xpay Academic Research Collection

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

No annotations are provided, so the description must fully disclose behavioral traits. It only says 'Get information' which implies a read operation, but doesn't state whether it makes external calls, returns static content, or requires authentication. It also doesn't describe the output format or side effects, leaving 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 a single, clear sentence that front-loads the purpose. There is zero wasted text, and it is appropriately sized for a trivial informational tool. It could be more detailed but remains concise.

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 the tool's simplicity, the description lacks essential context about the return value. With no output schema and no annotations, the description should clarify what 'information' the user receives (e.g., a list of tools, an overview, or limitations). It also doesn't mention prerequisites or limitations, leaving the agent to guess about the actual 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?

The tool has zero parameters and the input schema is empty. Per the rubric, the baseline for 0 params is 4. The description doesn't need to explain parameter meanings since there are none, and it adds no unnecessary param info.

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 provides information about NanciMCP's capabilities for literature review, using a specific verb and resource. It distinguishes from sibling tools that directly search, download, or analyze literature by focusing on the MCP itself. However, 'capabilities' is somewhat vague and doesn't specify what kind of info (e.g., tool list, help text).

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. It doesn't mention that this is an intro/help tool or contrast it with the many search/read tools. Usage is only implied by the name and purpose.

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 only states that the tool analyzes a citation network but does not disclose details about how the analysis works, whether it makes multiple API calls, what limits mean operationally, or what output to expect. Minimal behavioral disclosure beyond the core action.

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, clear sentence with no wasted words. It is appropriately concise but lacks additional valuable context that could be included without verbosity, such as indicating what the analysis output looks like.

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 four parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'analyze' produces, how depth and limits affect results, or how it relates to sibling citation/reference tools. The description leaves significant gaps in understanding.

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 all parameters fully described (depth, paperId, citationsLimit, referencesLimit). The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 applies.

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 'citation network for a specific paper', which distinguishes it from sibling tools that list citations or references. However, it doesn't specify what 'analyze' entails or differentiate it explicitly from papers-citations and papers-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?

There is no guidance on when to use this tool compared to alternatives like papers-citations or papers-references. The description lacks context, prerequisites, or exclusions, leaving usage entirely implied.

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 of behavioral disclosure. It does not specify the output format (e.g., counts per phase), whether the tool is read-only, or any side effects. The only behavioral detail added is the max_studies default, which is already present in the schema, so the description contributes little beyond structured data.

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 first two sentences are redundant: 'Analyze the distribution...' and 'This tool analyzes the distribution...' say essentially the same thing. The rest is a structured input list that is concise but the repetition makes it less efficient than it could be.

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 inputs but omits any description of the return value or how the analysis is presented. Since there is no output schema, the description should at least mention that it returns phase distribution counts. It also does not clarify whether criteria are combined or how max_studies affects the analysis beyond being a cap. Given the tool's simplicity, these gaps leave it incomplete.

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 repeats parameter names and brief explanations (e.g., 'A list of medical conditions to filter the analysis'), but these add no meaning beyond the schema's own descriptions. It does mention the default for max_studies, though the schema already includes 'default: 1000'.

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 specific verb and resource: 'Analyze the distribution of trial phases'. It explicitly mentions what it does (distribution of clinical trial phases) and distinguishes it from sibling search/download tools by focusing on analysis rather than raw search or retrieval.

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 by indicating it filters analysis by conditions, interventions, sponsors, and max_studies. However, it provides no explicit guidance on when to use this tool versus alternatives like search_trials_by_condition or search_trials_by_intervention, and lacks any when-not or alternative recommendations.

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 says 'Get papers written by a specific author' and does not mention pagination, ordering, result format, or any other behavioral traits. This is insufficient for an agent to know what to expect.

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 immediately states the core purpose. It contains no unnecessary words and is well-structured for quick 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?

Given there are three parameters and no output schema, the description is too sparse. It lacks critical context such as which specific database or system the author ID belongs to, how results are ordered, or any details about the response. This makes it incomplete for an agent deciding whether to invoke this 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?

The input schema fully describes all three parameters (authorId, limit, offset) with individual descriptions, so the description adds no extra meaning beyond the schema. Baseline 3 is appropriate since schema coverage is 100%.

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 a resource 'papers' with a filtering condition 'written by a specific author', making the primary function clear. However, it does not explicitly distinguish itself from sibling tools like papers-search-basic that may also support author filtering, so it stops short of a 5.

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 such as authors-search or papers-search-basic. It lacks any mention of use cases, prerequisites, 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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It only states that it searches authors and does not mention read-only nature, pagination behavior, output format, or any side effects. The schema mentions limit/offset, but that is in structured data, not the description.

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 words. Every word contributes to the tool's purpose, making it highly 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 tool is simple and the description provides the core purpose, but with no output schema and no annotations, some behavioral context (e.g., return format, differentiation from 'authors-papers') is missing. The description is adequate for a basic search tool but not rich enough to be 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 schema already covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds meaningful context for the 'query' parameter by specifying it can be a name or affiliation, which goes beyond the schema's generic 'Search query for authors'.

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 with a specific verb ('Search') and resource ('authors'), and specifies the search scope ('by name or affiliation'). This clearly distinguishes it from sibling tools that search papers, trials, or other entities.

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 searching authors but provides no explicit guidance on when to use this tool versus alternatives like 'authors-papers' or other search tools. No exclusions or alternative recommendations are 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 the full burden. It discloses the core behavior (downloads a PDF, saves to a directory, returns the path) and the default save location. However, it omits error handling, network prerequisites, overwrite behavior, and file naming, which are relevant for a download 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 well-structured: a single sentence for the main purpose, followed by clearly labeled Args and Returns sections. Every line adds value and there is 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?

For a straightforward download tool, the description covers the essential context: parameters, default behavior, and return value. Minor gaps exist, such as not specifying the exact filename or whether the save directory is auto-created, but these are not critical 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?

The input schema provides no descriptions for either parameter, but the tool description fully compensates by explaining paper_id with an example ('2106.12345') and save_path with a default value ('./downloads'). This gives clear, actionable meaning 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 'Download PDF of an arXiv paper,' which is a specific verb+resource. This distinguishes it from sibling download tools for other repositories (e.g., download_biorxiv, download_medrxiv) by naming the exact source.

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 arXiv papers based on the resource name and tool name, but it does not explicitly mention alternative tools or provide when-to-use/when-not-to-use guidance. No exclusions or comparisons to sibling functions are present.

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 disclose behavioral traits itself. It states the core behavior (downloads a PDF, saves it, returns the path) but omits potential side effects such as whether existing files are overwritten, whether directories are created, network/authentication requirements, or error behaviors. This 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 concise, structured with Args and Returns, and contains no fluff. Every line provides necessary information, making it easy to scan and understand.

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 inputs, the action, and the return value, which is adequate for a simple tool. Minor gaps include not mentioning whether the .pdf extension is added automatically, whether directories are created, or how errors are handled, but these are not critical for basic 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 description fully explains both parameters: paper_id as a bioRxiv DOI and save_path as the destination directory with a default. Since the schema provides no descriptions (coverage 0%), the description compensates completely, adding meaning beyond the minimal schema fields.

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

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

The description starts with 'Download PDF of a bioRxiv paper,' clearly stating the action and resource. This differentiates it from sibling download tools for other repositories (e.g., download_arxiv, download_medrxiv) and read tools (e.g., read_biorxiv_paper).

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 clearly implies usage when a bioRxiv PDF needs to be saved, but it does not explicitly discuss alternatives or when not to use the tool. It does not contrast with read_biorxiv_paper or other download tools, beyond what the name itself implies.

