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

No annotations are provided, so the description must disclose behavioral traits. While it states the output (countries and institutions), it does not explain whether results are aggregated, limited by data source, or if there are restrictions on query scope. Key behavioral details like caching, pagination, or API limits are missing.

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, front-loaded with purpose. Efficient but could be slightly more concise by merging the second sentence. No 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?

No output schema exists, so the description should explain the return value structure (e.g., list of countries with counts). It only vaguely mentions 'shows which countries and institutions are most active'. With 5 parameters and no annotations, the description lacks completeness for an analysis tool.

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 each parameter having a clear description. The description adds no new parameter information beyond what the schema provides. A baseline of 3 is appropriate since the schema already 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 tool's function ('analyze the geographical distribution of research activity') and specifies what it shows ('which countries and institutions are most active'). This effectively identifies the tool's purpose and distinguishes it from siblings like 'analyze_topic_trends'.

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 explicit guidelines on when to use this tool vs. alternatives (e.g., 'analyze_topic_trends'). The description implies usage for geographical analysis but lacks context about when it is appropriate or not, and does not mention prerequisites or limitations.

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 behavioral burden. It states that the tool analyzes trends and returns grouped works, which is transparent for the basic operation. However, it does not disclose any additional behavioral traits such as rate limits, resource consumption, or response limits.

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 long, front-loaded with the core purpose, and contains no extraneous words. 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?

The tool has 5 well-documented parameters and no output schema. The description explains the output (works grouped by year) adequately but lacks details on pagination, response structure, or edge cases. Given the absence of annotations, it is minimally sufficient but not comprehensive.

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?

All 5 parameters have 100% schema coverage with detailed descriptions, especially for 'exact_phrase' and 'search_field'. The tool description does not add any new meaning beyond what the schema already provides, meeting the baseline for high 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 'analyze' and the resource 'publication trends over time' with a specific output 'returns works grouped by year'. This distinguishes it from sibling tools like 'get_trending_topics' (current trends) and 'analyze_geographic_distribution' (geographic 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 does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. While the purpose is clear, there are no usage conditions or exclusions, forcing the agent to infer from 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?

No annotations are provided, so the description carries the full burden. It describes the output as 'suggestions' but does not disclose details such as result limits, permissions, or side effects. The description is not misleading but could be more transparent.

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 that are front-loaded with purpose and concise, with no extraneous information. Every sentence adds value.

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 tool with two parameters and no output schema, the description is fairly complete. It explains what it does and the nature of the output. However, lacking details like result count or limitations keeps it from a perfect score.

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 coverage is 100% with descriptions for both parameters. The description adds context about partial queries for query and lists entity types, slightly enhancing understanding but not significantly beyond 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 'Fast autocomplete/typeahead search' and specifies it returns 'quick suggestions for partial queries'. The verb and resource are well-defined, and it implicitly distinguishes from sibling full-search tools by emphasizing speed and partial queries.

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 indicates use for partial queries needing quick suggestions, but lacks explicit guidance on when not to use or alternatives. The context from sibling tools (e.g., search_works) provides implicit differentiation.

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 should disclose behavioral traits. It does not mention read-only nature, error handling for invalid IDs, rate limits, or the structure of returned metadata. This leaves significant 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?

The description is two concise sentences, front-loaded with the function and followed by a practical use case. No unnecessary 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 has one parameter and no output schema, the description covers the basic operation but omits details about the return value (what 'full work metadata' includes) and any behavioral aspects. It is minimally adequate but not fully complete.

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 already describes the 'ids' parameter with a clear format and limit. The description adds minimal value by restating the purpose and max of 20, which is already in the schema. With 100% schema coverage, baseline is 3.

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 resolves DOIs or work IDs to full work metadata in one batch call, and explicitly mentions its use case for checking reference lists. This distinguishes it from single-work tools like get_work.

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 specific use case (checking reference lists) and implies when to use this batch approach versus individual lookups. However, it does not explicitly mention when not to use it or compare to alternatives.

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 states the outputs but does not mention potential errors (e.g., venue not found), rate limits, or authentication needs. The description implies a read-only query, but more detail on what happens with invalid input would improve transparency.

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, front-loaded with the core action and outputs. Every sentence is informative and necessary, 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?

