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

With no annotations provided, the description carries full burden. It discloses the search behavior, field options, and return format, but doesn't mention rate limits, authentication requirements, pagination behavior, or error conditions. It adequately describes what the tool does but lacks operational details an agent might need for robust invocation.

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

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

The description is well-structured with clear sections (purpose, args, returns) and uses bullet-like formatting for parameters. Every sentence adds value, though the field options list is quite long. It's appropriately sized for a 5-parameter advanced search tool, though could be slightly more concise in the fields enumeration.

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

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

Given 5 parameters with no schema descriptions and an output schema present, the description provides excellent parameter documentation and clarifies the return structure references 'juba_search'. It covers the core functionality well, though additional behavioral context (like rate limits or authentication) would make it more complete for a search 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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics: it explains query supports specific etiquetas syntax, lists all field options with their meanings, decodes tipo_fallo values (S=Definitiva, etc.), and specifies date format (DD/MM/YYYY). This adds substantial value beyond the bare schema.

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

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

The description clearly states the tool performs 'Advanced search on JUBA with field-specific filtering' and specifies it searches across specific fields like summary text, legal topics, case caption, etc. It distinguishes from sibling 'juba_search' by emphasizing field-specific filtering and advanced capabilities, providing clear 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 implies usage context by mentioning 'advanced search' and listing specific searchable fields, suggesting this is for more targeted queries than basic search. However, it doesn't explicitly state when to use this versus 'juba_search' or 'juba_get_fallo', nor does it provide exclusion criteria 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 effectively describes the tool's behavior: it retrieves full text (implying a read-only operation), specifies the input source ('from search results'), and outlines the return structure ('JSON with metadata and full ruling text'). However, it doesn't mention potential limitations like error handling, rate limits, or authentication needs, which could be relevant for a complete behavioral picture.

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, followed by usage guidance and detailed parameter/return explanations. Every sentence adds value: the first states what it does, the second specifies when to use it, and the subsequent sections clarify inputs and outputs without redundancy. It's appropriately sized for a single-parameter tool.

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 nested objects) and the presence of an output schema (which handles return value documentation), the description is complete. It covers purpose, usage context, parameter semantics, and behavioral aspects adequately, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.

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

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

The schema description coverage is 0%, so the description must fully compensate. It does so by clearly explaining the parameter 'id_fallo' as a 'numeric ruling ID from search results' with an example (e.g., 191298), adding crucial semantic context beyond the schema's basic type and title. This provides all necessary information for correct 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 specific action ('retrieve the full text of a court ruling') and resource ('by its numeric ID'), distinguishing it from sibling tools like juba_search and juba_advanced_search which presumably return search results rather than full ruling texts. It explicitly mentions getting 'complete ruling text, including the full judicial reasoning, not just the summary,' which establishes its unique purpose.

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

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

The description provides explicit guidance on when to use this tool: 'Use id_fallo values from search results to get the complete ruling text.' This directly links it to sibling tools (juba_search, juba_advanced_search) by specifying that the input should come from their outputs, clearly indicating the workflow and alternative tools for different purposes.

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 return format (JSON with specific fields), pagination behavior (up to 15 per page), and default values for materia and limit parameters.

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 front-loaded: purpose statement first, then parameter details, then return format. Every sentence earns its place with essential information, and there's no redundancy or 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 complexity (3 parameters, no annotations, but with output schema), the description is remarkably complete. It covers purpose, parameters with semantics and defaults, behavioral constraints, and return format - providing everything an agent needs despite the sparse structured data.

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 bare input schema (0% coverage). It provides query examples, lists all materia options with their default, explains the limit constraint, and clarifies the return structure - all crucial information not present in the schema.

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

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

The description clearly states the tool's purpose with specific verb ('Search') and resource ('JUBA for court decision summaries (sumarios)'), distinguishing it from siblings by focusing on keyword-based search rather than advanced search or retrieving specific fallo details.

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

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

The description provides clear context for when to use this tool (searching by keyword) and implies alternatives through sibling tool names, but doesn't explicitly state when to choose juba_advanced_search or juba_get_fallo instead.

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