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 only says 'Get one author' and does not mention what happens on missing IDs, what fields are returned, rate limits, or any side effects. The behavior is minimally implied but not detailed.

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, terse sentence with no wasted words. It is appropriately concise for the scope, though it sacrifices detail for brevity. The structure is clean and front-loaded with the key action.

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 tool with two parameters, no annotations, and an output schema (not shown), the description is too sparse. It fails to explain the fields parameter, usage context, or any behavioral nuances, leaving an agent with incomplete information to call 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?

Schema description coverage is 0%, so the description must compensate. It clarifies that author_id is used to identify the author, but completely omits any explanation of the 'fields' parameter—its purpose, allowed values, or format. With two parameters, this is a significant 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 clearly states the verb 'Get', the resource 'Semantic Scholar author', and the identifier 'author ID'. It distinguishes this from siblings like get_authors (plural) and search_authors (search-based), so an agent can easily tell it apart.

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 gives no explicit guidance on when to use this tool versus alternatives like search_authors or get_authors. There is no mention of conditions such as 'when you have a known author ID' or exclusions, leaving usage to inference.

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 behavioral disclosure burden. It usefully reveals pagination behavior ('one page'), but it leaves the meaning of 'associated' ambiguous (authored, co-authored, or cited?) and says nothing about ordering, rate limits, authentication, or failure 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?

A single 10-word sentence that front-loads the verb and resource. It is efficiently sized for a straightforward tool, though its brevity comes at the cost of the semantic gaps noted in other dimensions.

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?

With 5 parameters at 0% schema coverage, overlapping siblings, and no annotations, the description is too thin to fully support correct invocation. An output schema exists, which spares it from documenting return values, but parameter semantics and tool-selection context remain unaddressed.

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% with 5 parameters, yet the description explains none of them. While names like author_id, limit, and offset are somewhat self-explanatory, the meaning and format of fields and publication_date_or_year are completely unexplained, and the description adds no 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 states a specific verb and resource ('Get one page of papers associated with a Semantic Scholar author') that clearly distinguishes it from author-lookup siblings like get_author and search_authors. However, it does not explicitly differentiate from paper-lookup siblings like get_papers or search_papers, which could also be selected for fetching papers.

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 given on when to use this tool versus alternatives such as get_papers, search_papers, or search_papers_relevance. With a sibling list full of overlapping paper-fetching tools, the absence of any selection criteria or exclusions leaves the agent to guess.

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 behavioral disclosure burden. It does disclose two useful traits: the operation is batched and capped at 1000 IDs. However, it says nothing about handling of missing or invalid IDs, result ordering, rate limits, or authentication, leaving significant behavior unspecified.

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?

A single front-loaded sentence with no filler; the batch limit is placed prominently and every word contributes. It is concise without being empty.

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?

Insufficient for a tool with no annotations and no per-parameter documentation. The output schema may cover return shape, but the agent still lacks when-to-use guidance, understanding of the fields parameter, and failure behavior expectations.

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 does not: 'author IDs' only restates the author_ids property, and the optional fields parameter is completely unexplained even though it likely controls which author attributes are returned.

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 supplies a specific verb ('Batch-get'), a resource (Semantic Scholar authors), and a batch-size limit (1000), so the core operation is clear. It does not explicitly differentiate from the sibling get_author or search_authors, and 'author IDs' is slightly ambiguous as to whether IDs are the input or the returned objects.

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?

There is no guidance about when to use this tool versus get_author (single author) or search_authors (find authors by query). 'Batch' hints at multiple IDs, but no explicit conditions, preconditions, or alternatives are stated.

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 mentions pagination ('one page') but does not disclose behavior such as default limit, offset semantics, field filtering, or whether results are sorted. For a read tool, this is a moderate gap.

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 concise sentence that is front-loaded with the core action. It earns its place but could add a bit more context without becoming 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?

With 5 parameters, 0% schema coverage, no annotations, and an output schema present, the description is too thin. It does not explain pagination parameters, field selection, or the output structure, making it incomplete for correct invocation.

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, but it only explains paper_id implicitly. The limit, offset, fields, and publication_date_or_year parameters are not described at all, leaving the agent to guess their semantics.

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 states a specific verb ('Get') and resource ('papers that cite the requested paper'), which clearly identifies the tool's function. It does not explicitly distinguish it from siblings like get_references, but the resource is specific enough that an agent can infer the difference.

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 such as get_references or search_papers. The description implies a use case (fetching citing papers) but does not state exclusions or conditions.

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 for behavioral disclosure. It only says 'get' and gives no information about read-only behavior, external identifier resolution, authentication/rate limits, or failure modes.

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 with no filler, and the key scoping information—one paper, by identifier—is front-loaded. Every word contributes to the agent's understanding.

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?

Since an output schema exists, the return shape does not need to be explained, and the description is adequate for a simple lookup. Still, the fields parameter is left unexplained and there is no guidance on which sibling tools to use for search or batch retrieval, leaving gaps in the full calling context.

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 useful meaning to paper_id by explaining that it accepts either a Semantic Scholar ID or a supported external identifier, which the schema alone does not convey. However, the optional fields parameter is not mentioned at all, and with 0% schema description coverage the description should compensate more fully.

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 a direct fetch of a single paper, keyed by Semantic Scholar ID or supported external identifier. The singular 'one paper' and identifier-based lookup distinguish it from search_papers, but it never explicitly names sibling tools such as get_papers.

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 the tool should be used when an identifier is already known, but it gives no explicit guidance about when not to use it or when to prefer search_papers/get_papers. This is usable guidance but left mostly to inference.

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 behavioral disclosure burden. It adds a useful constraint ('up to 500') and makes the retrieval semantics inferable from 'batch-get', but it does not mention handling of invalid IDs, over-limit requests, error behavior, or the effect of the optional fields parameter.