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 discloses that the tool always returns a message indicating lack of direct download support, and explains why. This fully informs the agent of the tool's limitations without any hidden 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?

The description is well-structured with Args, Returns, and Note sections. It is appropriately sized—each sentence provides necessary information, and it is front-loaded with the primary action in the first line.

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 2-parameter tool with no output schema, the description covers everything: purpose, parameter semantics, behavioral outcome, and alternative usage. It is complete for an agent to correctly invoke and interpret the 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 coverage is 0%, but the description adds rich meaning: paper_id is described as a CrossRef DOI with an example format, and save_path is explained as the directory to save the PDF with a default value. This goes well beyond the bare schema properties.

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 action ('Attempt to download PDF of a CrossRef paper') and then specifies the actual outcome ('Message indicating that direct PDF download is not supported'). This distinguishes it from sibling download tools by clarifying that CrossRef does not support direct PDF downloads.

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 note provides explicit guidance: 'CrossRef is a citation database and doesn't provide direct PDF downloads. Use the DOI to access the paper through the publisher's website.' This tells the user when to use this tool and, more importantly, what alternative to pursue, which is exactly what usage guidelines should do.

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 that the extracted content is 'memory only', suggesting non-persistence, but it does not mention other important behaviors such as whether the PDF is stored, authentication requirements, 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, front-loaded sentence that directly states the main action and target. It contains no redundant words, and every part 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 no output schema, so the description should clarify what the extracted text looks like as a return value. It only says 'extract text content (memory only)', leaving the output format and any side effects ambiguous. This is a significant gap for an agent to know how to handle the result.

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 fully describes the arxivId parameter with 100% coverage. The description adds no additional parameter-level context, so the baseline score of 3 applies.

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 (Download full-text PDF and extract text content) and the target resource (arXiv paper). This distinguishes it from sibling tools that target other repositories or focus solely on download without extraction.

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 this tool versus alternatives like download_arxiv or read_arxiv_paper. The description only states what it does, with no usage context, exclusions, or mention of alternative 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 carry the full burden. It describes the action, parameters, and return value (path to downloaded PDF), but does not disclose potential side effects such as directory creation, file overwriting, network behavior, or error handling. This is adequate for a simple download but lacks deeper 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 concise and well-structured, with a clear intro and formal Args/Returns sections. Every line contributes essential information and there is no filler, making it easy to parse.

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

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

The description covers the tool's purpose, both parameters, and the return value. However, it does not mention error handling, prerequisites, or what happens if the paper ID is invalid, and there is no output schema or annotations to fill that gap. It is adequate for simple invocation but not exhaustive.

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 provides no descriptions for its parameters (schema description coverage is 0%), but the description compensates by explaining paper_id with an example ('2009/101') and clarifying save_path as a directory with a default value. This adds significant meaning 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 'Download PDF of an IACR ePrint paper' with a specific verb (download), a specific resource (IACR ePrint paper), and an example paper ID format. This distinguishes it from sibling download tools for other sources and from read_iacr_paper, making the purpose 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 provides clear context: it is used to download the PDF of an IACR paper. However, it does not explicitly mention when not to use it or compare to alternatives like read_iacr_paper, so there is no exclusion guidance. The intended use case is clear but not fully elaborated.

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 of behavioral disclosure. It discloses that it downloads a PDF to a specified directory and returns the path, but omits details on directory creation, overwriting, and error handling.

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 well-structured: a one-line summary followed by clearly labeled Args and Returns sections. No extraneous 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?

For a simple download tool with two parameters and no output schema, the description covers purpose, inputs, defaults, and return value. It is complete enough 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?

The description explains paper_id as a medRxiv DOI and save_path as a directory with a default, adding meaning beyond the bare schema. This compensates fully for the 0% schema description 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 states 'Download PDF of a medRxiv paper.' This clearly specifies the action (download) and the resource (PDF of medRxiv paper), distinguishing it from sibling tools like download_arxiv and read_medrxiv_paper.

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 guidance on when to use this tool over alternatives. It does not mention sibling download tools or read_medrxiv_paper, leaving the agent without criteria for selection.

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 burden of disclosing behavior. It explicitly states that 'direct PDF download is not supported' and that the return value is a string message indicating that. This is a critical behavioral disclosure that informs the agent the tool will not actually download anything.

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 uses structured Args and Returns sections. It avoids unnecessary detail, though the 'Attempt to download' phrasing combined with the unsupported return message could be tightened for 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?

The description explains the tool's immediate behavior but lacks contextual completeness. It does not suggest alternative tools, explain why direct download is unsupported, or provide any steps for the agent to achieve the user's goal. With no output schema or annotations, the description leaves the agent without guidance on what to do next.

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 provide parameter meaning. It does so by identifying paper_id as a PubMed ID (PMID) and save_path as a directory with a default value. This adds value beyond the bare schema titles.

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 action ('download PDF of a PubMed paper') and names the resource, so it is not a tautology. However, it does not differentiate from sibling download tools, and the Returns section reveals the tool only reports that direct download is unsupported, making the actual purpose ambiguous.

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 read_pubmed_paper or other download tools. The description lacks any usage context, exclusions, or alternative recommendations.

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 burden of explaining behavior. It discloses the action (downloads a PDF), the argument formats, and the return value (path to downloaded file). It does not disclose potential side effects like file creation/overwriting, directory creation, error handling, or permission requirements. This 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 well-structured with Args and Returns sections. The format list is long but necessary for usability. The prose is efficient, though the list could be slightly more compact (e.g., using commas). Overall, every part 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 minimal schema, no output schema, and no annotations. The description covers the two parameters and the return value, which is sufficient for basic invocation. It lacks discussion of error cases or behavior when the paper is inaccessible, but for a straightforward download tool this is a reasonable level of completeness.

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

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

The schema has 0% description coverage, but the description compensates fully. It exhaustively enumerates the accepted formats for paper_id (Semantic Scholar ID, DOI, ARXIV, MAG, ACL, PMID, PMCID, URL) and specifies the default for save_path. This adds essential meaning 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 opens with a specific verb and resource: 'Download PDF of a Semantic Scholar paper.' This clearly distinguishes it from sibling download tools for arXiv, bioRxiv, PubMed, etc. The title and name are not tautological; the description adds the 'PDF' aspect.

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: use this when you need a PDF from Semantic Scholar, given a paper ID in one of the listed formats. However, it does not explicitly mention alternatives or when not to use it, such as 'for arXiv papers, use download_arxiv.' The guidance is clear but not explicit about 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?

Annotations are absent, so the description carries the full burden. It only states the basic function without disclosing side effects, permissions, read-only nature, or behavior when parameters are missing (both are optional). This is insufficient for 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 a single concise sentence, well-structured and easy to read. However, it is under-specified, so it does not fully earn its place by providing necessary details.

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

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

Given the tool has no annotations, no output schema, and a minimal schema, the description should compensate. It does not mention return format, error handling, or parameter usage, making it incomplete for an agent to rely on.

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

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

The schema has two parameters (id and document_id) with no descriptions, and the description does not explain them. With 0% schema coverage, the description fails to add any meaning about how to pass the search result identifier or which parameter to use.

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 fetches full document content for a search result, using a specific verb and resource. However, it does not distinguish itself from sibling read/download tools like read_arxiv_paper or download_full_paper_arxiv, which also fetch content.

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. There is no mention of prerequisites, such as needing a search result ID, nor any exclusions or preferred contexts.

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 burden. It discloses that fields are organized into categories and that omitting the category returns all categories and default fields. However, it does not mention return format, potential errors, or whether the list is exhaustive, leaving some transparency 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 well-structured with clear sections and front-loaded purpose. It is concise but has slight redundancy between the first and second sentences ('Get organized list' vs 'This tool provides a list'), which could be tightened.

