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 describes the output content but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or whether it's a read-only operation (implied by 'Get' but not explicit). It adds value by detailing the metadata fields but lacks 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 a single, efficient sentence that front-loads the core purpose ('Get full metadata for a specific PubMed article by PMID') and lists the included data without unnecessary words. Every part earns its place by clarifying scope and content.

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, no output schema, no annotations), the description is complete enough for basic use: it specifies the action, input requirement, and output details. However, it could improve by mentioning the absence of behavioral annotations or output schema, but it adequately covers the core functionality.

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 100%, with the parameter 'pmid' clearly documented as 'PubMed ID'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating further.

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 'full metadata for a specific PubMed article by PMID', specifying the exact data returned (title, abstract, authors, journal, publication date, DOI, MeSH terms). It distinguishes from siblings like 'search_articles' (search vs. specific retrieval) and 'get_citations' (citations vs. metadata).

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 'by PMID' and listing the metadata returned, suggesting it's for retrieving complete article details when the PMID is known. However, it doesn't explicitly state when not to use it (e.g., vs. 'search_articles' for unknown IDs or 'get_citations' for citation data), though the specificity helps differentiate.

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 the purpose but lacks details on permissions, rate limits, response format, or error handling. This is inadequate 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 two concise sentences, front-loaded with the main purpose and followed by usage context, with zero wasted words or 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 no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, return values, and error handling, which are essential for a tool with no structured data to compensate.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter (pmid). The description adds minimal context by implying the parameter is for 'a given PubMed article', but does not provide additional syntax or format details beyond what the schema provides.

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 articles that cite') and resource ('a given PubMed article (PMID)'), and distinguishes this tool from siblings by specifying forward citation tracking, which is not covered by other tools like get_article or search_articles.

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 ('Useful for forward citation tracking'), but does not explicitly state when not to use it or name alternatives among siblings, such as get_related for other types of article relationships.

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 the return types ('PMC links and publisher free-access URLs') and implies a read-only operation, but lacks details on rate limits, authentication needs, error conditions, or response format. It adds some behavioral context but leaves gaps for a tool with potential external API calls.

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?

Two concise sentences with zero waste: the first states purpose and examples, the second specifies return values. It is front-loaded with the core functionality and 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 no annotations and no output schema, the description provides adequate purpose and return type information but lacks details on behavioral traits (e.g., network dependencies, error handling) and output structure. For a tool that likely interacts with external services, this leaves room for improvement in 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 100%, so the schema already documents the 'pmid' parameter fully. The description adds no additional parameter semantics beyond what the schema provides (e.g., format examples, validation rules). Baseline 3 is appropriate when schema does the heavy lifting.

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 verb ('Get links to') and resource ('free full text versions of an article'), with concrete examples ('PubMed Central'). It distinguishes from siblings by focusing on link retrieval rather than article metadata (get_article), citations (get_citations), or searches (search_articles/search_mesh).

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 full-text links are needed for a PubMed article, but provides no explicit guidance on when to choose this tool over alternatives like get_article (which might include links) or when not to use it (e.g., for non-PubMed IDs). The context is clear but lacks sibling differentiation or exclusion criteria.

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 the ranking algorithm ('NCBI's algorithm') which adds useful context beyond basic functionality, but does not cover other behavioral aspects such as rate limits, error conditions, or response format. The description is adequate but lacks depth for a tool with no annotation support.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, resource, and key behavioral trait (ranking algorithm). It is front-loaded with essential information and contains no redundant or unnecessary details, making it highly concise and effective.

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 parameters, no output schema, no annotations), the description is minimally complete. It covers the core functionality and ranking behavior but lacks details on output format, error handling, or integration with sibling tools. Without an output schema, the agent must infer return values, which is a gap in 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 100%, so the schema already fully documents both parameters ('pmid' and 'max_results'). The description does not add any additional semantic information about the parameters beyond what is in the schema, such as format details for 'pmid' or implications of 'max_results'. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Get related articles') and resources ('PubMed article (PMID)'), and distinguishes it from siblings by specifying the ranking algorithm ('NCBI's algorithm'). It explicitly identifies the target resource type and the ranking methodology, making it distinct from tools like 'get_article' or 'search_articles'.

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 'PubMed article (PMID)' and 'ranked by relevance', suggesting it should be used when seeking related content for a specific article. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_citations' or 'search_articles', and does not specify prerequisites or exclusions, leaving some ambiguity for the agent.

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 search capabilities (query syntax, boolean operators, date ranges) but doesn't mention important behavioral aspects like rate limits, authentication requirements, pagination behavior, or what the response format looks like. It provides good functional context but lacks operational 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 a single, well-structured sentence that efficiently communicates the core functionality. Every element (PubMed, biomedical articles, query syntax details) earns its place by providing essential information without redundancy. It's front-loaded with the main purpose and follows with supporting details.

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 search tool with 5 parameters, 100% schema coverage, and no output schema, the description provides adequate functional context but lacks completeness. It doesn't describe what the search results look like (structure, fields returned), doesn't mention limitations or constraints beyond query syntax, and doesn't address error conditions or edge cases that would help an agent use it effectively.

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 100%, so the schema already documents all 5 parameters thoroughly. The description mentions 'full PubMed query syntax including field tags ([Title], [Author], [MeSH]), boolean operators (AND, OR, NOT), and date ranges' which adds some context about the query parameter's capabilities, but doesn't provide additional meaning beyond what the schema already specifies for individual 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 specific action ('Search PubMed for biomedical articles') and resource ('PubMed'), distinguishing it from sibling tools like get_article (retrieve specific article) or search_mesh (search MeSH terms). It provides a precise verb+resource combination that immediately communicates the tool's function.

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 the mention of 'PubMed query syntax,' suggesting this is for complex searches. However, it doesn't explicitly state when to use this tool versus alternatives like get_article (for known IDs) or search_mesh (for terminology searches). The guidance is implied rather than explicit.

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 what the tool does but lacks behavioral details such as whether it's read-only or mutative, any rate limits, authentication needs, or response format. For a search tool with no annotations, this is a significant gap in disclosing 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 appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the core purpose, and the second adds useful context without redundancy, making it concise 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 complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects, output format, and usage guidelines, making it inadequate for an AI agent to fully understand how to invoke and interpret results.

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 100%, so the schema already documents both parameters ('query' and 'max_results') with descriptions and constraints. The description does not add any parameter-specific information beyond what the schema provides, such as examples or additional context, meeting the baseline for high schema coverage.

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

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

The description clearly states the verb ('Search') and resource ('Medical Subject Headings (MeSH) vocabulary'), making the purpose evident. It specifies the type of vocabulary and mentions what can be found (standardized biomedical terms, tree numbers, scope notes), though it doesn't explicitly differentiate from sibling tools like 'search_articles' beyond the resource focus.

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

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

The description includes 'Useful for finding standardized biomedical terms...' which implies a general context but provides no explicit guidance on when to use this tool versus alternatives like 'search_articles' or other siblings. There are no when-to-use or when-not-to-use statements, leaving usage unclear relative to other tools.

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