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 front-loaded sentence that conveys the verb, object, and key constraint with no filler. Every word earns its place and the structure makes the main behavior immediately clear.

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 output schema covers returned values, and paper_ids is clearly described enough for a straightforward batch fetch. However, the optional fields parameter is entirely unexplained and there is no explicit guidance distinguishing this tool from get_paper, so an agent may under-specify the request or choose the wrong sibling.

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 clarifies that paper_ids are caller-supplied paper identifiers and that the batch is capped at 500, but it says nothing about the fields parameter—its meaning, allowed values, or effect—leaving one of two parameters effectively undocumented.

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 names a specific verb ('batch-get'), resource ('papers'), and defining constraints ('up to 500 caller-supplied paper identifiers'). This clearly distinguishes it from the search_* siblings, which do not take caller-supplied paper_ids, and from get_paper, which is the singular variant.

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 phrase 'caller-supplied paper identifiers' signals that this tool is for known IDs rather than search queries, and 'batch' positions it for fetching multiple papers at once. It does not explicitly name get_paper for single lookups or state when not to use this tool, so it stops short of full when/when-not guidance.

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 'one page' which hints at pagination, but does not explain default page size, how offset/limit interact, whether results are sorted, or what happens if the paper has no references. It also does not disclose any rate limits, authentication needs, or side effects (though this is likely a read 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 a single sentence with no wasted words. It front-loads the core action and resource. However, it is so brief that it misses important parameter and usage details, so it is concise but under-specified.

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 has 4 parameters, 0% schema coverage, no annotations, and an output schema exists, the description is incomplete. It does not explain pagination parameters, field selection, or how the output is structured. The output schema may help, but the description should at least clarify the meaning of limit, offset, and fields to allow correct invocation.

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 the four parameters. It only clarifies that paper_id identifies the paper whose references are returned. It does not explain limit, offset, or fields. The term 'one page' implies limit/offset but does not define their semantics or defaults. The fields parameter is completely unexplained, leaving the agent to guess what field names are valid.

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 states a specific verb ('Get') and resource ('references cited by the requested paper'), and the phrase 'one page' signals pagination. It is clear enough to distinguish from siblings like get_citations, which likely returns citing papers rather than cited references. However, it does not explicitly differentiate from get_paper or get_papers, which could also return reference lists.

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 gives no guidance on when to use this tool versus alternatives like get_citations or search_papers. It does not mention pagination behavior, how to request more pages, or any context about typical use cases. The only implied usage is that it returns references for a paper, but no exclusions or alternatives are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only reveals that this is 'one offset-paginated request' and says nothing about matching semantics, ordering, rate limits, or read-only safety. This is minimal added context beyond the name.

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 front-loaded sentence with no filler. Every word contributes to the core action, making it structurally clean and appropriately sized for such a simple 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?

For a tool with no annotations, four parameters, and a sibling set that includes get_authors and get_author, this description is insufficiently complete. An agent still needs to understand pagination behavior, fields semantics, and how this tool differs from the retrieval siblings. The output schema covers return values, but the calling context remains vague.

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 needed to explain query, limit, offset, and fields. It only hints that the query is a name and gives no details about field projections, pagination bounds, or how the parameters interact. The description fails to compensate 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 clearly states the verb 'search', the resource 'authors', and the filter 'by name', and it mentions offset pagination. It is distinguishable from siblings such as get_authors, though it does not explicitly contrast them.

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 about when to use search_authors versus get_authors or get_author. There are no conditions, exclusions, or alternative recommendations, leaving the agent to infer the appropriate context.

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 behavioral disclosure. It does disclose the key pagination behavior: returns up to 1000 papers per call, no limit argument, and requires the token for the next page. However, it does not mention other behaviors like filtering semantics, output format, or any potential side effects. The disclosure is useful but incomplete.

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 no redundancy. It front-loads the core action and states the pagination constraint efficiently. 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?

With 11 parameters and zero schema descriptions, the description is severely under-specified. An agent cannot correctly construct a query without understanding parameters like fields_of_study, publication_types, min_citation_count, and publication_date_or_year. The output schema exists, but the input semantics are nearly absent, making this inadequate for a tool of this complexity.

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 token parameter via pagination, but the other 10 parameters (query, sort, year, venue, fields, etc.) are left entirely to schema titles, which provide no semantic meaning. The description adds value only for the token, failing to clarify the rest.

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 action: running a bulk paper search request with pagination. It mentions the specific endpoint and the pagination behavior. However, it does not differentiate from the sibling search_papers_relevance, which likely orders by relevance, leaving ambiguity about when to choose one over the other.

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 explicit guidance on when to use this tool versus alternatives like search_papers_relevance or get_papers. It only implies bulk retrieval via the word 'bulk', but does not state conditions for selection or exclusion.

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 behavioral disclosure. It does disclose important limits (100 per call, 1000 total), which is useful. However, it omits other behavioral traits such as authentication requirements, side effects, or that it's a read operation, leaving 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?

Two sentences with no fluff, purpose front-loaded, and the limit context stated efficiently. Excellent structure for the information provided.

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 has 11 parameters with no schema descriptions and no output schema details in the description. The only contextual info is the result limits. Given the complexity, this is incomplete for correct usage; an agent would struggle with valid values for fields like publication_types or fields_of_study.

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 no information about any of the 11 parameters, leaving agents to guess from names alone. This is a significant 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 clearly states the tool runs a relevance-ranked /paper/search request, specifying the verb and resource. It distinguishes from siblings through the 'relevance-ranked' qualifier, though it does not explicitly name the alternative search tool.

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 given on when to use this tool versus alternatives like search_papers. The description only mentions limits, not the selection criteria based on relevance needs.

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