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

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

The tool is simple with one optional parameter, and the description covers input behavior and default logic. However, with no output schema or annotations, it leaves the structure of the returned list unspecified. Overall, it is 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?

Schema coverage is 100%, but the description adds value by explaining the default behavior (returns all categories if not provided) and giving an example value ('conditions'). This goes beyond the schema's parameter description, which only lists allowed categories.

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: to get an organized list of available fields for customizing search results. It uses a specific verb ('Get') and names the resource ('available fields'), and it differentiates itself from sibling search tools by focusing on field discovery rather than executing searches.

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 that the fields are used to customize results of other search tools, making it clear when to use this tool. It does not explicitly name alternatives or exclusions, but the context implies it should be used before configuring search results.

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 burden. It discloses the return format ('dictionary format') and behavior when not found ('empty dict'), backed by a concrete example. It could add details about potential errors or rate limits, but the core behavior is 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?

The description is well-structured with clearly labeled Args, Returns, and Example sections. Every sentence adds value, and there is no redundancy or fluff.

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 one-parameter tool, the description covers the full essential context: purpose, parameter semantics, return type, and not-found behavior, plus a usage example. It is complete enough without an output schema.

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

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

The schema provides only the parameter name 'doi', but the description adds a meaningful explanation with an example ('10.1038/nature12373'). This compensates well for the 0% schema coverage, though it does not discuss malformed or missing DOI handling.

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 'Get a specific paper from CrossRef by its DOI', using a specific verb and resource. This distinguishes it from sibling tools like search_crossref (for searching) and download_crossref (for downloading).

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

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

The description implies usage when a DOI is already known, but does not explicitly mention when not to use it or point to alternatives such as search_crossref for finding papers without a DOI. The context of sibling tools partially fills this gap.

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 disclose behavioral traits. It describes the operation as retrieving statistical information, implying a safe read. However, it does not explain the nature of the statistics returned, potential performance implications, or any requirements, so transparency is moderate.

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 first two sentences are redundant ('Get statistical information about field values' and 'This tool retrieves statistical information...'), which wastes space. The input section is clearly structured but could be more concise. Overall, the description is acceptable but not tight.

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 only two optional parameters and no output schema, so the description must cover the result format. It explains the inputs well but does not describe what the statistics output looks like (e.g., counts, distributions). This leaves a gap in completeness for a stats 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% (both parameters are described). The description adds value by providing an explicit example for field_types (['ENUM', 'STRING']) and clarifying the purpose of each parameter, which goes beyond the schema alone.

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 statistical information about field values in the ClinicalTrials.gov database. This is a specific verb+resource pairing that distinguishes it from sibling tools like get_available_fields and trial search tools.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives. It implies usage (when you need statistics on field values) but does not offer conditions, exclusions, or references to sibling tools, leaving the agent to infer applicability.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the general action without revealing output format, error handling, network requirements, or whether it returns fields beyond the abstract. This lacks transparency for a tool with no structured 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 a single, clear sentence that directly states the tool's action and a key detail. It is concise without unnecessary filler, and effectively front-loads the main purpose.

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

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

Given no output schema and no annotations, the description must explain what the tool returns, but it only vaguely says 'detailed information including its abstract'. It does not list specific fields, clarify the scope of 'detailed', or note any limitations. This is insufficient for a tool without structured 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 single parameter 'paperId' is fully described in the input schema (e.g., 'Semantic Scholar ID, arXiv ID, DOI, etc.'), so the description adds no additional meaning. With 100% schema description coverage, 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 identifies the tool as retrieving detailed information about a specific paper, including its abstract. However, it does not distinguish itself from the sibling tool 'get_paper_details', which likely serves the same 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?

The description implies usage when one has a paper ID and wants its abstract/details, but it provides no explicit guidance on when to prefer this tool over alternatives like 'get_paper_details' or 'read_semantic_paper'. No exclusions or alternatives are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds no context beyond the tool's name — no mention of data source, return format, error behavior, side effects, or any limitations. The description is essentially a restatement of the tool 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 concise sentence with no unnecessary words. It is efficiently structured, though its brevity comes at the cost of missing needed context. Still, for what it is, it is appropriately sized.

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 no output schema and no annotations, and the description is vague about what 'detailed information' entails. This leaves ambiguity about whether this tool is appropriate compared to siblings that fetch papers from specific sources or that return abstracts. The description does not sufficiently complete the context needed for correct tool selection.

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; the single parameter paperId is described in the schema as 'The ID of the paper to retrieve details for.' The description text adds no additional parameter semantics, so the baseline of 3 applies.

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: 'Get detailed information about a specific research paper' — specific verb (Get) and resource (detailed information about a paper). However, it does not differentiate from sibling tools like read_semantic_paper or get-paper-abstract, so it's clear but lacks distinction.

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. The description does not mention context, prerequisites, or exclusions, leaving the agent to guess which of the many paper-related tools to select.

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

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

With no annotations, the description carries the transparency burden. It discloses the default behavior: 'If not provided, returns DETAIL_TOOL_DEFAULTS' and lists the 25 fields covered. This goes beyond the schema by explaining what data the agent will receive, though it doesn't disclose the full response structure or error handling.

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

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

The description is well-structured: a clear opening sentence, then a concise explanation of inputs with an example and a detailed default field list. Every sentence serves a purpose, and the format is easy to scan.

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 2 parameters and no output schema, the description does a good job explaining the return default fields. It could be more complete by describing the response format or mentioning error cases, but the list of 25 fields gives the agent a solid understanding of 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?

The schema already describes both parameters (100% coverage), so the baseline is 3. The description adds value with an example NCT ID ('NCT04280705') and elaborates on the 'fields' parameter by explaining the default field set. This extra context helps the agent understand how to use the parameters.

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

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

The description clearly states the tool's purpose: 'Get comprehensive details for a single clinical trial' and 'retrieves detailed information for a single clinical trial given its NCT ID.' The verb 'retrieves' and resource 'single clinical trial' are specific, and the use of 'single' distinguishes it from the sibling tool get_trial_details_batched.

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 context is clear: use this tool when you have an NCT ID and need detailed trial information. The description says 'given its NCT ID' but does not explicitly discuss when to use alternatives like get_trial_details_batched or mention exclusions. This is clear context without explicit alternatives, warranting a 4.

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, and it discloses key behaviors: batching with a default size, preservation of input order, and the exact default field set. It also clarifies what happens when `fields` is not provided. It doesn't discuss errors or response envelope, but for a read-style batched lookup it is adequately 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?

The description is well-organized and front-loaded with the core purpose, then provides batching semantics, usage, and input details. The field list adds length but is valuable and directly relevant. There is slight redundancy between the intro and the input section, but no filler.

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 moderate complexity and absence of an output schema, the description covers batching behavior, ordering, default field set, and usage timing relative to search tools. It does not specify error handling or exact response structure, but the tool is simple enough that this is acceptable.

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%, but the description adds substantial value by enumerating the 25 default fields and clarifying the default batch_size and behavior. It explains that `fields` defaults to a specific list, which the schema only shows as null. This goes beyond the schema descriptions.

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

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

The description opens with a specific verb ('Retrieve'), names the resource ('detailed clinical trial records'), and highlights batching, which distinguishes it from the singular get_trial_details sibling. It also explains why batching is used ('to reduce payload during discovery').

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

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

The description explicitly states when to use the tool: 'Use this after search tools which return a minimal field set.' This provides clear usage context as a follow-up enrichment step. It does not name alternatives or exclusions, but the context 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 carries the full burden of behavioral disclosure. It only states 'look up' which implies a read operation, but it does not disclose important traits such as maximum batch size, handling of invalid IDs, rate limits, or the structure of the returned data. This is insufficient for a batch 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 concise sentence that directly states the tool's function without any wasted words. It is front-loaded and appropriately sized for a simple operation.

