', specifying the verb and resource. It differentiates well from sibling tools like google_hotels (hotels) and google_search (general search).

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?

Sample prompts illustrate typical usage but there is no explicit guidance on when not to use this tool or alternatives. The description implies usage through examples but lacks exclusions or context for complex queries.

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 provided; description implies read-only search but does not explicitly state safety or side effects. Does not mention authentication or rate limits.

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?

Concise structure with clear introductory sentence, helpful sample prompts, and organized Args section. No unnecessary 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?

Covers purpose, parameters, and expected outputs. Lacks details on sorting or filtering but adequate for a simple search tool. No output schema needed.

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?

Description includes detailed parameter explanations for both 'query' (with examples) and 'num_results' (with defaults and max), fully compensating for 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb 'Search for hotels and accommodation' with specific outputs (thumbnail images, prices, ratings, booking URLs). Distinct from sibling tools like google_flights and google_maps.

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 by tool name and description; sample prompts give context but no explicit when-to-use vs alternatives or when-not-to-use. No exclusions provided.

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 should fully disclose behavioral traits. It states the tool returns thumbnails and source URLs but does not mention authentication, rate limits, or any limitations (e.g., no adult content filtering, no API quotas). This lack of context reduces transparency.

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, with a main purpose sentence followed by output characteristics and sample prompts. The sample prompts, while helpful, slightly lengthen the description. The essential information is front-loaded, and 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 the tool's simplicity (2 parameters, no output schema), the description adequately covers purpose, parameters, and return format (thumbnails and source URLs). It does not cover error handling or edge cases (e.g., no results), but for a straightforward search tool, it is mostly 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?

The input schema has 0% coverage, meaning the description must explain parameters. The description includes an 'Args' section that clearly defines query as 'The image search query string' and num_results as 'Number of image results to return (default 5, max 10).' This adds meaningful context beyond the schema's type and title.

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 'Search Google Images and return images inline in chat.' It specifies a specific verb (search) and resource (Google Images), and the sample prompts reinforce this. It distinguishes itself from siblings like google_search (web results) and google_lens (image recognition).

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 sample prompts that imply when to use (e.g., 'Show me images of...'), but it does not explicitly state when not to use or mention alternative tools. The context signals include many sibling tools, but no guidance is given to differentiate them.

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 supported input formats (URLs, file paths, base64) and the tool's general purpose. However, it omits behavioral details like rate limits, authentication, error handling, or what happens when no match is found.

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 yet informative: a clear opening sentence, context for text-only models, sample prompts, and a parameter description. Each sentence adds value, and the structure is easy to scan.

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) and the presence of an output schema, the description is fairly complete. It covers input formats and usage scenarios. However, it could briefly mention the return value or typical result structure to be fully self-contained.

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, 'image_source', is clearly described as accepting 'a public image URL, local file path, or base64-encoded image data.' Since the input schema lacks a description field (0% coverage), the description adds all necessary meaning and fully explains the parameter's acceptable values.

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 action: 'Reverse image search using Google Lens.' It lists specific identification capabilities (objects, products, brands, landmarks, text) which distinguishes it from generic search tools. However, it does not explicitly differentiate it from similar sibling tools like 'google_images' or 'ocr_image'.

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 sample prompts that trigger the tool, offering clear contextual guidance on when to use it. It also explains that it gives vision capabilities to text-only models. However, it does not state when not to use it or mention alternatives among sibling 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?

No annotations are provided, so the description carries the full burden. It explains the internal process (OpenCV detection, cropping, sending to Google Lens) and that it returns identification results for each object. However, it does not disclose error handling or behavior when no objects are detected, which is a 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 description is well-structured with clear sections: overview, step-by-step process, use cases, supported formats, and sample prompts. 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?

The description covers purpose, usage, parameter details, and sample prompts. Since an output schema exists, not explaining return values is acceptable. Missing error handling or performance notes, but overall fairly complete for a single-parameter 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?

The schema has 0% description coverage, but the description adds value by stating that image_source supports local file paths and base64-encoded image data, going beyond the schema's type 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 detects and identifies all objects in an image using OpenCV and Google Lens. It distinguishes itself from the sibling tool google_lens by detailing the multi-step process (detect, crop, send both original and crops).

