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 that the tool clears the entire cache and returns a dictionary with cleared_count. However, it does not mention any potential side effects, permissions required, or behavior if the cache is already empty. Given the simple destructive nature, this is acceptable but not rich.

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

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

The description is short and front-loaded, with the core action stated in the first sentence. Subsequent sentences add usage context and return information without redundancy. Every sentence earns its place.

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

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

This is a simple tool with no parameters, and the description covers the action, the rationale for use, and the return value. It is complete for its intended scope and no additional information is necessary.

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

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

The tool has zero parameters, so the schema fully covers expectations with an empty object. Per the baseline rule, a score of 4 is appropriate since there is no parameter information to add.

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

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

The description opens with a specific verb and resource: "Clear all entries from the BioPortal search cache." This clearly distinguishes it from the sibling tool remove_stale_cache_entries, which targets only stale entries, by specifying the full scope of action.

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

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

The description provides explicit use context: "force fresh API calls for all subsequent BioPortal searches" and "useful when ontology data has been updated." It does not explicitly mention the alternative remove_stale_cache_entries or state when not to use it, but the context is clear enough for most 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 must carry the behavioral burden. It discloses the operation is a read/fetch, notes that it returns 'child terms with their prefLabels,' and clarifies 'immediate children.' Yet it omits any details about error handling, rate limits, authentication, or what happens when no children exist, leaving some uncertainty for an agent.

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 an opening summary, usage guidance, parameter list, and return note. Every sentence adds value, and the information is front-loaded so the agent immediately grasps the tool's core function. No fluff or repetition.

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

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

The tool is a simple read operation with two parameters, and an output schema exists. The description provides enough context for an agent to understand the purpose, parameters, and expected return. It doesn't explicitly differentiate from sibling tools like 'term_search_from_branch' or 'get_ontology_class_tree,' but the 'immediate children' scope and examples are sufficiently clear. Minor gap: no mention of pagination or limits, but that's not critical for this tool type.

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 for parameter meaning. It provides dedicated Args entries with type explanation and realistic examples (e.g., a CHEBI IRI, 'CHEBI' as acronym) for both required parameters. This goes beyond the bare schema and gives an agent enough to construct correct calls, though it doesn't cover edge-case formats.

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

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

The description starts with a specific verb-resource pair: 'Fetch all immediate children terms for a given branch in an ontology.' It clearly distinguishes this from siblings like 'term_search_from_branch' or 'get_ontology_class_tree' by emphasizing 'immediate' children and providing a concrete use case (exploring hierarchy, dropdown options). The scope is unambiguous.

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

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

The description explicitly states when to use the tool: 'Use this tool to retrieve the child terms under a specific branch IRI in a BioPortal ontology.' It also gives practical contexts (exploring hierarchy, populating dropdowns). However, it does not mention when not to use it or point to alternatives, which would be helpful given the array of 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, the description carries full burden and does an excellent job. It discloses that the output is in compact YAML, that ontology branches are reported as branches unless expanded, that each expansion costs a BioPortal lookup, and that the return data is 'cleaned and transformed.' It even warns that a template 'can easily have twenty' branches, making costs explicit.

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 substantial but perfectly structured: a one-sentence purpose, a rationale for the compact format, a warning about branch costs, and clearly separated parameter explanations. Every sentence adds value, and the section headers (Args, Returns) improve scannability.

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 (2 params, 1 enum) and the existence of an output schema, the description covers all necessary aspects: purpose, format, cost implications, parameter options, and return transformation. No critical 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?

Schema description coverage is 0%, so the description is the sole source of parameter meaning. It explains template_id with an example URL and expand_branches with detailed semantics for each enum value ('none' reports the branch, 'labels' lists labels without IRIs, 'terms' includes IRIs at ~1.7x size).

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 begins with 'Get a template from the CEDAR repository,' clearly identifying the verb, resource, and scope. It also distinguishes this from sibling tools like get_instances_based_on_template by focusing on template retrieval rather than instances, and notes the compact YAML format as a specific feature.

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 contrasts this with the JSON-LD form, calling it 'far cheaper' and noting it 'leaves out provenance and other bookkeeping keys,' which implies when to choose this over alternatives. It provides direct guidance on when to use expand_branches ('Use expand_branches to list those values, bearing in mind that each branch costs one BioPortal lookup') and explains the trade-offs of each setting.

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 transparency burden. It discloses pagination behavior to avoid token limits, the return structure including errors, and that it fetches complete content. This adds useful behavioral context beyond the basic 'get' 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 summary, an explanatory paragraph, and well-labeled Args/Returns sections. It is slightly redundant at the start, but all information is useful and earns its place.

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

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

For a moderate-complexity tool with no annotations and a zero-coverage schema, the description is highly complete. It covers parameters, return structure, pagination rationale, and error handling, fully equipping an agent to invoke and interpret the tool correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It thoroughly explains each parameter: template_id with a full URL example, limit with min/max/default, and offset with default. This adds significant meaning beyond the bare schema types.