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 batch lookup tool with no output schema and no annotations, the description is too sparse. It does not mention return format, batch limits, or error behavior, leaving significant gaps for an agent to invoke the tool correctly. The parameter is well-documented, but the operation-level context is lacking.

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 fully documents the single parameter 'paperIds' with a clear description ('Array of paper IDs (Semantic Scholar IDs, arXiv IDs, DOIs, etc.)'). The tool description adds no additional parameter meaning, so the baseline of 3 applies due to high schema coverage.

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

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

The description 'Look up multiple papers by their IDs' uses a specific verb ('look up') and resource ('multiple papers'), clearly distinguishing this batch operation from single-paper lookup or search siblings. The phrase 'by their IDs' specifies the input method, making the tool's function 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 provides no guidance on when to use this tool versus alternatives like get_paper_details or search_papers. There is no mention of when batch lookup is appropriate, nor any exclusions or prerequisites. The implied usage is minimal and not explicitly 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?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the core function without mentioning read-only intent, pagination details, response structure, or potential errors. The schema covers parameter formats but not behavior such as how citations are ordered or what constitutes a 'paper that cites'.

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

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

The description is a single, well-structured sentence that front-loads the action and object. Every word is meaningful, and there is no wasted text.

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

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

The tool is a simple lookup with well-documented parameters, making the minimal description acceptable for basic use. However, it lacks usage guidance and behavioral details, so it is not fully complete for an agent that needs to decide between this and the reference-listing sibling. Given the simplicity and complete schema, a score of 3 reflects the missing 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 input schema provides 100% description coverage for all three parameters, including the required paperId and the pagination controls limit/offset. The description adds no extra semantic meaning beyond what the schema already states, so the baseline score of 3 applies.

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 clearly identifies the resource as 'papers that cite a specific paper'. This unambiguously differentiates it from sibling tool papers-references, which would return the papers cited by a given paper. The directionality is explicit and distinct.

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 this tool versus alternatives. It does not mention that papers-references covers the opposite direction or that this is for forward citations. An agent must infer usage context from the tool name alone.

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

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the core action and mentions 'advanced filtering' but reveals nothing about result handling, pagination, error behavior, 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 is a single concise sentence that directly states the action and resource, with no wasted words. It is appropriately 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?

For a tool with 10 parameters and no output schema, the description is minimal. It omits broader context such as typical use cases, return value expectations, or limitations, leaving the agent without enough operational 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 input schema covers 100% of the 10 parameters with individual descriptions, so the baseline is 3. The phrase 'advanced filtering options' vaguely hints at the available filters but does not add meaningful detail 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 searches for academic papers and mentions 'advanced filtering options' as a differentiator. However, it doesn't explicitly distinguish it from sibling tools like papers-search-basic beyond the 'advanced' modifier.

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 instead of alternatives such as search, search_papers, or papers-search-basic. The term 'advanced' implies a use case but offers no explicit context, exclusions, or alternative references.

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 disclosure burden. It only states 'get papers' and does not describe pagination behavior, error handling, or any side effects. The limit and offset parameters are present in the schema but not explained behaviorally in the description.

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 short sentence that is front-loaded with the essential action. No wasted words or irrelevant details.

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

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

Given no output schema and no annotations, this terse description leaves out important context such as the exact return format, whether results are paginated, and how IDs are interpreted. It is minimally sufficient but incomplete for an agent that needs to reliably invoke and interpret the result.

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 no parameter-specific information, relying entirely on the schema's own descriptions of paperId, limit, and offset.

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 papers that are cited by a given paper, effectively listing its references. The verb 'get' and resource are specific, but it does not explicitly differentiate from the sibling 'papers-citations' tool, which likely retrieves incoming citations.

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 guidance on when to use this tool versus alternatives. It does not mention that papers-citations might be more appropriate for finding citing papers, nor any other situational 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 provided, the description carries full responsibility for disclosing behavioral traits. It offers none—no mention of result format, sorting, scope of 'academic papers,' or any side effects. This is a bare minimum statement with zero behavioral context.

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

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

The description is a single, front-loaded sentence with no redundant words. It is highly concise, though it sacrifices substance for brevity. The efficiency is acceptable, but it leaves out critical information that would not compromise 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 simplicity and the absence of an output schema, the description should at least explain what 'academic papers' covers, how results are ordered, and how this relates to sibling search tools. It does none of this, leaving the agent with insufficient context to select or 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?

The input schema already provides 100% description coverage for both parameters ('query' and 'limit'). The description adds no new meaning beyond the schema; the word 'simple' is the only extra hint, and it is vague. Baseline 3 applies 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 action ('Search') and the resource ('academic papers'), making the core purpose evident. However, it does not distinguish itself from sibling tools like 'paper-search-advanced' or 'search_papers', relying solely on the 'basic' in the tool name.

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 the many alternative search tools. The phrase 'simple query' implies a limited use case, but it never explicitly states when to choose this over advanced or source-specific search options.

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 discloses the side effect of saving the PDF to a directory via save_path and states the return type. However, it omits error handling, network dependence, and file overwrite behavior, which are relevant but not critical.

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 structured docstring with a clear first sentence, followed by concise Args and Returns sections. No redundant content; every line provides necessary 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?

For a simple tool with no output schema, the description covers purpose, both parameters, and the return value. It could mention error cases or explicitly state that the PDF may be downloaded if missing, but the save_path hint covers this implicitly.

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 fully compensates. It explains paper_id with an example ('2106.12345') and clarifies save_path as a directory with a default value, adding meaning beyond the bare schema fields.

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 reads and extracts text content from an arXiv paper PDF, using a specific verb and resource. It distinguishes itself from sibling tools by specifying arXiv as the source and text extraction as the output, unlike download_arxiv or search tools.

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

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

The description provides clear context that this tool is for reading/extracting text from an arXiv paper PDF, implying usage when full text content is needed. It does not explicitly mention alternatives or exclusions, but the context is self-evident.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the PDF may be saved to save_path ('is/will be saved'), which is a useful behavioral detail. However, it does not mention network dependencies, error conditions, or potential side effects beyond file saving.

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

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

The description is extremely concise: one purpose sentence, then an Args list and Returns line. Every element is relevant, front-loaded, and no unnecessary details are included.

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 2-parameter tool with no output schema and no annotations, the description covers the core functionality, parameter meanings, and return type. Minor gaps include not explaining whether the tool always downloads the PDF or uses an existing file, and no mention of error handling, but it remains mostly complete for typical use.

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 paper_id as 'bioRxiv DOI' and save_path as 'directory where the PDF is/will be saved' with a default, adding meaningful clarity beyond the schema's bare type definitions.

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: 'Read and extract text content from a bioRxiv paper PDF.' The specific resource (bioRxiv paper) and action (read/extract) differentiate it from sibling tools like download_biorxiv and search_biorxiv.

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 by specifying it operates on bioRxiv DOI and saves/reads from a directory. It implies when to use it (for extracting text from bioRxiv papers) but doesn't explicitly mention alternatives or exclusions, such as 'use download_biorxiv to download only.'

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

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

The description discloses that the tool returns a message indicating direct paper reading is not supported, explaining the underlying limitation. It also adds context about the save_path for a PDF. However, there is slight ambiguity about whether any download actually occurs before the unsupported message is returned, given the mention of 'PDF is/will be saved'.

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

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

The description is well-structured with an action sentence, Args, Returns, and Note sections, placing the core behavior first. It is reasonably concise, though the Returns and Note sections somewhat repeat the 'not supported' message, which could be condensed.

