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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches data from IMDb, implying it's a read-only operation, but doesn't mention potential rate limits, authentication needs, error handling, or what the JSON structure looks like. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured Args and Returns sections. There's no wasted text, and it efficiently communicates the core functionality. However, the use of 'imdbId' in the description doesn't exactly match the schema's 'imdb_id', which is a minor inconsistency, preventing a perfect score.

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

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

Given the tool has an output schema (which should document the return structure), the description doesn't need to explain return values in detail. However, with no annotations and minimal behavioral context, it's somewhat incomplete for a data-fetching tool—it lacks information on error cases, data freshness, or IMDb-specific constraints. The presence of an output schema raises the baseline, but gaps remain.

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 minimal parameter semantics: it explains that 'imdbId' is 'The IMDb ID of the movie to get cast for,' which clarifies the purpose of the single parameter. However, with 0% schema description coverage and only one parameter, this is adequate but not exceptional. It doesn't provide format details (e.g., IMDb ID pattern like 'tt1234567') or examples, so it meets the baseline for low parameter count.

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 the cast of a movie from IMDb.' It specifies the verb ('Get') and resource ('cast of a movie'), and distinguishes it from siblings like get_directors or get_writers by focusing on cast members. However, it doesn't explicitly differentiate from get_imdb_details, which might also return cast information, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like get_imdb_details (which might provide broader movie details including cast) or search_imdb (for finding movies). There are no explicit when/when-not instructions or prerequisites, leaving the agent to infer usage 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?

No annotations are provided, so the description carries full burden. It mentions the return format ('JSON object containing all countries'), which is helpful, but doesn't disclose important behavioral traits like whether this is a read-only operation, potential rate limits, authentication needs, or what 'all countries' means in practice (complete list vs paginated).

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 brief (two sentences) but has structural issues. The first sentence states the purpose, but the second sentence about returns is redundant since an output schema exists. The formatting with 'Returns:' on a separate line adds unnecessary length without 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 has no parameters and an output schema exists, the description is reasonably complete for a simple retrieval tool. However, with no annotations and siblings that might overlap, it should ideally clarify scope or limitations to be fully 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 parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters since none exist, and it correctly doesn't attempt to describe non-existent inputs.

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 'Get' and resource 'all countries', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_country_codes_for_upcoming_releases' which suggests there might be multiple country-related tools, so it doesn't achieve full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_country_codes_for_upcoming_releases' that might provide overlapping or more specific country data, there's no indication of when this comprehensive list is preferred over filtered 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 carries the full burden. It states the return type is a JSON object, which is basic but helpful. However, it lacks details on data freshness, rate limits, authentication needs, or error handling, which are important for a tool fetching external data from IMDb.

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 brief and front-loaded with the core purpose in the first sentence. The second sentence adds return format, which is useful. There's no wasted text, though it could be slightly more structured (e.g., bullet points).

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 0 parameters, 100% schema coverage, and an output schema (implied by 'Has output schema: true'), the description is moderately complete. It covers the purpose and return format but lacks behavioral context (e.g., how often data updates, any limitations), which is a gap for a tool with no annotations.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, but that's appropriate here, earning a baseline score of 4 for this dimension.

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 'Get' and the resource 'available country codes for upcoming releases from IMDb', making the purpose specific and understandable. It distinguishes from siblings like 'get_countries' by specifying the scope is for upcoming releases only, though it doesn't explicitly contrast with 'get_upcoming_releases' or 'get_upcoming_indian_movies'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention siblings like 'get_upcoming_releases' (which might return full release data) or 'get_upcoming_indian_movies' (which is region-specific), leaving the agent to infer usage context from tool names 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get') and returns a JSON object, implying a read-only operation, but does not cover critical aspects like error handling (e.g., invalid IMDb IDs), rate limits, authentication needs, or data freshness. For a tool with no annotations, this is a significant gap in 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 well-structured and concise, with a clear purpose statement followed by bullet points for args and returns. Each sentence earns its place by delivering necessary information without redundancy. However, the use of 'Args:' and 'Returns:' is slightly verbose for such a simple tool, and it could be more front-loaded by integrating parameter details into the main sentence.