The description explains the return values (h-index, citation impact, indexing status) but lacks detail on format (e.g., whether citation impact is a percentile or number) and does not handle edge cases like missing venue. Given no output schema, this is adequate but not thorough.

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 baseline is 3. The description does not add additional parameter-level meaning beyond what the schema already provides (e.g., it doesn't clarify that at least one parameter should be provided or which combination is preferred).

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: checking quality and prestige metrics of a journal or conference, listing specific outputs (h-index, citation impact, indexing status) and a concrete use case. This distinguishes it from sibling tools that focus on authors, works, or trends.

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 advises when to use the tool ('Use before citing a paper to confirm the venue is reputable'), providing clear context. It lacks explicit exclusions or alternatives, but the guidance is direct and helpful.

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 responsibility for behavioral disclosure. It only states what the tool does without mentioning any side effects, rate limits, or read-only nature. Given the lack of annotations, this is a significant 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 two sentences, the first stating the core action and the second providing a use case. It is efficient with no wasted words, though front-loading key terms could be slightly improved.

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?

There is no output schema, and the description does not explain what the comparison results look like (e.g., metrics returned, format). Given four parameters and the comparative nature, the description misses critical contextual details for an agent to fully understand the tool's output.

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 coverage is 100%, providing baseline parameter descriptions. The tool description adds some extra guidance for 'topics' (recommended count) and 'exact_phrase' (uniform application), but adds minimal value for date parameters. Overall, description does not substantially enhance understanding beyond 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 compares publication volume and citation metrics across multiple research topics. It distinguishes from sibling tools like 'analyze_topic_trends' (single topic trends) and 'search_by_topic' (retrieving works), making its purpose specific and 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 notes the tool is 'useful for understanding relative activity in different fields,' implying when to use it. However, it does not explicitly exclude cases or mention alternatives among siblings, though the context of sibling tools provides some 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?

No annotations are provided, so the description must fully disclose behavior. It states the tool finds OA versions but omits details like whether it checks multiple repositories, what happens if no version exists, or any rate limits or authentication needs. This leaves significant 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?

The description is two sentences, no redundant information, and front-loads the core purpose. Every sentence adds value.

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?

Despite having 7 parameters and no output schema, the description is very brief. It does not explain what the tool returns (e.g., list of links, metadata), nor does it cover edge cases or response format. This leaves the agent without full context for reliable usage.

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 has 100% description coverage for all parameters, so the description does not need to add per-parameter details. It adds overall context (e.g., mentioning preprint sources) but does not elaborate on parameter semantics beyond the schema, resulting in a baseline score.

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 defines the tool's function: finding freely available open access versions of papers, including preprints. It distinguishes itself from sibling tools like search_works by focusing specifically on OA full-text access.

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 the tool is useful for accessing full text without a subscription, providing context for when to use it. However, it does not explicitly mention when not to use it or suggest alternatives, which would improve clarity.

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 exist, so the description carries full burden. It states the tool finds reviews but does not disclose any behavioral traits like rate limits, authentication needs, or result limits. It is not misleading but lacks depth.

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: first defines the tool's core function, second adds context and an optional feature. No unnecessary words, and the key information is front-loaded.

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 8 parameters and no output schema, the description covers the overall purpose and a key configuration option (journal restriction). It lacks details on return format or pagination, but given the straightforward nature of search tools, it is nearly complete.

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 has 100% coverage with detailed parameter descriptions. The tool description adds value by explaining the purpose of reviews and mentioning optional journal restriction, which complements the schema. The description does not repeat schema details but provides context.

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 finds review articles, systematic reviews, and meta-analyses, and explains their value as high-quality citations. This distinguishes it from sibling tools like 'search_works' (general works) and 'find_seminal_papers' (landmark 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 provides context for when to use the tool (to establish context in top papers) and hints at optional journal restriction. However, it does not explicitly mention when not to use it or compare directly with alternatives like 'search_works'.

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 provided; description discloses defaults for min_citations and published_before and clarifies the 'seminal' concept, but does not detail sorting, pagination behavior, or output structure beyond per_page max.

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 plus a usage statement; each sentence adds value without redundancy, front-loaded with key 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?

Adequately complete given 7 params and no output schema: explains defaults, semantics, and use case. Could detail output format but not required.

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 coverage is 100% with good parameter descriptions; the main description does not add meaning beyond what's already in the schema, earning the baseline score.

