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 tool returns 'Complete filter documentation,' which is helpful but lacks details like format (e.g., Markdown, JSON), error handling for invalid filter names, or performance characteristics. It adequately describes the core behavior but misses nuanced 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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured 'Args' and 'Returns' sections. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy to scan and understand.

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, and an output schema exists (so return values needn't be explained), the description is mostly complete. It covers purpose, usage, parameter semantics, and return intent. However, it could improve by mentioning sibling tools more explicitly or adding error-handling hints, leaving minor gaps.

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

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

The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'filter_name' as the 'Name of the filter' with examples ('e.g., 'upcase', 'date', 'money''), clarifying what the parameter represents beyond the schema's basic string type. This compensates well for the low coverage, though it doesn't detail constraints like valid filter 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 the tool's purpose with specific verb ('Get documentation') and resource ('specific Shopify Liquid filter'), distinguishing it from siblings like 'list_liquid_filters' (which lists filters) and 'search_liquid_docs' (which searches documentation). It precisely identifies what the tool does without ambiguity.

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

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

The description explicitly states when to use this tool ('for a specific Shopify Liquid filter') and implies alternatives through sibling tool names (e.g., use 'list_liquid_filters' to see available filters, 'get_liquid_object' for objects). It provides clear context for selecting this tool over others in the suite.

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 returns 'Complete object documentation,' which hints at a read-only operation, but doesn't explicitly confirm it's safe (non-destructive) or address other traits like error handling, authentication needs, or rate limits. For a 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 well-structured and front-loaded, with the core purpose stated first, followed by clear sections for 'Args' and 'Returns.' It avoids unnecessary fluff, though the 'Returns' section could be more concise (e.g., 'Documentation for the object' instead of 'Complete object documentation').

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 (single parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks usage guidelines and behavioral details. With no annotations, it doesn't fully compensate for the missing context, keeping it at a baseline level.

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

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

The schema description coverage is 0%, so the description must compensate. It adds meaningful context by explaining the parameter 'object_name' with examples ('e.g., 'product', 'cart', 'shop''), which clarifies its purpose beyond the bare schema. However, it doesn't detail constraints like valid object names or formatting rules, preventing a perfect score.

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 documentation for a specific Shopify Liquid object.' It specifies the verb ('Get') and resource ('documentation for a specific Shopify Liquid object'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_liquid_objects' or 'search_liquid_docs', which would be needed for 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 'list_liquid_objects' (for browsing) or 'search_liquid_docs' (for broader searches), nor does it specify prerequisites or exclusions. The agent must infer usage from context 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 provided, the description carries the full burden. It discloses the tool's read-only nature by stating it 'Get[s] documentation,' but lacks details on error handling, rate limits, or authentication needs. It adds basic behavioral context but misses deeper operational 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?

The description is front-loaded with the core purpose, followed by structured 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it efficient and easy to parse for an 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?

Given the tool's low complexity (1 parameter), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, parameter semantics, and return intent, aligning well with the structured data available.

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 adds meaning by explaining that 'tag_name' is the 'Name of the tag' with examples ('if', 'for', 'assign'), clarifying its purpose beyond the bare schema. However, it does not detail format constraints or edge cases.

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 documentation') and resource ('for a specific Shopify Liquid tag'), distinguishing it from siblings like list_liquid_tags (which lists tags) and search_liquid_docs (which searches documentation). It precisely defines the tool's function without redundancy.

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

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

The description implies usage when documentation for a specific tag is needed, but does not explicitly state when to use alternatives like list_liquid_tags (for browsing) or search_liquid_docs (for broader searches). It provides clear context but lacks explicit exclusions 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a list organized by category, which adds useful behavioral context beyond basic listing. However, it lacks details on permissions, rate limits, or error handling, leaving 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 and adds return details in the second. Every sentence earns its place by providing essential information without waste, making it appropriately sized and well-structured.

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

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

Given the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is mostly complete. It specifies what is listed and how results are organized. However, with no annotations, it could benefit from more behavioral context like performance or limitations, though the output schema reduces the need to explain 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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description does not need to add parameter semantics, and it correctly avoids redundancy. A baseline of 4 is appropriate as it efficiently handles the no-parameter case without unnecessary details.