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

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

Given the tool's low complexity (1 parameter, no annotations, but has an output schema), the description is minimally adequate. It covers the basic purpose and parameter semantics, and the output schema likely handles return values, reducing the need for detailed output explanation. However, it lacks usage guidelines and behavioral details, making it incomplete for optimal agent operation without additional context.

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

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

The description adds meaningful context for the single parameter: 'imdbId: The IMDb ID of the movie to get directors for.' This clarifies the parameter's purpose and format beyond the schema's basic title ('Imdb Id') and type. With schema description coverage at 0%, the description effectively compensates by providing essential semantic information, though it could specify format details (e.g., 'tt' prefix).

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 the directors of a movie from IMDb.' It specifies the verb ('Get'), resource ('directors'), and source ('IMDb'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_writers' or 'get_imdb_details', which might provide overlapping or related information, so it falls short of a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions IMDb as the source but does not specify prerequisites, limitations (e.g., availability of data), or when to choose siblings like 'get_imdb_details' for broader movie information. This lack of contextual advice leaves the agent without clear usage direction.

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 full burden. It states 'Returns: JSON object containing all genres', which adds some behavioral context about the output format. However, it lacks critical details like whether this is a read-only operation, potential rate limits, authentication needs, or error conditions. For a tool with zero annotation coverage, this 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?

The description is brief with two sentences, but the structure is slightly awkward with 'Returns:' on a separate line. It's front-loaded with the main purpose, but the second sentence could be integrated more smoothly. It avoids unnecessary verbosity.

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

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

Given the tool has an output schema (context signals indicate 'Has output schema: true'), the description doesn't need to detail return values. However, with no annotations and simple functionality, the description is minimally adequate. It could benefit from more behavioral context or usage guidance to be fully 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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters, and it correctly doesn't mention any. This is appropriate for a parameterless tool.

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 with 'Get all genres', which is a specific verb+resource combination. It distinguishes itself from siblings by focusing on genres rather than other data types like movies, directors, or languages. However, it doesn't explicitly differentiate from potential similar tools (none exist in siblings).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention context, prerequisites, or comparisons with sibling tools like get_types or other data retrieval tools. The agent must infer usage based solely on the tool name and description.

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

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

No annotations are provided, so the description carries the full burden. It mentions it returns a JSON object, which is basic, but lacks details on behavioral traits such as rate limits, authentication needs, error handling, or what 'in depth details' specifically includes (e.g., runtime, ratings, plot). This is a significant gap for a tool with no annotation coverage.

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 appropriately sized with three sentences: purpose, args, and returns. It's front-loaded with the main action, and each sentence adds value without waste. However, the formatting with 'Args:' and 'Returns:' could be slightly more integrated for better flow.

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

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

Given the tool has an output schema (which covers return values), no annotations, and low complexity with 1 parameter, the description is minimally adequate. It states the purpose and parameter semantics but lacks behavioral context and usage guidelines, leaving gaps in completeness for effective agent 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?

The description adds meaning by specifying that 'imdbId' is for a movie/series to get details for, which clarifies the parameter's purpose beyond the schema's generic 'Imdb Id' title. With 0% schema description coverage and only 1 parameter, this compensates adequately, though it doesn't detail format constraints (e.g., 'tt' prefix).

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 'Get' and resource 'more in depth details about a movie/series from IMDb', which is specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_imdb' or 'get_most_popular_movies', which might also provide details but through different mechanisms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an IMDb ID), exclusions, or compare to siblings like 'search_imdb' for when you don't have an ID. The description only states what it does, not when to use it.

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

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

No annotations are provided, so the description carries full burden. It mentions the return format ('JSON object'), which adds some behavioral context, but lacks details on permissions, rate limits, data freshness, or error handling. This is a significant gap for a tool with zero annotation coverage.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by return info. It's efficient with two short sentences, though the second could be integrated more smoothly (e.g., 'Returns a JSON object...'). Minimal waste, but not perfectly structured.