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 finds seminal/foundational papers with constraints (older than 5 years, high citations) and distinguishes itself by emphasizing 'must-cite' papers, differentiating from generic search tools like search_works.

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?

Provides explicit usage context ('Use to identify citations that reviewers expect to see') but lacks explicit when-not-to-use or alternatives, though the context is clear enough for agents to infer.

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 provided, so the description carries full burden. It does not disclose behavioral traits such as whether it is read-only, if authentication is needed, or how invalid input is handled. Only the basic function is described, missing critical context for safe 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?

Two sentences, concise and front-loaded with purpose. However, it could be slightly more efficient by combining the two sentences without losing clarity.

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 output schema and no annotations, the description is minimal. It does not specify the output structure (e.g., what fields are returned for collaborators and statistics). Adequate for a simple tool but lacks completeness for an agent to fully understand what to expect.

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% for both parameters, so baseline is 3. Description adds no additional meaning beyond 'frequent collaborators' and does not clarify parameter formats (e.g., author_id format, default value for min_collaborations).

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?

Description clearly states the verb 'Analyze', the resource 'author's co-authorship network', and the outcome 'returns frequent collaborators and collaboration statistics'. It distinguishes itself from sibling tools like 'get_author_profile' or 'get_author_works' by focusing specifically on co-authorship network analysis.

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 for analyzing co-authorship but does not explicitly state when to use this tool versus alternatives like 'get_author_works'. No guidance on when not to use it or prerequisites (e.g., valid author_id).

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?

Describes what data is returned but does not disclose additional behavioral traits beyond a read operation. With no annotations, the description is adequate 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?

Two concise sentences: first states functionality, second gives usage context. No unnecessary 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 no output schema, the description lists key return fields (h-index, citations, etc.), making it fairly complete. Could mention limits on counts or pagination.

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 coverage is 100%, so baseline 3. Description mentions top-cited and recent works, which aligns with parameters, but adds no new semantic detail beyond what the schema already 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?

Clearly states it retrieves a comprehensive research profile including h-index, i10-index, citations, top-cited works, recent works, and topics. Differentiates from siblings like get_author_works or get_top_cited_works by being comprehensive.

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?

Explicitly states use cases: identify key researchers, potential reviewers, or study an expert's body of work. Lacks explicit comparison to alternatives or when not to use.

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 fully convey behavioral traits. It states 'Get all publications', but the schema includes pagination (`per_page`), meaning it does not actually return all works without paging. The description fails to mention pagination, response format, or any 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 consists of two concise, front-loaded sentences. The first sentence delivers the primary action, and the second adds context. There is no wasted or redundant information.

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 lacks an explanation of the return value (no output schema provided). It also does not provide enough context for a tool with 5 parameters, such as pagination behavior or sorting options, leaving agents underinformed.

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 has 100% coverage with descriptions for all 5 parameters, so the baseline is 3. The description does not add any additional meaning beyond the schema; it does not elaborate on how the year range or sorting parameters affect results.

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 it retrieves publications by a specific author over time, using a specific verb ('Get') and resource. It distinguishes itself from sibling tools like 'search_works' by focusing on a single author's trajectory.

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 mentions it is useful for analyzing research trajectory and productivity, implying when to use it. However, it does not provide explicit guidance on when not to use it or contrast with alternatives such as 'get_author_profile' or 'get_author_collaborators'.

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 bears full responsibility for behavior. It discloses that only immediate connections are returned and details the asymmetric output (citing as summaries, references as IDs), which adequately informs the agent of key behavioral 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?

Two sentences, no wasted words. The most critical information is front-loaded, and every sentence adds value.

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?

In the absence of an output schema, the description explains the return format (citing summaries, reference IDs) and references a sibling tool for hydration. It could mention pagination or default limits in the description itself, but overall it is sufficiently complete for an experienced agent.

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 coverage is 100% with descriptions for all three parameters. The description does not add new information about parameters beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 it retrieves a citation network including both forward (citing) and backward (referenced) works, distinguishing it from separate forward-only or backward-only sibling tools like get_work_citations and get_work_references.

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 advises using batch_resolve_references for full reference details, providing some usage guidance. However, it does not explicitly contrast with sibling tools for directional-only requests, missing an opportunity to guide when not to use this combined 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?