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 compares with google_lens and provides sample prompts that illustrate when to use this tool (e.g., multiple items in an image). It also mentions supported input types (local paths and base64), giving clear context for use.

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 provided, so description carries full burden. It discloses output format (ratings, prices, map screenshot) and default/max results. Lacks discussion of authentication, rate limits, or query limitations.

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?

Description is concise with a main sentence, sample prompts, and structured args. Every part serves a purpose 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?

No output schema, but description covers return types (ratings, prices, addresses, map screenshot). Parameters are explained. Missing some context like pagination or error handling, but sufficient for a search 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 coverage is 0%, so description adds crucial meaning: query is a place search query with examples, num_results has default 5 and max 10. The description explains both parameters effectively beyond the bare 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 clearly states 'Search Google Maps for places, restaurants, businesses, and locations' and lists return data (ratings, prices, addresses, map screenshot). It differentiates from siblings like 'google_maps_directions' which is for directions.

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?

Provides example prompts that implicitly indicate appropriate use cases. However, it does not explicitly state when not to use or mention alternatives like 'google_maps_directions' for directions.

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. The description mentions it returns 'route info and a map screenshot' but lacks details on rate limits, authentication, or data format. It covers basic behavior but not comprehensively.

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?

Description is brief and well-structured: intro sentence, example prompts, and arg definitions. Every sentence is useful, 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?

For a simple 3-parameter tool with no output schema, the description is mostly complete: it explains inputs, examples, and basic output. It lacks return value specifics but that's acceptable given complexity.

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%, but the description adds meaning: origin/destination as 'address, city, or place name', mode with list of options ('driving', 'walking', 'transit', 'cycling') and default 'driving'. This compensates for the bare 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 explicitly states the tool gets driving/walking/transit/cycling directions between locations, with sample prompts that cover various modes. It clearly distinguishes from sibling tools like google_maps which likely handle place searches.

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?

Sample prompts provide clear usage context (e.g., 'Get directions from Berlin to Munich'). However, it does not explicitly state when not to use this tool or mention alternatives, though siblings like google_maps imply distinction.

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 must convey behavioral traits. It mentions returning 'recent headlines, articles, and article images' but does not clarify what 'recent' means, any rate limits, date range constraints, or response structure. Missing details on output format or authentication needs.

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 with a one-sentence summary, followed by sample prompts and clear parameter documentation. No unnecessary content, well-organized, and front-loaded with essential purpose.

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 low complexity with only 2 parameters, and the description covers basic functionality. However, without an output schema, the description should more explicitly detail what is returned (e.g., fields, links, summary). It mentions 'headlines, articles, and article images' but lacks specifics on the structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0% description coverage, so the description compensates by explaining the query parameter as 'The news search query string' and documenting num_results with default and max values. This adds meaningful guidance beyond the bare 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 clearly states the tool searches Google News for recent headlines, articles, and images. Sample prompts illustrate the use case, and the verb 'Search' combined with the resource 'Google News' distinguishes it from sibling tools like general search or other Google services.

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?

Sample prompts provide clear use cases for news queries. However, the description does not explicitly state when to prefer this over sibling tools or mention situations to avoid, though the tool name and context imply news-specific usage.

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 provided, and description lacks behavioral details such as authentication, rate limits, or output format. The existence of an output schema is not mentioned, and description does not disclose operational constraints.

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?

Description is concise with front-loaded purpose, sample prompts, and parameter explanations. 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?

For a simple search tool with two parameters and an output schema, description adequately explains parameters. Lacks usage context like when to prefer this over other search tools, but overall 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?

Since schema has 0% description coverage, description adds meaning by explaining query and num_results (default, max). However, could include more detail on query syntax or accepted 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?

Description clearly states the tool searches Google Scholar for academic papers, citations, and research. It distinguishes from sibling tools like general web search.

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?

Sample prompts illustrate typical usage, but no explicit guidance on when to use versus alternatives (e.g., google_search, google_books). No when-not-to-use advice.

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 fully describes behavior: returns titles, URLs, snippets, and supports filters like time range, site, language, and region. It doesn't mention rate limits or error handling, but for a search tool it is sufficiently 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 well-structured with a clear purpose, sample prompts, and parameter descriptions. It is comprehensive but slightly verbose with 7 sample prompts; however, they add value and the overall length is justified.