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

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

Given 0 parameters, 100% schema coverage, and an output schema (implied by 'Has output schema: true'), the description is adequate but basic. It states purpose and return format, yet lacks context on data scope (e.g., all languages globally or per region) and behavioral traits, leaving some gaps for a tool with no annotations.

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 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, but that's appropriate here, earning a baseline high score since the schema fully handles the empty parameter set.

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

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

The description states the verb 'Get' and resource 'all languages', which clarifies the basic purpose. However, it doesn't differentiate from sibling tools like 'get_countries' or 'get_genres' beyond the resource name, and the title is null, leaving some ambiguity about scope or format compared to similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'get_countries' and 'get_genres', it's unclear if this is for filtering or general reference, and there are no exclusions or prerequisites mentioned, leaving usage context implied at best.

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 discloses pagination behavior and specifies the return format (JSON with 5 movies), which is helpful. However, it doesn't mention rate limits, authentication needs, data freshness, or error handling, leaving gaps for a tool that likely accesses external data.

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

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

The description is well-structured with a clear purpose statement followed by Args and Returns sections. It's appropriately sized with no redundant information, though the 'Args' and 'Returns' labels are slightly verbose for a single parameter.

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 (1 parameter, no annotations, but with an output schema), the description is reasonably complete. It explains the purpose, parameter meaning, and return format. The output schema existence reduces the need to detail return values, but additional behavioral context (e.g., data source reliability) would enhance completeness.

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

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

Schema description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'The starting index (0-based) to retrieve movies from', adding crucial semantic context beyond the schema's bare 'integer' type. However, it doesn't clarify valid ranges or constraints (e.g., maximum 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 action ('Get'), resource ('most popular movies from IMDb'), and includes pagination context. It distinguishes from siblings like 'get_top_250_movies' by specifying 'most popular' rather than 'top rated', though it doesn't explicitly contrast with 'get_most_popular_tv_shows' or other list tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving popular movies with pagination, but provides no explicit guidance on when to use this versus alternatives like 'get_top_250_movies', 'search_imdb', or other sibling tools. The context is clear but lacks comparative 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 provided, the description carries the full burden. It discloses key behavioral traits: the tool fetches from IMDb, uses pagination, and returns exactly 5 items per call. However, it doesn't mention rate limits, authentication needs, data freshness, or what happens with invalid 'start' values. The description adds value but leaves gaps in behavioral understanding.

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 appropriately sized with three sentences: purpose, parameter explanation, and return value. It's front-loaded with the core functionality. The 'Args:' and 'Returns:' sections are slightly redundant with schema/output_schema but help readability. Minor trimming could remove the section headers.

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 1 parameter with 0% schema coverage and an output schema present, the description does well: it explains the parameter's semantics and return format. However, for a tool with no annotations, it could better address behavioral aspects like error handling or data source limitations. The output schema reduces the need to detail return values.

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 clearly explains that 'start' is a '0-based starting index' for pagination, which adds crucial meaning beyond the schema's bare 'integer' type. However, it doesn't specify valid ranges or what happens at the end of the list, leaving some ambiguity.

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 specific action ('Get'), resource ('most popular TV shows from IMDb'), and scope ('with pagination'). It distinguishes itself from siblings like 'get_top_250_tv_shows' by focusing on current popularity rather than all-time rankings, and from 'search_imdb' by being a curated list rather than a search query.

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 the mention of pagination, suggesting this is for browsing popular shows in batches. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_top_250_tv_shows' or 'search_imdb', nor does it mention any prerequisites or exclusions.

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

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: pagination behavior (5 movies per call, 0-based indexing) and return format (JSON object). However, it doesn't mention rate limits, authentication needs, 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?

Perfectly structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first sets context, the second explains the parameter, the third describes the output. 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?