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 two-parameter tool without annotations or output schema, the description covers the behavioral limitation, parameter meanings, and return type. It could be more explicit about side effects (e.g., whether a PDF is actually downloaded or if the save_path is used at all), but overall it provides sufficient context for an agent to understand the tool's behavior.

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 enriches the schema by specifying paper_id as a CrossRef DOI with an example ('10.1038/nature12373') and explaining save_path as the directory where the PDF is/will be saved. Since schema description coverage is 0%, this added detail is essential for correct usage and fully compensates for the lack of schema descriptions.

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: 'Attempt to read and extract text content from a CrossRef paper.' It distinguishes the tool from sibling read_* tools by specifying the CrossRef source. The subsequent clarification that reading is not supported is transparent and does not obscure the primary 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?

The description provides context by noting that CrossRef is a citation database and doesn't provide direct paper content, and advises using the DOI through the publisher's website. This gives clear guidance on when not to use this tool, though it doesn't explicitly mention alternative sibling tools for downloading or retrieving metadata (e.g., download_crossref or get_crossref_paper_by_doi).

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

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

With no annotations provided, the description carries full burden. It discloses that the tool extracts text and mentions a side effect via save_path ('where the PDF is/will be saved'), implying it may download the paper. However, it does not detail error behavior, network usage, or what happens if the paper_id is invalid or the PDF is already present.

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: the first sentence states the purpose, followed by clear Args and Returns sections. Every sentence adds value, and the structure is easy to parse.

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

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

For a simple tool with 2 parameters and no output schema, the description covers purpose, parameters, and return type. It is nearly complete, but could be improved by explicitly stating whether it always downloads the PDF or reads from an existing file, and by mentioning potential edge cases like invalid paper_id.

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 0% (no descriptions in the schema), so the description must compensate. It does so by explaining paper_id with a concrete example ('2009/101') and clarifying save_path's role as the directory for the PDF. This adds meaning beyond the bare property names and default 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 clearly states the tool's function: 'Read and extract text content from an IACR ePrint paper PDF.' The verb 'read and extract' is specific, and 'IACR ePrint paper' distinguishes it from sibling tools like read_arxiv_paper or download_iacr.

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

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

The description implies usage: when you need text content from an IACR ePrint paper. However, it does not explicitly state when to use this tool versus downloading the PDF (download_iacr) or searching (search_iacr), nor does it mention any exclusions or 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?

With no annotations, the description carries the burden. It does disclose the return type and hints at the save_path side effect ('is/will be saved'), but it remains ambiguous whether the tool downloads the PDF or reads an existing file. This lack of clarity about side effects and failure modes warrants a mid-range score.

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

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

The description is appropriately concise and front-loaded with a clear summary, followed by structured Args and Returns sections. Every sentence adds value, with no 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?

Given the tool's low complexity (2 params, no output schema), the description adequately covers purpose, parameter meanings, and return type. The only minor gap is the ambiguity about downloading behavior, which would be useful for complete 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 meaningful context for both parameters: paper_id is defined as a medRxiv DOI and save_path as a directory where the PDF is/will be saved. This fully compensates for the 0% schema description coverage, going beyond the schema's type/default information.

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 'Read and extract text content from a medRxiv paper PDF', which uses a specific verb and resource. This distinguishes it from sibling tools like download_medrxiv (which downloads) and search_medrxiv (which searches).

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 usage guidance or mention of alternatives. It does not explain when to use this tool versus download_medrxiv or other read_* siblings, nor does it state any prerequisites 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 are provided, so the description carries the full burden. The Returns line does disclose that direct paper reading is not supported and that it returns a message to that effect—useful transparency. However, this contradicts the opening sentence, creating internal inconsistency that undermines trust in the behavioral description.

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 compact and well-structured with Args and Returns sections, wasting few words. However, the misleading first sentence detracts from overall clarity, preventing a perfect score.

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 is essentially a stub, the description omits crucial context: why reading is unsupported, what alternatives exist, and what the user should do instead. With no output schema and no annotations, the description leaves the agent without enough guidance to use the tool effectively.

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

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

With 0% schema description coverage, the description meaningfully compensates by explaining paper_id as 'PubMed ID (PMID)' and save_path as 'unused'. This adds practical value beyond the raw schema, even though save_path is documented as unused rather than functional.

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

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

The description opens with 'Read and extract text content from a PubMed paper', a clear verb+resource, but the Returns section explicitly states 'direct paper reading is not supported.' This directly contradicts the stated purpose, making the tool's actual function ambiguous and misleading. It also does not distinguish itself from sibling read_* tools like read_arxiv_paper or read_medrxiv_paper.

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 about when to use this tool versus alternatives. Since the tool does not support reading, it should explicitly direct users to alternatives such as download_pubmed or get_paper_abstract, but no such advice is present.

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 does disclose that the PDF is saved to save_path and that the return is a string, which is useful. However, it omits details about whether the tool downloads the PDF, how errors are handled, or potential side effects like directory creation or overwriting, leaving some behavioral ambiguity.

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

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

The description is well-structured with a short one-line purpose, an Args section, and a Returns section. Every part is necessary, and the format list is organized with bullet points, making it easy to scan. 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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description covers purpose, parameters, and return type well. However, it lacks usage guidance and does not mention prerequisites or error conditions (e.g., network access, paper availability), leaving minor gaps in completeness.

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

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

The schema has zero descriptions for parameters (coverage 0%), and the description compensates fully. It provides an extensive explanation of paper_id with eight allowed formats and concrete examples, plus clarifies save_path with its default value. This is far beyond the minimal schema information.

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 ('Read and extract text content') and the resource ('Semantic Scholar paper'). It distinguishes itself from sibling read_* tools by specifying the Semantic Scholar source and providing multiple identifier formats, making the 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?

Usage is implied by the description: use this when you want to read a Semantic Scholar paper. However, it does not explicitly mention alternatives or when not to use this tool. The list of accepted ID formats includes ARXIV, which could overlap with read_arxiv_paper, but no guidance is given for choosing between them.

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 disclose behavioral traits, but it only states that the tool aggregates sources. It doesn't mention output format, pagination, rate limits, or any side effects, offering minimal behavioral 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 a single, concise sentence with no wasted words, but it is under-specified rather than efficiently informative. It earns its place but lacks substance.

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 basic schema and lack of annotations/output schema, the description should clarify sources, return values, and usage context. It is too vague to allow an agent to use it correctly, especially with many sibling search tools.

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 0%, and the description provides no explanation of the 'query' or 'max_results' parameters beyond their basic names. The description adds no meaning beyond the input schema, failing to compensate for the lack of schema descriptions.

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 it's a 'search tool aggregating across sources,' which is a clear verb+resource and distinguishes it from source-specific sibling tools like search_arxiv or search_pubmed. However, it doesn't specify which 'sources' are aggregated, leaving some ambiguity about its exact scope.

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 'aggregating across sources' implies usage when a cross-source search is needed, but there's no explicit guidance on when to prefer this over source-specific searches or any exclusions. It provides only implied 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 carries the burden of disclosing behavior. It only mentions 'using their API' but doesn't describe rate limits, error handling, return format, or any side effects. This is a search operation, but the safety profile isn't confirmed.

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 fluff, front-loading the action and resource. It's concise but perhaps too sparse, though that's more an issue for other dimensions. For this dimension, it earns points for efficiency.

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 6 parameters, no output schema, and no annotations, the description is insufficiently complete. It doesn't mention what the result contains, pagination behavior, or any specifics about search types or sort orders. The tool is more complex than the description implies.

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 each parameter, so the schema does the heavy lifting. The description adds no additional meaning or context about parameters, but the baseline of 3 applies because the schema is comprehensive.