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 7 parameters and no annotations, the description covers each parameter with examples and allowed values, explains the output format, and includes sample prompts. An output schema exists but isn't shown; the description nonetheless provides sufficient context 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 detailed explanations for all 7 parameters, including defaults, allowed values (e.g., time_range options), and examples (e.g., site). This goes well beyond the schema, which only provides types and defaults, giving the agent clear guidance on usage.

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: 'Search Google and return results with titles, URLs, and snippets.' It provides specific verb and resource, and the sample prompts show various use cases, distinguishing it from sibling tools like google_images or google_maps.

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 includes sample prompts that indicate typical use cases (e.g., searching for web frameworks, Reddit discussions, recent news). However, it does not explicitly state when not to use this tool or provide direct comparisons to sibling tools, leaving some ambiguity.

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 must fully disclose behavioral traits. It only describes return content (prices, stores, ratings) but omits authtication needs, rate limits, error responses, or any side effects. For a search tool, this minimal disclosure is insufficient.

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?

Description is concise: one-line purpose, then sample prompts, then parameter list. Every sentence adds value, no redundancy. Front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, no output schema, no annotations), the description covers the purpose, parameters, and typical usage via examples. It mentions return fields (prices, ratings) but could be more explicit about the return format. Still adequate for most use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no descriptions (0% coverage), so the description adds crucial meaning: 'query: The product search query string. num_results: Number of results to return (default 5, max 10).' This clarifies both parameters beyond bare type/default information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Search Google Shopping for products with prices, stores, ratings, and product images.' This distinguishes it from sibling tools like google_search (general web) and google_flights (travel), by specifying the resource (Google Shopping) and the product-oriented scope.

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?

Provides explicit sample prompts that trigger the tool, e.g., 'Find the cheapest MacBook Air,' which illustrates typical use cases. However, it does not mention when not to use this tool or suggest alternatives, leaving a slight gap for an AI agent deciding between similar 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?

No annotations are provided, so the description carries the full burden. It mentions that 'from_language is optional, auto-detected if empty', but lacks details on rate limits, authentication, or side effects. Minimal behavioral disclosure.

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 sample prompts and an Args list. It is slightly verbose due to examples, but every sentence adds value. No wasted 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?

For a simple translation tool with an output schema, the description covers the essential behavior: what it does, required parameters, and sample usage. It could mention the return format, but the output schema presumably handles that.

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 explains each parameter's role (text, to_language, from_language) and notes the optionality of from_language. This adds meaning beyond the schema's type and title fields.

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 'Translate text from one language to another using Google Translate', with a specific verb and resource. It distinguishes from all sibling tools, as none are translation-related.

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?

Sample prompts effectively illustrate when to use the tool, such as translating phrases or asking for meanings. No explicit alternatives or exclusions are given, but the context 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?

No annotations exist, so the description must disclose behavioral traits. It does not mention rate limits, data freshness, authentication needs, or output structure (though an output schema exists). The description is too brief to compensate for missing 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 short and front-loaded with the core purpose followed by useful sample prompts. Each sentence adds value, though the sample prompts could be considered slightly redundant.

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 (single input, output schema exists), the description is adequate but misses explanations of how to interpret interest over time or limitations. The samples partially compensate.

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 coverage, the description adds a brief clarification: 'query: The topic or search term to check trends for.' This provides basic meaning but lacks details like expected format or examples beyond the provided prompts.

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 'Check Google Trends for a topic to see interest over time, related topics, and related queries.' This verb+resource construction provides a distinct purpose differentiated from sibling tools like google_search or google_news.

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?

Sample prompts illustrate appropriate use cases (e.g., trending tech, comparing Python vs JavaScript). While explicit when-not-to-use or alternative tools are not mentioned, the samples provide strong 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?

No annotations are provided, so the description must cover behavioral traits. It states the tool gets current conditions and forecast, but does not elaborate on the forecast duration, response format, or any limitations (e.g., rate limits, region coverage). Since an output schema exists, the return values are handled externally, but the description lacks depth in behavioral context.