Given 1 parameter with 0% schema coverage and no annotations, the description does well by explaining the parameter and output. However, as a data retrieval tool with no annotations, it could mention rate limits or data freshness. The existence of an output schema reduces the need to detail return values.

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

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

Schema description coverage is 0%, so the description must compensate. It fully explains the single parameter 'start' as 'The starting index (0-based) to retrieve movies from', adding crucial semantics not in the schema (which only shows type: integer). This completely 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 the verb 'Get' and resource 'top 250 movies from IMDb', specifying the exact dataset. It distinguishes from siblings like 'get_most_popular_movies' and 'get_top_250_tv_shows' by focusing on IMDb's top 250 movies ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for paginated retrieval of IMDb's top 250 movies, but does not explicitly state when to use alternatives like 'get_most_popular_movies' or 'search_imdb'. It provides clear context (pagination) but lacks explicit exclusions or comparisons.

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 pagination behavior and that it returns 5 shows per call, which is useful context. However, it doesn't mention rate limits, authentication needs, error handling, or whether the data is cached/real-time, leaving gaps for a read 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?

The description is well-structured with a clear main sentence followed by Args and Returns sections, making it easy to parse. It's appropriately sized with no wasted words, though it could be slightly more concise by integrating the return info into the main sentence.

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

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

Given the tool's low complexity (one parameter), no annotations, and an output schema exists (implied by 'Returns'), the description is mostly complete. It covers purpose, parameter semantics, and return format. However, it lacks details on behavioral aspects like rate limits or data freshness, which would enhance completeness.

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

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

The schema description coverage is 0%, so the description must compensate. It fully explains the single parameter 'start' as 'The starting index (0-based) to retrieve TV shows from,' adding crucial meaning beyond the schema's basic integer type. This is comprehensive for the one 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 specific action ('Get'), resource ('top 250 TV shows from IMDb'), and scope ('with pagination'), distinguishing it from siblings like get_most_popular_tv_shows or get_top_250_movies by specifying it's about IMDb's top-rated shows with pagination support.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving top TV shows from IMDb with pagination, but it doesn't explicitly state when to use this tool versus alternatives like get_most_popular_tv_shows or search_imdb, nor does it mention any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It mentions pagination and that it returns 5 movies per call, which is useful behavioral context. However, it doesn't disclose important traits like rate limits, authentication needs, error handling, or whether the data is real-time/cached. For a data-fetching tool with zero annotation coverage, this leaves significant gaps.

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 perfectly structured and concise: a clear purpose statement followed by a well-organized Args/Returns section. Every sentence earns its place by providing essential information without redundancy. The front-loaded purpose statement immediately conveys the tool's function.

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

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

Given the tool has an output schema (so return values are documented elsewhere) and the description fully explains the single parameter, it's reasonably complete. However, with no annotations and a data-fetching operation, the description could better address behavioral aspects like data freshness or limitations. The presence of an output schema reduces the burden, but some contextual gaps remain.

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 fully compensates by explaining the single parameter: 'start: The starting index (0-based) to retrieve movies from.' It adds crucial semantics beyond the schema's type information, clarifying it's a 0-based index for pagination. This is excellent compensation for the schema gap.

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

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

The description clearly states the specific action ('Get'), resource ('top box office data for the US from IMDb'), and scope ('with pagination'), distinguishing it from sibling tools like get_most_popular_movies or get_top_250_movies. It explicitly mentions the US focus and pagination feature, which differentiates it from other movie listing tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through 'with pagination' and the parameter explanation, suggesting it's for retrieving paginated US box office data. However, it doesn't explicitly state when to use this tool versus alternatives like get_most_popular_movies or get_top_250_movies, nor does it mention any exclusions or prerequisites.

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 and does well by disclosing key behavioral traits: it specifies the dataset size (top 250), pagination behavior (5 movies per page, 0-based indexing), and return format (JSON object). It doesn't mention rate limits, authentication needs, or data freshness, but provides substantial operational context beyond basic functionality.