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 on arXiv via their API. It's specific about the resource (arXiv papers) and the action (search), which differentiates it from generic search tools, though it doesn't explicitly distinguish from similar arXiv-specific siblings like 'search_arxiv'.

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 like search_biorxiv or paper-search-advanced. There are no stated exclusions or context cues. The only implication is that it's for arXiv, but that's minimal.

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

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

With no annotations, the description must carry the full burden of disclosing behavioral traits. It mentions the return type (list of paper metadata) but does not explicitly state that the operation is read-only, whether authentication is required, or any rate limits. This is insufficient for a no-annotation tool, making transparency weak.

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 six lines: a front-loaded purpose, an Args block, and a Returns block. Every sentence earns its place with no fluff or unnecessary detail, making it appropriately concise and well-structured.

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

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

For a simple search tool, the description covers the essential aspects: purpose, parameters, and return type. However, given the large number of sibling search tools, some additional context about scope or differences (e.g., metadata-only vs full-text) would improve completeness.

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

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

The input schema has zero description coverage, and the description's Args section fully compensates by explaining 'query' (search query string with example) and 'max_results' (maximum number of papers with default). This adds clear semantic meaning beyond the structural schema fields.

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

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

The description opens with 'Search academic papers from arXiv,' which clearly identifies the verb and resource. However, it does not differentiate from the similar sibling tool 'search-arxiv' (hyphenated), so it loses a point for lack of sibling distinction.

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 guidance on when to use this tool versus alternatives like search-arxiv, papers-search-basic, or paper-search-advanced. It simply states what the tool does without any 'when to use' or 'when not to use' 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 provided, the description carries the burden of disclosing behavior. It clearly explains that the tool aggregates results from two sources, returns formatted BibTeX entries ready for LaTeX, and implies a read-only search operation. This goes beyond a simple 'Search papers' statement and provides useful behavioral context for the agent.

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

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

The description is two sentences, front-loaded with the primary purpose, then adds key details about sources and output format. Every sentence earns its place with no redundant information, making it highly concise and well-structured.

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

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

Given the tool's simplicity, rich schema coverage, and absence of an output schema, the description is largely complete. It explains what the tool does, which sources it uses, and the output format. It doesn't mention potential quirks like duplicate handling across sources or pagination, but these are not critical for a search tool with this level of schema documentation.

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

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

The schema already provides 100% coverage with rich descriptions for all four parameters, including examples for query and author, a range for num, and a clarifying explanation for year. The description adds minimal semantic value beyond what the schema states, so the baseline score of 3 applies.

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 academic papers and return BibTeX citations', using a specific verb and resource. It distinguishes itself from sibling search tools (like search_arxiv or search_pubmed) by mentioning the two sources (DBLP and Semantic Scholar) and the BibTeX output format, making the purpose immediately clear.

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

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

The description provides context by specifying that it searches DBLP for computer science and Semantic Scholar for broad academic coverage, implying when it is appropriate to use. It does not explicitly compare to alternatives or state exclusions, but the source coverage and citation format give a clear usage context, meeting the 'clear context, no exclusions' benchmark.

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

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only states the basic function and return type, without mentioning whether the operation is read-only, any authentication/rate-limit constraints, or pagination behavior. This leaves the agent without advanced behavioral 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 compact and front-loaded with the main purpose, followed by clearly structured Args and Returns sections. Every sentence adds useful information, with no redundant text. It is appropriately sized for a tool with two simple parameters.

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 the return format clearly, but it only states 'List of paper metadata in dictionary format' without specifying the dictionary fields. The absence of usage context and behavioral details also limits completeness, but the tool is simple enough that this is adequate.

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?

Although the input schema has no descriptions (coverage 0%), the description adds meaningful parameter details: 'query: Search query string (e.g., 'machine learning')' and 'max_results: Maximum number of papers to return (default: 10)'. This goes beyond the schema's type information and includes an example and default 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 opens with a clear verb+resource statement: 'Search academic papers from bioRxiv.' This makes the tool's purpose evident and distinguishes it from sibling tools for other repositories (e.g., search_arxiv, search_pubmed). The Args and Returns sections further clarify the scope.

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 bioRxiv searches but does not explicitly state when to use this tool over alternatives like search_arxiv or download_biorxiv. No exclusions or alternative recommendations are provided. An agent must infer from the tool name and the word 'bioRxiv'.

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, but it fails to disclose a critical inconsistency: it presents `**kwargs` as Python keyword arguments while the input schema declares a single required string property named `kwargs`. The description does not explain how to serialize filter/sort/order into that string, making it unclear how to correctly invoke the 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 well-structured with Args, Returns, and Examples sections, making it easy to scan. The introductory background on CrossRef is slightly extraneous but not harmful. It earns a high score for organization and front-loading the purpose, though it is longer than strictly necessary.

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 the detailed examples, the description lacks essential context for correct invocation: the schema requires `kwargs`, but the description treats it as optional Python `**kwargs`; there is no guidance on string formatting. It also omits pagination, error behavior, and the shape of returned metadata (no output schema exists), leaving significant gaps for an 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?

The description adds meaningful semantics for query (with examples), max_results (default and max), and kwargs sub-parameters (filter, sort, order). However, it never bridges the gap between the `**kwargs` concept and the actual `kwargs` string parameter in the schema, so the added value is undermined by the unresolved serialization question.

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

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

The description opens with a clear, specific verb+resource statement: 'Search academic papers from CrossRef database.' It explicitly identifies the database and the search action, distinguishing it from sibling tools like search_arxiv or search_pubmed despite not naming alternatives.

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?

Usage is implied by the name and 'Search academic papers from CrossRef database,' but there is no explicit guidance on when to choose this over other search tools (e.g., search_papers, paper-search-advanced) or when not to use it. No alternatives or exclusions are 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 the full burden of behavioral disclosure. It mentions it returns a list of paper metadata, but does not disclose potential rate limits, authentication requirements, or any limitations. As a read-only search tool, the risk is lower, but the description is still minimal.

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 well-structured with an Args/Returns format. Every sentence adds value: the main purpose, parameter explanations, and return type. There is no redundant or 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?

For a two-parameter search tool with no output schema, the description covers the essential information: what it does, parameters, and return format. However, it lacks detail on the exact structure of the paper metadata dictionaries and does not mention any usage caveats, which would improve completeness.

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

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

The schema has 0% description coverage, so the description must compensate. It clearly explains both parameters: 'query' with an example and 'max_results' with its default and meaning. This provides complete semantic information beyond the raw 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 specifies the verb 'Search' and the resource 'academic papers from Google Scholar', making its purpose explicit. It distinguishes itself from numerous sibling search tools by naming Google Scholar as the source, which is unique among the siblings.

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 this tool should be used for searching Google Scholar, but it does not explicitly state when to use it over alternatives or provide any exclusions. There is no mention of alternative tools for other academic sources, leaving the choice somewhat implicit.

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. It explains the return format and parameters, but does not explicitly state read-only behavior, rate limits, or potential side effects. It is adequate for a simple search tool but not rich.

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

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

The docstring is well-structured with Args and Returns sections, every sentence provides useful information, and there is no redundancy.

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

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

Given the tool's simplicity and lack of output schema, the description covers the purpose, parameters, and return format comprehensively. It is complete for an agent to 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?

Schema coverage is 0%, but the description provides detailed meaning for all three parameters, including examples and defaults. This fully compensates for the lack of schema descriptions.

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 academic papers from IACR ePrint Archive, with a specific verb and resource. It distinguishes from sibling archive search tools by naming the archive.

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 context is clear: this searches IACR ePrint Archive, implying it should be used for IACR papers. However, it lacks explicit mention of alternatives or when not to use it, so it falls short of a 5.