With no annotations provided, the description must disclose behavioral traits, but it only states the tool returns 'detailed information' without specifying what that includes, whether it's read-only, or any rate limits or permissions.

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 concise sentence that efficiently communicates purpose and supported types with no extraneous information.

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 minimally adequate given the simple parameter set and full schema coverage, but it does not clarify what 'detailed information' entails or how this tool compares to specific entity getters, leaving some gaps.

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 coverage is 100%, providing baseline 3. The description adds the list of supported entity types, which is already in the schema's enum, and emphasizes 'by ID', which is already clear from the schema's id description. Minimal added value.

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 verb 'Get detailed information' and specifies the resource 'any OpenAlex entity by ID', clearly distinguishing it from sibling tools that focus on specific entity types.

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 lists supported entity types, allowing an agent to infer appropriate usage, but it lacks explicit guidance on when to use this generic tool vs. more specific siblings like get_work or get_author_profile.

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. It states the basis for relatedness (topics, citations, references) but does not detail output format, error handling, or default behavior (e.g., per_page default=10). This is adequate but not comprehensive.

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, no wasted words. The first sentence defines the action, the second provides utility. Front-loaded and efficient.

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 explains what and why, but lacks explicit mention of output format (list of works) and default/max per_page behavior. Given tool simplicity and schema coverage, it is nearly complete but has a minor gap.

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 coverage is 100% with clear descriptions for both id and per_page. The tool description adds no additional parameter meaning, so baseline 3 is appropriate as the 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 tool finds works related to a given work based on shared topics, citations, and references. It uses a specific verb ('Find') and resource ('works related to a given work'), distinguishing it from siblings like get_work, get_work_citations, or search_works.

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 advises it is 'useful for discovering similar papers in a research area,' providing clear context for use. However, it does not explicitly exclude cases or compare to alternatives like search_works or get_citation_network, leaving room for slightly better 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?

No annotations are provided, so the description carries the full burden. It transparently states 'Automatically filters for papers with significant citations', which is a key behavior. However, it omits details like rate limits, authentication needs, or what happens when no results are found. Overall clear for a read-only query tool.

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 sentences long, front-loaded with the core purpose, and each sentence adds unique value. No redundant or irrelevant information.

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 covers the tool's purpose and key behaviors well, but lacks information about the output format (no schema provided). Given 12 parameters and no output schema, a brief note on return fields (e.g., citation count, title) would enhance completeness, but it is still largely sufficient.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining automatic filtering, providing examples (e.g., exact phrase usage), and suggesting parameter combinations (e.g., source_issn). This guidance exceeds what the schema alone offers.

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 finds 'most highly cited works' and identifies 'influential and seminal papers'. It uses a specific verb ('Find') and resource, distinguishing it from sibling tools like search_works or find_seminal_papers by its focus on citation count and automatic filtering.

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 on usage, e.g., 'Combine with source_name or source_issn to find the most-cited papers in a specific top journal/conference'. However, it lacks explicit guidance on when not to use this tool versus alternatives (e.g., find_seminal_papers) or exclusions.

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 provided, but the description implies a read-only operation by stating it 'gets' venues and ranks them. It does not mention side effects, permissions, or limitations, but for a straightforward read tool, it offers sufficient insight into its 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?

Three sentences with no fluff. First sentence states purpose, second and third provide usage guidance. Information is front-loaded and every sentence adds value.

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 simple nature of the tool, full schema coverage, and no output schema, the description adequately covers purpose, workflow, and usage context. It is complete for an agent to select 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?

Input schema has 100% coverage with good descriptions. The description adds no additional parameter meaning beyond the schema, achieving the baseline score.

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 it retrieves top journals and conferences for a research field ranked by h-index, using specific verbs and naming the resource. It distinguishes itself from sibling tools like search_works_in_venue by positioning itself as a prerequisite step.

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?

Explicitly describes when to use the tool ('Essential first step before searching for citations') and directs users to the next tool ('use search_works_in_venue to restrict searches to them'), providing clear context and alternative.

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 'recent publication activity' but doesn't explain the underlying algorithm, how 'trending' is determined, or any behavioral nuances like data freshness or potential biases.

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 pack the core purpose efficiently without redundancy. Could be slightly more structured but is concise and front-loaded.

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 output schema, the description should provide more context about return format, pagination, or error conditions. It does not address what the agent can expect from the output, leaving gaps.