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 appropriately sized with three focused sentences: purpose statement, parameter explanation, and return specification. It's front-loaded with the core functionality. The 'Args:' and 'Returns:' formatting is helpful, though slightly less polished than pure prose. Every sentence earns its place by adding distinct 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 moderate complexity (pagination, curated list), no annotations, and the presence of an output schema (which handles return value documentation), the description is complete enough. It covers purpose, usage context, parameter semantics, and behavioral details like pagination mechanics. The output schema existence means the description doesn't need to detail return 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?

With 0% schema description coverage (the schema only shows 'start' is an integer), the description fully compensates by explaining that 'start' is 'the starting index (0-based) to retrieve movies from' and clarifies that it returns '5 top rated Indian movies starting from the specified index.' This adds crucial semantic meaning about how the parameter affects the output.

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 specific action ('retrieve movies'), resource ('top 250 rated Indian movies on IMDb'), and scope ('with pagination'). It explicitly distinguishes this tool from siblings like 'get_top_250_movies' (general top movies) and 'get_top_rated_tamil_movies' (specific language subset) by specifying the Indian focus and pagination mechanism.

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 about when to use this tool (to retrieve paginated top-rated Indian movies) and implicitly distinguishes it from alternatives like 'get_most_popular_movies' or 'search_imdb' by focusing on a specific curated list. However, it doesn't explicitly state when NOT to use it or provide direct comparison with all 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns '5 top rated Malayalam movies starting from the specified index,' which clarifies pagination behavior (5 items per call) and the 0-based indexing. However, it doesn't address important aspects like rate limits, authentication requirements, error conditions, or whether the data is static or real-time updated.

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 efficiently structured with a clear purpose statement followed by parameter and return value documentation. Every sentence serves a purpose, though the discrepancy between 'Top 50' in the first sentence and '5 top rated' in the returns section creates some confusion that slightly reduces clarity.

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 (single parameter, list retrieval), no annotations, but presence of an output schema, the description provides adequate context. It explains the purpose, parameter usage, and return format. The output schema existence means the description doesn't need to detail return structure, though it could better explain the 'Top 50' versus '5' discrepancy for full completeness.

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

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

The description explicitly documents the 'start' parameter as 'The starting index (0-based) to retrieve movies from,' which adds crucial semantic context beyond the schema's bare type declaration (integer). With 0% schema description coverage and only one parameter, this compensation is effective, though it doesn't explain constraints like valid range or maximum index.

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

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

The description clearly states the tool retrieves 'Top 50 Malayalam movies as rated by IMDb users,' specifying both the resource (Malayalam movies) and the selection criteria (IMDb user ratings). It distinguishes from siblings like get_top_rated_indian_movies by focusing specifically on Malayalam films, though it doesn't explicitly contrast with get_top_rated_tamil_movies or get_top_rated_telugu_movies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving top-rated Malayalam movies, but provides no explicit guidance on when to use this tool versus alternatives like get_top_rated_indian_movies or get_most_popular_movies. The context is clear (Malayalam movies, IMDb ratings), but lacks specific when/when-not instructions or named 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?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the tool returns a JSON object with 5 movies, but doesn't disclose critical details like whether it's read-only, how it handles invalid indices, rate limits, or data freshness. The description is too sparse for a tool with behavioral implications.

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 brief and structured with separate sections for the overview, args, and returns, but it's somewhat inefficient. The first sentence states 'Top 50 rated Tamil movies' while the returns mention only 5 movies, creating potential confusion. The information could be more front-loaded and cohesive.

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

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

Given the tool has an output schema (which handles return values) and only one parameter with low schema coverage, the description is moderately complete. It covers the basic purpose and parameter meaning but lacks usage guidelines and behavioral details, making it adequate but with clear gaps for effective agent 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?

The schema has 0% description coverage, but the description compensates by explaining the 'start' parameter as 'The starting index (0-based) to retrieve movies from', adding meaningful context beyond the bare schema. However, it doesn't clarify valid ranges or what happens if the index exceeds available movies, leaving some ambiguity.