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. It states the return format ('List of paper metadata in dictionary format') and implies a read-only search operation, but does not disclose any potential rate limits, auth requirements, or other 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?

The description is compact and front-loaded with the main purpose, followed by structured Args and Returns sections. Every sentence provides necessary information without redundancy.

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

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

For a simple search tool with two parameters and no output schema, the description provides the essential purpose, parameter meanings, and return format. It could further clarify when to use this rather than sibling search tools, but the scope is adequately covered.

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

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

The schema has no property descriptions (0% coverage), and the description compensates by explaining 'query' as a search query string with an example and 'max_results' as the maximum number of papers to return with a default. This adds meaningful context beyond the bare parameter names.

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

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

The description uses the specific verb 'Search' with the resource 'academic papers from medRxiv', clearly distinguishing this tool from sibling search tools for other repositories. The purpose is immediately understandable.

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 searching medRxiv papers but does not explicitly mention when to use this tool over alternatives like search_biorxiv or search_arxiv. No exclusion criteria or alternative recommendations 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?

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'search' and does not mention whether the operation is read-only, what API endpoint is used, how results are returned, or any side effects. This is a significant gap for a tool with no 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 a single concise sentence with no redundant words, which is efficient. However, it is also under-specified, so while it earns points for brevity, it lacks substantive content that would make it fully effective.

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

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

With no output schema and no annotations, the description should explain return values or behavioral context. It does not. The tool is simple, but the description still fails to provide sufficient context for an agent to know what to expect, especially given the large number of sibling search tools.

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 adds no additional meaning beyond the schema's parameter descriptions for 'searchTerm' and 'numPapers', but the schema already documents these adequately.

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 the tool 'Search for research papers', which clearly identifies the action (search) and resource (research papers). However, it does not distinguish this from the many sibling search tools like search_arxiv or search_pubmed, so it lacks sibling 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 is provided on when to use this tool versus the numerous alternative search tools. The phrase 'using the research API' is vague and does not clarify context, prerequisites, 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 are provided, so the description carries the full burden. It only reveals fuzzy matching via 'closest title match,' but fails to disclose return format, pagination, side effects, or how filtering parameters interact. This is a significant gap for a tool with six siblings.

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 gets to the point without padding. However, its brevity borders on under-specification, though for a simple core action it remains 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?

With 5 parameters, no output schema, and no annotations, the description leaves too many questions unanswered: what does a 'closest match' return exactly? What is the output format? How do the additional filters affect the search? The description does not adequately cover the tool's context for an agent to invoke it confidently.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description itself adds no parameter-level meaning beyond what the schema already provides, not even clarifying how 'title' is matched or how year/citation filters combine.

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 clear verb ('find'), a specific resource ('paper'), and a distinct matching method ('closest title match'). This distinguishes it from generic search tools like papers-search-basic, though it lacks explicit differentiation from other title-focused searches.

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 the many sibling search tools. It does not mention alternatives, prerequisites, or scenarios where another search would be more appropriate.

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

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

No annotations are provided, so the description is the main source of behavioral disclosure. It states that the tool returns a list of paper metadata dictionaries and that max_results caps the number of returned papers, but it does not mention pagination, rate limits, authentication, or exact result fields.

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 compact docstring with clear Args and Returns sections, no filler, and 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 simple two-parameter search tool, the description covers arguments and return type. It could be improved by specifying the exact dictionary keys or any result limits, but it is sufficient for basic 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 coverage is 0%, but the description fully documents both parameters: query with an example and max_results with its default and meaning as a maximum count. This compensates for the missing schema descriptions.

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

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

The description uses the specific verb 'search' with an explicit resource 'PubMed' and summarizes the result as academic paper metadata. This clearly distinguishes it from siblings like search_arxiv or search_medrxiv.

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 clearly identifies PubMed as the target database, implying use when searching PubMed literature. However, it does not explicitly discuss alternatives or when not to use it.

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

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

With no annotations provided, the description carries the full burden. It provides return format and parameter details but does not explicitly disclose that the tool is read-only, any rate limits, or authentication requirements. For a search tool, the first sentence implies a non-mutating operation, but this is not explicitly stated.

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

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

The description is well-structured with Args and Returns sections, using concise bullets. Every sentence earns its place, with no filler 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?

For a relatively simple search tool with 3 parameters and no output schema, the description covers purpose, parameters, and return format adequately. It could mention pagination or potential rate limits, but these are not essential for a basic search 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%, but the description compensates fully by explaining each parameter. It offers detailed format examples for year ('2019', '2016-2020', '2010-', '-2015') and states the default for max_results, adding significant value beyond the schema's type declarations.

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 academic papers from Semantic Scholar, specifying both the action and the specific source. This distinguishes it from sibling search tools like search_arxiv or search_pubmed, which target different databases.

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 queries against the Semantic Scholar database but does not explicitly state when to choose this tool over other search tools. No exclusions or alternatives are mentioned, leaving the usage context to be inferred from the source name.

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 present, so the description carries full behavioral disclosure. It explains the two-step process (API seeding with acronyms followed by local filtering), distinguishes between exact and partial matching with case-insensitivity, and clarifies that max_studies limits the API request rather than the final result count. It also enumerates the default return fields. This is a model of 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 front-loaded with a one-sentence purpose statement, followed by a brief mechanism paragraph and an Input section that elaborates on each parameter. Every sentence serves a purpose: the example anchors the usage, the matching rules define behavior, and the default field list provides essential return information. There is no fluff or redundancy.

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

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

Even without an output schema, the description gives a clear picture of what the tool returns by listing the eight default fields. It covers the algorithm, matching semantics, and parameter nuances. For a search tool with 4 parameters and no annotations, this description is complete enough for an agent to select and invoke 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?

Although schema coverage is 100%, the description adds significant value: it clarifies max_studies is the 'maximum number of studies to request from the API' (not just returned), specifies case-insensitive behavior for exact_match, and lists the full set of ACRONYM_SEARCH_DEFAULTS fields when 'fields' is omitted. This goes well beyond the schema's basic field descriptions.

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

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

The description opens with a clear verb+resource+scope statement: 'Search clinical trials by study acronym.' It then specifies the exact field used (protocolSection.identificationModule.acronym), provides a concrete example ('TETON'), and contrasts with other trial search tools by focusing exclusively on acronym-based lookup. This is specific and distinguishable from siblings.

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 context clearly implies this tool is for finding trials by acronym, and the details of the search/filter process give a good sense of when to use it. However, it does not explicitly compare with sibling tools such as search_trials_by_condition or state when not to use this tool. The guidance is implied rather than explicit, but the naming and opening sentence make the intended use obvious.

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

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

With no annotations, the description must carry the behavioral disclosure burden. It does add useful context: trials are matched by 'any of' the specified conditions, and it lists the default fields returned. However, it omits behaviors like pagination, error handling, or how queries are interpreted beyond the 'any of' statement, so it is not fully 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?

The description is well-structured with an opening purpose statement and a bulleted 'Input' section. It is slightly redundant in repeating that the tool searches by medical conditions, but every other sentence provides useful detail (defaults, examples, field list) without excessive verbosity.

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 moderate complexity (3 parameters, no output schema), the description covers the essential context: what conditions match, how many results are returned, and which fields appear by default. It does not describe the raw response structure or error cases, but for a search tool with high schema coverage this is a reasonably complete 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 already provides 100% description coverage, but the description adds value by giving an example of the conditions parameter and explicitly enumerating the nine default fields returned when 'fields' is not provided. This goes beyond the schema by clarifying real-world usage and default output contents.