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 coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema; it doesn't explain how parameters interact (e.g., min_works and time_period_years) or how they influence the results.

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?

Description clearly states it discovers emerging/trending topics based on recent publication activity, which is specific and distinguishes it from sibling tools like 'analyze_topic_trends' that may focus on deeper analysis. However, it could be more explicit about the 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?

No guidance on when to use this tool versus alternatives. The description doesn't provide context about prerequisites, scope, or limitations that would help an agent decide between this and other topic-related 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?

Without annotations, the description carries the full burden. It fully discloses that the tool returns all information, including complete author lists, abstract, topics, bibliographic data, etc. It does not mention error handling or performance, but the behavioral transparency is high for a read tool.

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 concise and front-loaded with the main action. It uses four sentences efficiently, listing key details without redundancy. Slightly verbose in enumerating fields, but still appropriate.

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 output schema and the complexity of a work object, the description thoroughly explains what will be returned, including all critical fields. This is sufficient for an agent to understand the tool's output.

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% and already explains the parameter format (OpenAlex ID, DOI, or URL). The description repeats this without adding significant new meaning, so value added is limited.

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 complete details of a specific work by ID or DOI, contrasting with summarized search results. It specifies verb (get), resource (work), and distinguishes from sibling tools like search_works.

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 advises using this tool for detailed information about a specific paper, especially for identifying PIs or corresponding authors. It implies when not to use (when summary is enough) but doesn't name alternative tools explicitly.

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, and the description does not disclose behavioral traits such as pagination limits (though max is in schema), rate limits, response format, or any side effects. It only states the purpose without additional 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 concise (one sentence) and clearly states the tool's purpose. It could benefit from a second sentence about pagination or sorting, but it is efficient and front-loaded.

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 no output schema, the description should explain return format or key fields. It does not. The 4 parameters are well-documented in the schema, but missing outcome details make it moderately complete.

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 coverage is 100%, with all parameters described. The description adds no extra meaning beyond what the schema already provides (e.g., 'id' as OpenAlex ID, DOI, or URL is already in schema). Baseline 3 is appropriate.

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 a specific verb ('Get') and resource ('works that cite a given work'). It clearly distinguishes from siblings like 'get_work_references' (which retrieves references) and provides context for forward citation analysis.

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 states it is 'essential for forward citation analysis and understanding research impact,' which implies when to use it. However, it does not explicitly compare to alternative tools like 'get_citation_network' or 'search_works,' nor does it mention when not to use.

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 provided; description only states the action without disclosing behavioral traits such as authentication needs, rate limits, or error handling (e.g., invalid ID). Minimal transparency.

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 the action front-loaded; no unnecessary words 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?

Simple tool with one parameter; description covers purpose and use case but lacks information about return format or pagination. Could be more complete given the absence of 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?

Schema description coverage is 100% for the single parameter, so baseline is 3. The description adds no extra meaning beyond the schema's parameter description.

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 works referenced by a given work, with a specific use case for backward citation analysis. However, it does not differentiate from sibling tools like get_work_citations or get_related_works.

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 for backward citation analysis but does not explicitly state when not to use it or mention alternative tools for forward citations or related works.

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 provided, so description carries burden. It specifies what is returned (cache status, configuration) and implies a non-destructive 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?

Two concise sentences, front-loaded with purpose, no unnecessary 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?

Fully adequate for a parameterless tool with no output schema; provides all necessary 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?

No parameters to document; schema coverage is 100%. Description adds minimal value beyond schema, but baseline for zero params is 4.

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?

Clearly states it checks health status and API connectivity, with specific resources mentioned (cache status, configuration). No ambiguity.

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?

Implicitly suggests use for verifying server health, but no explicit when-not or alternatives. Given no sibling health tools, this is sufficient.

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 adequately discloses that it is a read-only listing tool that returns preset names and keys. It does not mention any destructive behavior, auth needs, or rate limits, but those are not applicable here.

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 that are well-structured and front-loaded. The first sentence states the purpose, and the second provides usage guidance and examples. No 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 simplicity (one optional parameter, no output schema needed), the description is complete. It explains what the tool does, what it returns, and how to use the results. No 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?

The description does not mention the optional 'category' parameter; the schema already fully describes it (100% coverage, with enum and description). The description adds no extra semantic value beyond the schema, so baseline score of 3 is appropriate.