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

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

The description clearly states the specific action ('List all available Shopify Liquid filters') and distinguishes it from siblings by focusing on filters rather than objects, tags, or documentation search. It explicitly mentions the resource (Shopify Liquid filters) and scope (all available).

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 by specifying 'Shopify Liquid filters' and organizing results by category, which helps differentiate it from siblings like list_liquid_objects or list_liquid_tags. However, it does not explicitly state when to use this tool versus alternatives like search_liquid_docs or get_liquid_filter, missing 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 of behavioral disclosure. It states the tool lists objects and returns them organized by category, but lacks details on permissions, rate limits, pagination, or error handling. For a read-only tool with no annotations, this is a significant gap in transparency about operational behavior.

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 and front-loaded: the first sentence states the core purpose, and the second clarifies the return format. Every sentence earns its place with no wasted words, making it easy for an agent to parse 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 (0 parameters, read-only operation) and the presence of an output schema, the description is minimally adequate. It covers the purpose and return format but lacks behavioral context (e.g., permissions, limitations) and usage guidelines relative to siblings. It meets basic needs but leaves gaps that could hinder optimal tool selection.

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 adds value by explaining the return format ('List of all object names with titles organized by category'), which compensates for the absence of an output schema in the context signals (though 'Has output schema: true' is noted, the description still provides useful semantics). Baseline is high due to no parameters.

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

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

The description clearly states the tool's purpose: 'List all available Shopify Liquid objects' with a specific verb ('List') and resource ('Shopify Liquid objects'). It distinguishes from siblings like 'get_liquid_object' (singular) and 'list_liquid_filters/tags' (different resource types), though it doesn't explicitly contrast them. The purpose is unambiguous but could be more precise about 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?

No guidance is provided on when to use this tool versus alternatives. The description mentions the return format but doesn't specify contexts (e.g., for reference vs. search), prerequisites, or exclusions. With siblings like 'search_liquid_docs' and 'get_liquid_object', the lack of usage guidelines leaves the agent to infer appropriate scenarios.

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 full burden. It discloses the return format ('List of all tag names with titles'), which is useful behavioral context. However, it lacks details on error handling, pagination, or performance characteristics that might be relevant for a list 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 two sentences with zero waste, front-loading the purpose and then specifying the return format. It could be slightly more structured by combining into one sentence, but it efficiently conveys essential information without redundancy.

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

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

Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is reasonably complete. It states the purpose and return format, though it could benefit from clarifying the relationship with sibling tools. The output schema likely covers return details, reducing the need for extensive description.

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 schema fully documents the absence of inputs. The description adds no parameter-specific information, which is appropriate here. Baseline is 4 for zero parameters, as no compensation is needed.

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

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

The description clearly states the specific action ('List all available') and resource ('Shopify Liquid tags'), distinguishing it from siblings like 'get_liquid_tag' (singular) and 'list_liquid_filters/objects' (different resources). It precisely defines the scope as 'all available' tags.

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 all tag names, but does not explicitly state when to use this versus alternatives like 'get_liquid_tag' (for a specific tag) or 'search_liquid_docs' (for broader searches). 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 the full burden of behavioral disclosure. It mentions 'maximum 3' for queries, which adds a constraint, but fails to cover other critical aspects like rate limits, authentication needs, error handling, or pagination. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 with the core purpose in the first sentence. The 'Args' and 'Returns' sections add structure without redundancy, making it efficient. However, the formatting could be slightly improved for better readability.

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 nested objects) and the presence of an output schema (which handles return values), the description is somewhat complete. It covers the purpose and parameter semantics 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 description adds meaningful semantics beyond the input schema: it specifies that 'queries' are 'List of search terms' and includes a 'maximum 3' constraint, which is not in the schema (schema description coverage is 0%). This compensates well for the low schema coverage, though it could elaborate on term formatting 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 as 'Search Shopify Liquid documentation using full-text search,' which specifies the verb (search), resource (Shopify Liquid documentation), and method (full-text search). However, it doesn't explicitly differentiate from sibling tools like get_liquid_filter or list_liquid_filters, which might retrieve specific documentation items rather than performing 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 provides no guidance on when to use this tool versus alternatives. It lacks any mention of sibling tools (e.g., get_liquid_filter for direct retrieval or list_liquid_filters for listing) or context for when full-text search is preferred over other methods, leaving the agent without usage direction.

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