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

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

The description opens with a specific verb+resource: 'Search clinical trials by medical condition(s).' This clearly states what the tool does and, combined with the sibling tool names (search_trials_by_acronym, search_trials_by_intervention, etc.), distinguishes it as the condition-based search 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 description explains how to use the tool (provide conditions, max_studies, fields) but does not explicitly state when to prefer it over sibling trial search tools or when not to use it. The sibling names imply alternatives, but the description itself offers no comparative 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 present, so the description bears the burden of behavioral disclosure. It explains the OR-semantics for interventions and the default fields via SEARCH_TOOL_DEFAULTS, but it does not mention read-only status, potential errors, or output structure. These are useful but not fully comprehensive; a search tool implies read-only, yet this is not explicitly stated.

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

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

The description is well-structured with an intro and bullet-point-style parameter explanations. It is reasonably concise, though it slightly repeats 'intervention/treatment' and could be tighter. The example and defaults list are valuable, so it earns a high score for structure without being overly verbose.

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 values. It partially does by naming the default return fields, but it does not describe the result container or pagination. Given the tool's simplicity, this is adequate but leaves gaps for an agent trying to consume results. It is complete enough for basic use but lacks full contextual details.

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 meaningful value beyond schema by clarifying that multiple interventions are matched using OR, providing an example for the interventions parameter, and explicitly listing the SEARCH_TOOL_DEFAULTS fields when fields is not provided. This goes beyond the schema's simple parameter names.

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 clinical trials by intervention/treatment' with a specific verb and resource, and distinguishes it from sibling tools like search_trials_by_condition and search_trials_by_sponsor by explicitly naming the search dimension (interventions).

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—it searches for trials matching any of the provided interventions, with defaults for max results and return fields. However, it does not explicitly mention alternative tools or exclusions (e.g., 'use this instead of search_trials_by_condition'), so it lacks the when-not-to-use guidance for a top score.

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

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

With no annotations, the description carries the full transparency burden. It discloses the default field set (SEARCH_TOOL_DEFAULTS) and input format, but it does not mention edge cases (e.g., invalid IDs, missing fields), output structure, or rate limits. It adds some context beyond the schema but lacks comprehensive behavioral disclosure.

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 reasonably sized but contains redundancy: the first sentence 'Retrieve specific clinical trials by NCT ID(s)' is immediately restated in the next sentence 'This tool allows you to retrieve...'. The structure is clear with labeled parameters, but the redundancy adds 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?

The description covers the essential input parameters and default fields, but it does not explain the return format (no output schema) or error behavior. Given the tool's simplicity and availability of sibling tools like get_trial_details, more context would help but is not wholly missing.

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

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

The schema provides descriptions for both parameters, but the tool description adds meaningful detail: the example for nct_ids and the explanation of the default behavior when fields is omitted. This goes beyond the schema's minimal 'NCT IDs to retrieve' and 'Specific fields to return'.

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 clinical trials by NCT ID(s) with a specific verb ('Retrieve') and resource, distinguishing it from sibling search tools like search_trials_by_condition. However, it does not explicitly differentiate from the similarly named search_trials_nct_ids_only, so it falls short of a 5.

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 use when you have known NCT IDs, but it does not explicitly state when to use this tool versus alternatives like get_trial_details or search_trials_nct_ids_only. There is no when-not-to-use guidance, making it only minimally adequate.

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. It discloses the OR behavior across sponsors, the default return fields (SEARCH_TOOL_DEFAULTS), and the max_studies parameter. This gives a good sense of what the tool does and its constraints.

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 front-loaded with a one-sentence summary, then uses a structured bullet list for parameters. It is thorough without being verbose, and every sentence contributes 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?

For a read-only search tool with no output schema, the description covers the purpose, all parameters, defaults, and return field behavior. It is self-contained and sufficient for an agent to 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?

The input schema has brief descriptions, but the tool description adds substantial value: explains that sponsors is a list with OR logic, gives an example, explains the default fields, and states that max_studies defaults to 50. This goes well 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 opens with 'Search clinical trials by sponsor/organization,' which clearly specifies the verb (search), resource (clinical trials), and scope (by sponsor/organization). This distinguishes it from sibling tools like search_trials_by_condition or search_trials_by_acronym.

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 clearly indicates the use case (searching trials by sponsor) but does not explicitly compare with alternatives or state exclusions. However, the context is clear enough that an agent would know when to invoke it.

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

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

No annotations are provided, so the description must carry the full burden. It discloses the default return fields (SEARCH_TOOL_DEFAULTS) and max_studies default, offering some transparency about output. However, it does not describe API behavior such as how criteria are combined (AND/OR), error handling, or rate limits. For a read-only search tool, this is acceptable but leaves room for improvement.

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

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

The description is well-structured with a clear one-sentence purpose followed by a bulleted parameter list. Every sentence adds necessary information, and the front-loaded purpose makes it easy to scan. No redundant or vague phrasing.

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 parameters, default behavior, and return fields. It does not explain how criteria are combined (e.g., AND vs OR) or pagination, but the essential information for invocation and interpretation is present. This is appropriate for the tool's 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 coverage is 100%, so the baseline is 3. The description adds value by explaining the purpose of each parameter (conditions, interventions, sponsors, terms, nct_ids) and providing details not in the schema, such as the default max_studies being 50 and the exact list of default fields returned. This goes beyond the schema, warranting a 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?

The description clearly states 'Search clinical trials using multiple criteria', which specifies the verb (search), resource (clinical trials), and scope (multiple criteria). The name 'search_trials_combined' and the list of sibling tools like search_trials_by_condition and search_trials_by_intervention make it evident that this tool is for combined multi-criteria searches, distinguishing it from single-criterion siblings.

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 that this tool allows combined search with multiple criteria, which implies it should be used when more than one search dimension is needed. It does not explicitly mention alternatives or when not to use it, but the 'combined' wording and sibling context provide clear guidance. The absence of exclusions keeps it from a 5.

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 discloses that the tool is 'lightweight' and returns 'only NCT IDs and minimal metadata,' which is meaningful behavioral information. It also includes the default max_studies. However, it does not clarify what 'minimal metadata' includes, whether searches across parameter types are AND/OR, or any rate limits or pagination behavior, leaving gaps in 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 opens with two sentences that are almost redundant: 'Lightweight search returning only NCT IDs and minimal metadata for discovery.' and 'This tool performs a lightweight search that returns only the NCT IDs and minimal metadata for the purpose of discovering relevant trials.' This repetition wastes a sentence. The parameter list is clear, but the redundancy reduces the 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?

There is no output schema, so the description should clarify the response structure. It says 'minimal metadata' but does not enumerate the fields. Additionally, it does not explain how multiple search filters (conditions, interventions, sponsors, terms) interact, which is critical for a search tool. Given these omissions and vague output description, the tool is not fully contextualized.

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

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

Schema description coverage is 100%, so the schema already documents all five parameters. The description lists the same parameter descriptions without adding new meaning (e.g., it repeats 'A list of medical conditions to search for'). The only slight addition is mentioning 'Defaults to 100' for max_studies, which is also in the schema. Since the schema carries the semantics, a 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 the tool performs a 'lightweight search returning only NCT IDs and minimal metadata for discovery.' This is a specific verb+resource+scope. It does not explicitly distinguish from sibling tools like search_trials_combined, but the name 'nct_ids_only' and the repeated emphasis on 'lightweight' imply a focused search tool. A slightly higher score would require explicit naming of alternatives.

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 'for the purpose of discovering relevant trials,' implying it is best for early exploration rather than deep detail. However, it does not explicitly state when to avoid this tool or mention alternative tools for full trial data (e.g., search_trials_by_nct_ids or search_trials_combined). This is an implied usage context, not a clear exclusion.

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