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 it lists all available named journal/conference presets and institution group presets, with specific examples like UTD24, FT50, AJG/ABS tiers, and top AI conferences. It sets proper expectations for what the tool returns.

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?

Explicitly says 'Call this first to discover which preset keys to pass to search_in_journal_list or the institution_group parameter,' providing clear when-to-use guidance and hinting at when not to use (when keys are already known). Could be improved with explicit when-not 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 provided, but description clarifies read-only nature (returns data) and behavior (name-based search). Could be more explicit about sorting and pagination defaults, but schema covers those. No side effects or auth issues mentioned, but likely unnecessary.

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?

Three succinct sentences: purpose, return data, usage context, alternative. No fluff. Front-loaded with essential information.

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?

No output schema, but description mentions key return fields (h-index, citation count, affiliation). Lacks mention of other typical fields like author ID, but adequate for a search tool. Pagination implied via schema per_page.

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 baseline is 3. Description does not add parameter-specific details beyond schema, but it summarizes return data relevant to parameters. No need for higher score.

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?

Description clearly states action (Search) and resource (authors/researchers), specifies return data (h-index, citation count, affiliation), and distinguishes from sibling tool 'search_authors_by_expertise' by noting when to use each.

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?

Explicit guidance: 'Best for finding experts when you know the name. Use search_authors_by_expertise to find experts by research area.' Clearly states usage context and alternative.

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 carry the behavioral burden. It mentions ranking by h-index or citation count and suggests parameter usage, but lacks details on pagination, sorting behavior, output structure, or request limits.

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 long, front-loaded with purpose, and contains no extraneous information. Every sentence adds value.

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 6 parameters all documented in schema and no output schema, the description adequately covers the functionality and usage context. Some organizational details (e.g., data freshness, rate limits) are absent, but not critical for a search tool.

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 input schema already documents each parameter with descriptions. The tool description adds no additional meaning beyond what the schema provides, meeting the baseline of 3.

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 finds leading researchers/experts in a specific topic, ranked by h-index or citation count. It distinguishes itself from search_authors by targeting name-unknown scenarios.

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 says 'More useful than search_authors when you do not know names but need to identify key figures in a field.' This provides clear when-to-use guidance, though alternatives beyond the one sibling are not mentioned.

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 discloses basic functionality and venue filtering but omits details like pagination behavior, default sorting, rate limits, or what the return format looks like. The description is adequate 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 two concise sentences: first states purpose, second adds a key capability. Every sentence earns its place, and the information is front-loaded with no 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?

For a tool with 12 parameters and no output schema, the description is too brief. It covers the main purpose and one extra feature but lacks details on return behavior, pagination, or sorting defaults. Not fully complete for an agent to use 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 coverage is 100%, so the schema already documents all parameters. The description adds marginal context (e.g., exact phrase matching, venue filtering) but does not significantly enhance understanding beyond the schema. Baseline score of 3 is appropriate.

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 'Search for works within specific research topics or domains' using a specific verb and resource. It distinguishes from siblings like 'search_works' and 'search_works_in_venue' by emphasizing topic-based search and venue filtering.

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 advises 'Use this to explore literature in a particular field or subfield' and mentions venue filtering. However, it does not explicitly state when to use alternatives like 'search_works' or when not to use this tool, leaving room for ambiguity.

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 convey behavioral traits. It implies a read-only search but does not mention data source, result limits beyond schema, or any side effects. The focus is on usage context rather than behavioral 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?

Three sentences, each earning its place: purpose, role, and examples. Front-loaded with the core idea, no redundant or filler 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 11 parameters and no output schema, the description effectively orients the user with a clear use case and examples. It could mention the data source or result format for full completeness, but it covers the essential context 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 100%, so baseline is 3. The description adds value by illustrating combinations (e.g., with author_institution) but does not explain parameters beyond what the schema already 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 tool searches papers in preset journal/conference lists, distinguishing it from tools that search individual venues or general topics. Examples like 'UTD24 journals, FT50, AJG 4*' reinforce the specific resource.

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 calls this 'the main tool for credibility-gated searches' and demonstrates combining with other parameters via examples. However, it does not list alternatives or when not to use it, leaving some ambiguity among the many 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?