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 structured with a main statement, a list of sample prompts, and an args section. The sample prompts are valuable but take up space; they could be condensed. Overall, it communicates the needed information without excessive 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, clear purpose) and the existence of an output schema, the description is fairly complete. It covers what the tool does and how to invoke it. However, it omits any details about the forecast scope (e.g., hourly, daily) or potential caveats (e.g., coverage for specific regions).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage for the only parameter 'location'. The description compensates well by specifying it as 'The city or location to get weather for' and providing concrete examples (e.g., 'Dubai', 'London, UK'). This adds meaning beyond the schema. However, it could be slightly more precise about acceptable 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 clearly states the tool's purpose: 'Get current weather conditions and forecast for any location.' It uses a specific verb ('Get') and resource ('weather conditions and forecast'), and the sample prompts reinforce this. Among siblings, there is no other weather tool, so differentiation is not an issue.

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 sample prompts that implicitly suggest when to use the tool (e.g., for weather queries like 'What's the weather in Dubai?'). However, it does not explicitly state when not to use it or direct the agent to alternatives. With 37 sibling tools, explicit usage guidance would be beneficial.

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 provided, so description carries full burden. States it lists images but omits details like file type filtering, recursion, or read-only nature. Adequate but could disclose more behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with main purpose, context, sample prompts, and args. Slightly verbose with prompts but front-loaded and clear.

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?

Covers use case, default, and sample triggers. Output schema exists (not shown) so return values assumed covered. Adequate for a simple list 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 coverage is 0%, but description adds default directory and parameter purpose. Explains directory param defaults to ~/lens/. Adds value beyond empty 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 clearly states the tool lists image files in a directory for passing to google_lens. It uses specific verb 'list' and resource 'image files in a directory', and distinguishes from siblings like google_images or ocr_image.

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?

Provides clear context: user saves image to ~/lens/ and asks to identify it, with sample prompts. Lacks explicit when-not or alternatives, but context implies appropriate usage.

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 indicates the tool lists active subscriptions with counts, implying a read operation, but does not explicitly confirm it is read-only or mention authentication or side effects. Adequate but could be more 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 a single sentence with three sample prompts, making it concise and front-loaded. Every element serves a purpose, and the structure is efficient for AI consumption.

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 (no parameters, has output schema), the description is complete. It specifies the scope ('active feed subscriptions' with counts) and provides usage prompts, adequately informing an AI agent.

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?

There are no parameters, and schema coverage is 100%. Per the guidelines, the baseline is 4. The description does not need to add parameter information, and it appropriately provides context about what the tool returns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists active feed subscriptions with item counts, specifying the verb (list) and resource (subscriptions). This distinguishes it from siblings like subscribe, unsubscribe, and get_feed_items.

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 includes sample prompts that illustrate when to use the tool, such as 'Show my subscriptions' and 'What feeds am I following?'. However, it does not explicitly state when not to use it or list alternatives, which could be improved.

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

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

With no annotations, the description bears full burden. It explains the local OCR engine and supported input types but does not disclose limitations (e.g., language support, accuracy, behavior on unreadable images). This is adequate but not comprehensive.

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 one-liner, technical details, and sample prompts. Every sentence adds value. It is concise without omitting necessary 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 single-parameter tool with an output schema, the description covers the input, technology, and usage examples. It misses potential details like language support or error handling, but overall it is sufficiently complete for effective usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It does so excellently: 'image_source: Local file path or base64-encoded image data.' This adds critical meaning beyond the schema's bare type string.

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 'Extract text from an image using local OCR.' It specifies the verb (extract), resource (text from image), and distinguishes from siblings by emphasizing local execution and no internet needed. Examples of use cases further solidify 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 description provides context like 'No internet connection needed' and sample prompts, which imply when to use. However, it lacks explicit when-not-to-use advice or comparisons to sibling tools like 'read_document', which could be alternatives.

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 responsibility. It mentions automatic expiry and no auth requirement, but does not disclose rate limits, size limits, or error handling. 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 opens with a clear one-liner, followed by a brief use-case sentence and sample prompts. The Args section is structured. The sample prompts add length but are helpful for prompting. Could be slightly more terse.

