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

With no annotations provided, the description carries the full burden. It transparently describes the process: searches academic papers, uses an LLM to synthesize, and provides citations. It implies a read-only query operation, though it does not mention specific limits or auth requirements. No contradictions.

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 highly concise, with a clear opening sentence, a one-line explanation of the process, and well-structured args and returns sections. Every sentence adds value, and the format is 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, the description covers the essential aspects: what it does, how it processes, the output format. It mentions return values (answer with citations and DOIs), which compensates for the lack of visible output schema. Slightly more detail on edge cases or limitations would push it to 5.

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 'question' parameter with an example and clarifies 'num_references' with default (8) and max (15), adding meaning beyond the schema's type and 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 asks a research question and gets an AI-generated answer with citations. It distinguishes from siblings like search_papers (which returns papers) and get_paper_details (which gives details). The verb 'ask' and resource 'research question' are specific and clear.

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 explains when to use the tool (to ask a research question) and provides an example. It does not explicitly state when not to use it or mention alternatives, but the context of siblings implies its unique role in synthesizing answers rather than just searching or retrieving details.

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?

The description lists return fields (abstract, authors, journal, citation count) but does not clarify side effects, authorization needs, or rate limits. Since no annotations are provided, the description carries the full burden but only partially meets it.

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 clear sections for Args and Returns. Every sentence is necessary and adds value, 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?

Given the tool's simplicity (one parameter, output schema present), the description covers purpose, parameter format, and return values adequately. No missing information for an agent to use it 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 input schema only defines doi as a required string with no description. The description adds a clear explanation and an example ('10.1038/nature14539'), significantly improving parameter understanding despite 0% 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 retrieves detailed information about a specific paper by DOI, which distinguishes it from sibling tools 'ask_research_question' and 'search_papers' that serve different purposes.

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 using the tool when you have a DOI and want details, but does not explicitly state when to use it over alternatives or provide exclusion criteria. It lacks guidance on when not 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 must disclose behavior. It mentions the API source (CrossRef), the maximum limit (20), and return fields. However, it lacks details on rate limits, pagination, error handling, or ordering of results.

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, using a clear 'Args' and 'Returns' structure. No unnecessary words; every sentence adds value. It is front-loaded with the purpose.

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 (2 params, no nested objects, no annotations), the description covers the main aspects: purpose, usage, parameters, and return format. Minor omission: no guidance on handling large result sets or network issues.

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?

Despite 0% schema coverage (meaning the description doesn't repeat schema), the description adds value with examples for query and clarifies limit's default (8) and maximum (20), which are not in the schema. This compensates well.

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: 'Search 200M+ academic papers using CrossRef API.' It uses a specific verb (search) and resource (academic papers), and the sibling tools (ask_research_question, get_paper_details) are distinct, so there is no confusion.

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 example queries, which implicitly guide usage, but it does not explicitly state when to use this tool versus its siblings (e.g., for broad search vs. retrieving details). No 'when not to use' or alternative guidance is given.

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