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

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

The description clearly states the tool retrieves 'Top 50 rated Tamil movies on IMDb' with a specific verb ('retrieve') and resource ('Tamil movies'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_top_rated_indian_movies' or 'get_trending_tamil_movies' beyond the 'top rated' aspect, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_top_rated_indian_movies' or 'get_trending_tamil_movies'. It mentions the tool returns 5 movies starting from an index, but doesn't explain why one would choose this over other movie-listing tools, leaving usage context unclear.

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 for behavioral disclosure. It mentions the tool returns '5 top rated Telugu movies starting from the specified index' but doesn't explain critical behaviors like pagination (how to get beyond 5 movies), rate limits, authentication requirements, error conditions, or what happens when 'start' exceeds available movies. This leaves significant gaps for an agent to use it effectively.

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 efficiently structured with a clear purpose statement followed by parameter and return explanations. However, the formatting with 'Args:' and 'Returns:' sections is slightly verbose for such a simple tool, and the title repetition in 'get_top_rated_telugu_moviesArguments' in the schema is extraneous but not in the description itself.

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

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

Given the tool has an output schema (though not shown here), the description doesn't need to detail return values. However, with no annotations and a simple but potentially confusing behavior (returns 5 movies from a 'Top 50' list), the description should better explain the relationship between the 50 movies and the 5 returned, and address pagination or limits. It's minimally adequate but leaves operational questions unanswered.

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 meaningful context about the 'start' parameter being '0-based' and that it retrieves '5 top rated Telugu movies starting from the specified index', which clarifies how the parameter affects the output. With 0% schema description coverage (schema only has type and title), this compensation is valuable, though it could specify valid ranges or constraints.

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

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

The description clearly states the tool retrieves 'Top 50 rated Telugu movies on IMDb' with a specific verb ('retrieve') and resource ('Telugu movies'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_top_rated_indian_movies' or 'get_trending_telugu_movies', which would require more specific scope clarification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'get_top_rated_indian_movies' or 'get_trending_telugu_movies'. The description only explains what the tool does, not when it's appropriate compared to other movie-related tools in the server.

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 full burden. It mentions the return format (JSON object with 5 movies) but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or error handling. The description adds some context about the fixed return count but lacks comprehensive behavioral 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 front-loaded with the core purpose, followed by structured parameter and return details. Every sentence adds value: the first states what the tool does, the second explains the parameter, and the third describes the return format. 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?

Given the tool's moderate complexity (1 parameter, no annotations, but with output schema), the description is reasonably complete. It covers purpose, parameter meaning, and return format. The output schema exists, so detailed return value explanation isn't needed. However, it could benefit from more behavioral context given the lack of annotations.

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

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

Schema description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'starting index (0-based) to retrieve movies from', adding meaningful semantics beyond the schema's basic integer type. However, it doesn't clarify valid ranges or constraints for this 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 verb ('Get') and resource ('trending Tamil movies on IMDb'), making the purpose specific. It distinguishes from siblings like 'get_top_rated_tamil_movies' by focusing on trending rather than top-rated movies, and from 'get_trending_telugu_movies' by specifying Tamil language.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving trending Tamil movies, but doesn't explicitly state when to use this tool versus alternatives like 'get_top_rated_tamil_movies' or 'get_trending_telugu_movies'. No guidance on prerequisites or exclusions 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?

No annotations are provided, so the description carries full burden. It discloses the return format (JSON with 5 movies) and pagination behavior (starting index), but lacks critical behavioral details like rate limits, authentication needs, data freshness, or error handling. For a tool with no annotation coverage, this 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?

The description is appropriately sized with three sentences: purpose statement, parameter explanation, and return format. It's front-loaded with the core purpose. Minor improvement could be merging the Args/Returns sections more seamlessly, but overall it's efficient with zero waste.

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

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

Given the tool's low complexity (1 parameter) and presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and return structure. However, it lacks behavioral context like rate limits or data sources, which would be beneficial despite the output schema.