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 paste tool with 4 parameters and an existing output schema, the description covers purpose, usage, and parameter meanings. Minor gap: no mention of paste size limits or rate limiting, but not critical for typical use.

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. It explains that 'content' is the text to paste, 'title' is optional, 'syntax' is for highlighting (with examples), and 'expiry_days' is days until expiry with a default. This adds meaningful guidance 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 starts with a clear action ('Post text to dpaste.org and return a shareable URL') and lists sample prompts that illustrate typical use cases. Siblings include no similar paste tool, so differentiation is implicit.

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 states when it is great for (sharing code, logs, configs) and notes that no account or API key is needed. It does not explicitly exclude alternatives, but given the sibling list, no alternative exists.

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 provided, but the description discloses important behaviors: use of pdftotext with OCR fallback for scans, paragraph/table extraction in Word, tag stripping in HTML. This adds value beyond the 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 well-structured: concise purpose statement, bulleted format list, sample prompts, and argument definition. Front-loaded with key information, no wasted sentences.

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 presence of an output schema, the description adequately covers the tool's capabilities. It explains supported formats and argument semantics, though it could optionally mention error handling or encoding details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description's 'Args' section defines 'file_path' as 'Absolute path to the document file,' adding necessary semantics 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 clearly states the tool reads and extracts text from documents, lists supported formats (PDF, Word, plain text, HTML), and distinguishes itself from siblings like 'ocr_image' which handles images. It is specific with verb and resource.

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 usage context through sample prompts and format listings, implicitly guiding when to use. However, it does not explicitly state when not to use or suggest alternative tools, leaving some ambiguity.

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, so description carries full burden. It discloses support for FTS5 syntax, optional source_type filter, and default limit. Does not mention error handling or performance, but adequate for a search 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?

Concise and well-structured: purpose, sample prompts, then Args. Every sentence adds value. Front-loaded with purpose.

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 3 params, no enums, and output schema exists, description covers usage and parameters. Missing enumeration of source_type values, but otherwise 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 has 0% description coverage. Description adds meaning: query supports FTS5 syntax, source_type can limit to one type (but does not list options), limit with default. Adds value beyond schema names.

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 'Full-text search across all stored feed content' with examples of content types. Sample prompts and differentiation from siblings like 'get_feed_items' and 'search_transcript' are implicitly clear.

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?

Provides sample prompts indicating when to use, but does not explicitly state when not to use or compare to alternatives like 'search_transcript'.

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?

Discloses read-only behavior (searches existing content), case-insensitive matching, and context window. No annotations provided, so description carries full burden. Could mention it does not modify the transcript.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a clear purpose statement, important caveat, sample prompts, and parameter blocks. Every sentence is informative and economical.

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?

Fully explains purpose, prerequisites (transcribe_video), usage flow (leading to extract_video_clip), and parameter details. Output schema existence means return values are covered elsewhere.

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?

All four parameters are explained with context: url links to transcribe_video, query is case-insensitive, model_size must match, context_segments defaults to 2. Schema had 0% coverage, so description adds significant value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches an existing transcript for keyword matches, explicitly distinguishes itself from transcribe_video, and provides sample prompts for clarity.

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 when to use (search existing transcript) and when not (does not transcribe), and directs to transcribe_video as prerequisite. Sample prompts reinforce correct usage.

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 must carry the full burden. It discloses key traits like external dependency (TinyURL) and no authentication, but omits details on rate limits, error handling, or return format.

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 with no wasted words, front-loads the action and key convenience (no API key), and includes relevant sample prompts that aid the AI 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?

For a simple tool with one parameter and an output schema (not shown), the description covers the essential usage scenario, sample prompts, and external dependency, making it adequately 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?

With 0% schema description coverage, the description provides a basic explanation for the single parameter ('The URL to shorten'), which adds some meaning beyond the parameter name, but not extensive detail.

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 shortens URLs using TinyURL, with a specific verb and resource. It distinguishes itself uniquely among siblings as the only URL shortening tool.

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 by stating 'No account or API key needed' and includes sample prompts that illustrate when to use it, but it does not explicitly mention when not to use or compare to alternatives.