No annotations are provided, so the description must disclose behavioral traits. It mentions filters but does not discuss side effects, result structure, pagination behavior, or data freshness. The description is too brief for a tool without annotations.

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 fluff, front-loading the core purpose and key filters.

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 six parameters and no output schema, the description covers the main purpose but lacks details on result ordering, return format, or behavioral constraints. It is adequate but not thorough.

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 has 100% description coverage and each parameter is well-documented, including the exact_phrase parameter. The tool description adds minimal additional meaning beyond the schema, so baseline 3 is appropriate.

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 it searches for academic institutions with filters for research output, citations, and geographical location. It distinguishes from sibling tools by its focus on institutions, but does not explicitly differentiate from other search tools like search_authors.

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 for finding leading institutions but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusion cases.

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 provided, so description carries full burden. Describes sorting by h-index descending and return fields (h-index, impact metrics, OA status). Does not mention any side effects, but as a search tool, destructive behavior is unlikely. Could be improved by noting no 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?

Concise and well-structured. Front-loaded with main purpose, then adds key details about sorting and returns, and ends with usage guidelines. Each 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?

For a search tool with full schema coverage and no output schema, the description adequately explains return values and sorting. No gaps in 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?

Schema coverage is 100%, so baseline is 3. Description adds context about sorting and return values but does not significantly enhance parameter understanding beyond what schema descriptions already provide.

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?

Clearly states 'Search for journals, conferences, and other publication sources' with specific verb and resource. Distinguishes from sibling tools like check_venue_quality and get_top_venues_for_field by noting differences in 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?

Explicitly tells when to use alternatives: 'Use check_venue_quality for detailed metrics on a specific venue, or get_top_venues_for_field to discover the best venues in a research area.' Provides clear context for when 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?

No annotations were provided, so the description carries the full burden. It discloses advanced filtering capabilities (Boolean operators, exact phrase, field restriction, etc.) and gives examples. However, it does not mention what the response contains (e.g., list of works with metadata) or any limits like pagination maximum beyond per_page max 200.

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 two-sentence description is concise and front-loaded: the first sentence states the purpose, and the second provides usage guidance with a specific sibling tool reference. Every sentence serves a purpose without extraneous information.

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 complexity (17 parameters, no output schema, no annotations), the description covers the essential aspects: purpose, filtering capabilities, and alternative tool usage. It lacks details about the output structure but is otherwise sufficient for a search tool.

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%, providing a baseline of 3. The description adds value beyond the schema by explaining Boolean operator support, recommending min_citations over cited_by_count, and giving examples for institution filtering. This extra context justifies a score above baseline.

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: 'Search scholarly works with advanced filtering.' It lists many filter types (Boolean operators, year ranges, citation thresholds, venue, institution) and explicitly distinguishes itself from sibling tool 'search_in_journal_list' by noting that the latter is for preset journal lists like UTD24 or FT50.

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 vs. alternatives: 'The most flexible search tool. Use search_in_journal_list for preset journal lists like UTD24 or FT50.' Also includes recommendations like 'Prefer min_citations for simplicity' for parameter 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, the description carries the full burden. It describes the search action and parameter roles but does not disclose behaviors like pagination limits, response format, or rate limits. Since the schema covers parameters well, and the description adds the venue identification workflow, a score of 3 is appropriate for minimally adequate transparency.

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: the first defines the action, the second provides usage guidance. Every sentence adds value, and the structure is front-loaded. No redundant or vague phrasing; ideal conciseness.

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 (11 parameters, no output schema) and numerous siblings, the description covers the core purpose and usage guidance well. It lacks hints about the output format or field selections, which would make it fully complete for an agent relying solely on this description.

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 has 100% description coverage, so the baseline is 3. The description adds value by contextualizing venue parameters ('identify the venue first') but does not enhance understanding of other parameters beyond what the schema provides. Thus, it meets the baseline without exceeding.

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 for papers in a specific journal or conference, and positions it as the primary tool for restricting to credible venues. It uses a specific verb ('search') and resource ('works in a venue'), effectively distinguishing it from sibling tools like search_authors or search_sources.

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: identify the venue first via check_venue_quality or search_sources, then use name, ISSN, or ID. This sets clear context for appropriate use. However, it lacks explicit 'when not to use' statements, which would strengthen guidance against using this tool for unverified or non-venue queries.

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