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

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

The schema description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'The starting index (0-based) to retrieve movies from', adding essential meaning beyond the schema's basic integer type. However, it doesn't clarify constraints like valid range or typical usage patterns.

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

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

The description clearly states the action ('Get') and resource ('trending Telugu movies on IMDb'), making the purpose specific. It distinguishes from sibling tools like 'get_top_rated_telugu_movies' by focusing on trending rather than top-rated content, and from 'get_trending_tamil_movies' by specifying Telugu language.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving trending Telugu movies, but provides no explicit guidance on when to use this tool versus alternatives like 'get_top_rated_telugu_movies' or 'get_most_popular_movies'. It mentions the return format but doesn't clarify context or exclusions.

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

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

No annotations are provided, so the description carries full burden. It states the tool returns 'JSON object containing all types' which provides basic output information, but doesn't disclose important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens if no types exist. The description is minimal and lacks 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 extremely concise with just two brief sentences. The first sentence states the action, and the second describes the return format. There's no wasted verbiage, though some might argue it's too minimal given the lack of context about what 'types' means in this domain.

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

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

Given the tool has an output schema (which handles return value documentation) and zero parameters with 100% schema coverage, the description provides the minimum viable information. However, for a tool with no annotations and many similar siblings, the description should do more to clarify what 'types' refers to in this specific context to help the agent distinguish it from other get_* tools.

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

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

The tool has 0 parameters, and schema description coverage is 100%. With no parameters to document, the description doesn't need to add parameter semantics. The baseline for zero parameters with high schema coverage is 4, as there's nothing missing to compensate for.

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

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

The description states the purpose as 'Get all types' which is a clear verb+resource combination. However, it doesn't distinguish this tool from its many siblings (like get_genres, get_languages, get_directors) that also retrieve specific data categories. The description doesn't clarify what 'types' refers to in this context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. With 22 sibling tools on the server, the description offers no context about what 'types' are or when this retrieval would be appropriate compared to other get_* tools. The description simply states what it does without usage 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies the return format ('JSON object containing 5 most anticipated Indian movies') and hints at pagination behavior through the 'start' parameter, but doesn't mention rate limits, authentication needs, data freshness, or what happens if the start index exceeds available movies. It adds some context but leaves gaps for a tool with no annotation coverage.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by a structured 'Args:' and 'Returns:' section. Every sentence adds value—no redundant or verbose content—making it efficient 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?

Given the tool's low complexity (1 parameter), no annotations, but with an output schema (which handles return values), the description is mostly complete. It covers purpose, parameter semantics, and return structure. However, it lacks details on behavioral aspects like error handling or data limitations, which would be beneficial despite the output schema.

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

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

The schema description coverage is 0%, so the description must compensate. It explains the 'start' parameter as 'The starting index (0-based) to retrieve movies from,' which clarifies its purpose and format beyond the schema's basic integer type. However, it doesn't detail constraints (e.g., valid ranges) or provide examples, leaving some ambiguity for a single parameter.

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

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

The description clearly states the specific action ('Get'), resource ('most anticipated Indian movies on IMDb'), and scope ('based on real-time popularity'). It distinguishes from siblings like 'get_top_rated_indian_movies' by focusing on upcoming/anticipated movies rather than top-rated ones, and from 'get_upcoming_releases' by specifying Indian movies and IMDb as the source.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through 'most anticipated Indian movies on IMDb based on real-time popularity,' suggesting it's for discovering upcoming popular Indian films. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_top_rated_indian_movies' or 'get_upcoming_releases,' nor does it provide exclusions or prerequisites for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'pagination' and specifies that it returns '5 upcoming releases starting from the specified index,' which adds useful context about output format and limitations. However, it doesn't cover error handling, rate limits, authentication needs, or whether the data is static or real-time, leaving significant gaps for a tool with no annotation coverage.

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 and appropriately sized. It front-loads the core purpose in the first sentence, followed by a clear breakdown of arguments and returns. Each sentence adds value, with no redundant information, though it could be slightly more concise by integrating the returns into the initial statement.