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 provided, so description carries burden. It explains the subscription action and workflow but does not disclose idempotency, rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose first, then details. Slightly verbose with many examples, but each section 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?

Covers all necessary aspects: what, when, how, parameters, and ties to sibling tools. Output schema existence reduces need for return value details.

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 0% description coverage, but description gives exhaustive per-type identifier formats and allowed values for source_type, fully compensating.

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 subscribes to a content source for monitoring and search, listing specific types. It distinguishes from siblings like check_feeds and unsubscribe.

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?

Provides explicit workflow (subscribe then check_feeds, search_feeds) and sample prompts. Lacks explicit when-not-to-use but context 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?

No annotations are provided, so the description carries the full burden. It discloses format support (any FFmpeg-decodable format) and caching behavior (repeat requests are instant). However, it omits critical behavioral details such as error handling for nonexistent files, required permissions, processing time, or whether the operation is destructive. The caching note is helpful but incomplete.

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 main sentence, followed by supported formats, caching note, sample prompts, and argument list. It is front-loaded with the essential purpose. The sample prompts are slightly redundant but do not significantly harm conciseness. Minor improvements could be made by removing repetitive examples.

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 moderate complexity (transcription with model and language options) and the presence of an output schema (so return values need not be detailed), the description covers the key aspects: file, model sizes, language detection, and caching. However, it lacks details on defaults for model_size and language (though they are in schema), and does not mention potential timeout or resource constraints for large files.

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 adds significant meaning beyond the input schema, which has 0% coverage. It clarifies that 'file_path' must be an absolute path, explains 'model_size' values (tiny/base/small/medium/large) with a default, and specifies that 'language' is optional and auto-detected if empty. This compensates well 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 identifies the tool's action ('Transcribe'), the resource ('local audio or video file'), and the method ('using Whisper with timestamps'). However, it does not explicitly distinguish itself from the sibling tool 'transcribe_video', which likely processes remote or different sources. The name provides some differentiation, but the description misses the opportunity to clarify the scope.

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 includes sample prompts but fails to provide guidance on when to use this tool versus alternatives like 'transcribe_video' or other media tools. There is no mention of prerequisites (e.g., FFmpeg installation) or situations where a different tool would be more appropriate. The agent is left to infer usage context from the tool 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?

With no annotations, the description fully covers behavior: downloads audio, transcribes locally with Whisper, returns timestamped transcript, caches results, and auto-downloads models. It also lists model sizes with trade-offs, ensuring the agent understands resource usage and performance.

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 concise purpose, logical breakdown of behavior, caching, model sizes, sample prompts, and parameter details. Every sentence adds value, and the front-loaded purpose ensures quick understanding.

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 output schema exists, the description does not need to detail return values. It covers usage, parameters, caching, and model options comprehensively. The only minor omission is potential rate limits or disk space, but overall it 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?

The input schema has no descriptions (0% coverage), but the 'Args:' section in the description adds critical semantics: 'url' supported by yt-dlp, 'model_size' options with defaults, and 'language' auto-detection. This fully compensates 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 'Download and transcribe a YouTube video (or any video URL) with timestamps,' specifying the verb, resource, and output. It distinguishes from sibling 'transcribe_local' by focusing on URLs rather than local files.

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 multiple sample prompts that trigger the tool, covering various use cases like transcription, summarization, and timestamp querying. It also explains when to use different model sizes based on speed/accuracy needs, though it does not explicitly mention when not to use or alternative tools.

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

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

With no annotations, the description carries full burden. It mentions removal of stored content (destructive) but lacks details on reversibility, permissions, or side effects.

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, well-structured with a clear purpose, sample prompts, and parameter descriptions. No unnecessary 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 simple 2-parameter tool with an output schema, the description provides complete guidance 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?

Despite 0% schema description coverage, the description lists both parameters with meaningful context: source_type includes examples of allowed values, and identifier is explained as the same used when subscribing.

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 'Remove a subscription and all its stored content', using a specific verb and resource. It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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?

Sample prompts provide clear usage context. No explicit when-not-to-use or alternatives, but the sibling tool names imply the appropriate context.

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 provided, so description carries full burden. Discloses multi-service support, credential fallback, and default behavior for key. Does not mention overwrite policy, error behavior, or file size limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.