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 gets template instances belonging to an input template ID. This distinguishes it from sibling tools like get_cedar_template, which likely retrieves the template definition itself, and search-oriented 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 provides clear context: use this to fetch template instances for a given template. It does not explicitly exclude alternatives, but the purpose is distinct enough that no exclusions are necessary given the sibling tool names.

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 clearly states the tool fetches a tree structure and describes the return. However, it does not disclose error behavior, authentication requirements, or explicitly confirm read-only safety, though the verb 'Fetch' implies non-mutating.

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 summary, usage guidance, and Args/Returns sections. It is not overly verbose, though the first two sentences are slightly redundant.

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

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

The description is complete for a simple read-only tool: it explains what it does, when to use it, both parameters with examples, and the return value. Given the output schema exists, it does not need to detail response structures further.

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 properties have no descriptions (0% coverage), so the description must compensate. It provides explicit explanations with examples for both class_iri and ontology_acronym, fully making up 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 uses the specific verb 'Fetch' with resource 'hierarchical tree structure' and clearly defines scope as a specific class IRI in a BioPortal ontology. It distinguishes from sibling tools like get_branch_children by specifying that it returns ancestor path and sibling nodes.

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 'Use this tool to retrieve the ancestor path and sibling nodes' and provides the use case 'understanding where a term sits in the ontology hierarchy.' It does not mention when not to use it or alternatives, but the context is clear.

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

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

With no annotations, the description carries the full transparency burden. It clearly states the destructive action (removing expired entries) and reassures that valid cached results are preserved. It also discloses the return dictionary. However, it doesn't mention permissions, failure modes, or whether the operation is reversible.

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 three concise sentences: the first states the purpose, the second adds usage context and TTL explanation, and the third describes the return value. Every sentence earns its place with no redundancy.

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

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

For a simple parameterless tool, the description fully covers purpose, usage scenario, and return value. It also provides enough distinction from the sibling clear_bioportal_cache. The presence of an output schema means the return dictionary does not need more detail.

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

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

The tool has zero parameters, and the input schema confirms this. With no parameters to describe, the baseline is 4. The description adds no parameter documentation, and none 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 tool's function: 'Remove expired entries from the BioPortal search cache.' It specifies the verb (remove), the resource (cache entries), and the scope (only expired), distinguishing it from the sibling tool clear_bioportal_cache, which would clear all entries.

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 a clear usage context: 'Use it to free disk space without losing valid cached results.' This indirectly differentiates it from a full cache clear, but it does not explicitly mention alternatives like clear_bioportal_cache or state when not to use this tool.

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

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

There are no annotations, so the description carries full burden. It only states that it returns search results containing matching terms, without disclosing additional behavioral traits such as pagination, rate limits, read-only nature explicitly, or what happens if no matches are found. It implies read-only through 'search' but does not explicitly confirm safety or side effects.

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

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

The description uses a clear docstring structure with purpose, Args, and Returns. It is concise and front-loaded with the primary purpose. Minor redundancy exists (e.g., re-emphasizing 'specific branch' multiple times) but overall the structure is effective and not bloated.

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

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

The tool is a search operation with an output schema present, so return-value details are covered elsewhere. The description covers the purpose and parameters adequately. However, it lacks context on how branches are defined, potential errors, or any constraints on search behavior, leaving some gaps 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?

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for all three parameters in the Args section, including type clarification and concrete examples (e.g., 'aspirin', 'CHEBI', 'http://purl.obolibrary.org/obo/CHEBI_23367'). This adds meaning beyond the bare schema types.

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 BioPortal for ontology terms within a specific branch, using a specific verb ('Search') and resource ('BioPortal... terms within a branch'). It distinguishes itself from the sibling tool 'term_search_from_ontology' by emphasizing the branch restriction.

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

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

It explicitly says to use this tool to find the correct standardized name and IRI for a term label within a specific branch, giving clear context on when to use it. However, it does not mention explicit alternatives or exclusions (e.g., 'use term_search_from_ontology for whole-ontology search'), though the branch specificity effectively implies when it is appropriate.

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 this is a search operation returning matching terms, but lacks details on result format, pagination, rate limits, or any potential side effects. This is minimally adequate for a search tool but not rich.

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

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

The description is efficiently structured: a one-line summary, a 'Use this tool' context, and well-labeled Args/Returns sections. Every sentence earns its place, with no redundant phrases.

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 and the presence of an output schema, the description covers purpose, scope, and parameters adequately. However, it could be more complete by mentioning behavior for no results or invalid ontology acronyms, though these are minor given the output schema.

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

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

The schema provides only raw types with 0% description coverage, but the description includes an Args section that explains each parameter with examples ('melanoma', 'NCIT'). This fully compensates and adds meaning beyond the structured schema.

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

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

The description opens with 'Search BioPortal for standardized ontology terms within an entire ontology', which specifies the verb, resource, and scope. It also explicitly notes 'not restricted to a specific branch', effectively distinguishing it from the sibling tool 'term_search_from_branch'.

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

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

It states 'Use this tool to find the correct standardized name and IRI for a given term label across an entire ontology', giving clear context. The phrase '(not restricted to a specific branch)' implies when not to use it, but it does not explicitly name the alternative or provide strong exclusion criteria.

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