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 (3 required parameters) and the presence of an output schema, the description is fairly complete. It covers parameter semantics adequately and specifies the return format (JSON with 5 items), reducing the need to explain outputs. However, without annotations, it lacks details on behavioral aspects like error cases or performance, leaving room for improvement.

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 substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'country_code' for country filtering, 'type' with possible values 'TV' or 'MOVIE', and 'start' as a 0-based index. This compensates well for the schema's lack of descriptions, though it doesn't detail format constraints (e.g., country code standards).

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 the upcoming releases from IMDb with pagination.' It specifies the resource (upcoming releases from IMDb) and the action (get), but doesn't explicitly differentiate from sibling tools like 'get_upcoming_indian_movies' or 'get_most_popular_movies' beyond the 'upcoming' qualifier.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools like 'get_upcoming_indian_movies' (which might be a subset) or 'search_imdb' (which might offer broader filtering), nor does it specify prerequisites or exclusions for 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 of behavioral disclosure. It states the tool retrieves data ('Get') and returns a JSON object, but lacks details on error handling, rate limits, authentication needs, or whether it's a read-only operation. For a tool with no annotation coverage, this is a significant gap in 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place without redundancy, making it easy to scan and understand quickly.

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

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

Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is largely complete. It covers the purpose, parameter meaning, and return format. However, it lacks behavioral context like error cases or usage guidelines, which slightly reduces completeness for a tool with no annotations.

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 meaningful context beyond the input schema. The schema has 0% description coverage and only lists 'imdb_id' as a string parameter. The description explains that 'imdbId' is 'The IMDb ID of the movie to get writers for,' clarifying its purpose and format. This compensates well for the low schema coverage, though it doesn't detail exact ID formats or examples.

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 the writers of a movie from IMDb.' It specifies the verb ('Get') and resource ('writers of a movie'), and distinguishes it from siblings like get_directors or get_cast by focusing on writers. However, it doesn't explicitly differentiate from get_imdb_details, which might also provide writer information, keeping it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like get_imdb_details (which might return comprehensive data including writers) or search_imdb (for finding movies), nor does it specify prerequisites or exclusions. Usage is implied only by the tool's name and description.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that 'First 5 results are returned' and returns a 'JSON object', which adds some context on output format and result limits. However, it fails to disclose critical behaviors like whether this is a read-only operation (implied but not stated), potential rate limits, authentication needs, or error handling, leaving significant gaps 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?

The description is structured with clear sections (Args, Returns) but is overly verbose, listing all parameters in detail within the description itself rather than relying on the schema. While informative, this duplicates information that could be in the schema descriptions, making it less concise. The front-loaded purpose statement is good, but the bulkiness reduces efficiency.

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

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

Given the complexity (18 parameters, no annotations, schema coverage 0%, but has output schema), the description is mostly complete. It explains all parameters thoroughly and notes the output is a JSON object with the first 5 results, which aligns with the output schema. However, it lacks context on behavioral aspects like rate limits or error cases, slightly reducing completeness 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?

The schema description coverage is 0%, so the description must fully compensate. It provides detailed explanations for all 18 parameters, including search logic (e.g., 'Searches the whole word' vs. 'Searches the partial word'), format requirements (e.g., ISO codes), value sources (e.g., 'Get all possible types with get_types()'), and sorting options. This adds substantial meaning beyond the basic schema, effectively documenting parameter 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 searches for movies on IMDb and returns the first 5 results, providing a specific verb ('Search') and resource ('movies on IMDb'). However, it doesn't explicitly distinguish itself from sibling tools like 'get_most_popular_movies' or 'get_top_250_movies', which also retrieve movie information but through different mechanisms.

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 its parameter explanations (e.g., 'Get all possible types with get_types()'), suggesting when to use certain parameters. However, it lacks explicit guidance on when to choose this tool over sibling alternatives like 'get_imdb_details' for detailed info or other get_* tools for specific movie lists, leaving usage context partially inferred.

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