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

With no annotations provided, the description carries the full burden of disclosing behavior. It only states a retrieval operation (implying read-only) and lists supported identifiers, but doesn't mention authentication requirements, rate limits, response format, or how the 'view' parameter affects output. This leaves the agent with limited insight into the tool's behavior beyond the obvious.

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 action and resource, and includes a concise list of supported identifier types. Every word contributes value, with no redundancy or 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?

The tool is a relatively simple retrieval operation with a rich parameter schema (view, field, id_type). The description covers the core purpose but does not explain return structures (no output schema), potential limitations, or typical use cases. It is adequate but not comprehensive, especially given the lack of annotations.

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 parameters. The description adds a few examples for id_value (DOI and EID) that are replicated in the schema. Since the schema does the heavy lifting, the baseline of 3 is appropriate, and the description provides no extra semantic value beyond a restatement of supported identifier types.

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

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

The description clearly states the tool retrieves detailed metadata and abstract for a specific Scopus document by identifier, with a specific verb ('Retrieve') and resource. However, it doesn't explicitly distinguish from the sibling tool 'article_retrieval', which may have overlapping functionality, so it's clear but not fully differentiated.

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

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

The description implies the use case (when you have a document identifier and need metadata/abstract) but offers no explicit guidance on when to use this tool versus alternatives like article_retrieval, and no exclusions or prerequisites are stated.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that access depends on API key entitlements, which is a useful caveat, but it does not explain response format, error behavior, or the practical difference between view levels beyond 'full-text or metadata.' This is insufficient for a read 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 two sentences, front-loaded with the primary action and resource, and every word earns its place. It avoids redundancy and is highly scannable.

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 core purpose and identifier types, and the schema documents all parameters. However, with no output schema or annotations, the description leaves out details about return values and potential error scenarios, making it adequate but not fully complete for a tool with four parameters and multiple view options.

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 meaningful context by listing the identifier types and implying that full-text access is subject to entitlements, which helps clarify the view parameter's real-world implications. This goes beyond the schema's enum values.

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 'Retrieve' and the resource 'ScienceDirect article', and specifies the identifier types (DOI, PII, EID). It distinguishes itself from siblings by explicitly mentioning ScienceDirect, which contrasts with Scopus-related tools like scopus_search and abstract_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 provides clear context: it is used when you have a DOI, PII, or EID and need full-text or metadata. It does not explicitly mention alternatives or exclusions, but the context is unambiguous. The access entitlement note further guides usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions return metadata and query syntax, but does not disclose pagination behavior, rate limits, authentication needs, or error handling. The read-only nature is implied but not 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 two sentences long, with the first sentence covering purpose and output, and the second providing query syntax guidance. All information is relevant and front-loaded, with no wasted words.

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

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

For a tool with 9 parameters, no annotations, and no output schema, this description is under-specified. It does not cover sorting, filtering, pagination, or how this tool relates to siblings, leaving significant gaps for an agent to infer.

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

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

All 9 parameters are documented in the schema (100% coverage), so baseline is 3. The description adds value by explaining Scopus field codes (TITLE(), AUTH(), AFFIL(), etc.) with examples for the query parameter, which 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 clearly states the tool's function: 'Search the Scopus database of abstracts and citations.' It also lists return metadata (titles, authors, DOIs, citation counts), which distinguishes it from sibling tools like abstract_retrieval or article_retrieval that focus on specific document 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 via 'Search the Scopus database' but does not explicitly state when to use this tool versus alternatives like abstract_retrieval or serial_title_search. No exclusions or alternative mentions are provided, so guidance is implied rather than explicit.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It states the output includes publisher, subject areas, and CiteScore metrics, which gives some transparency about the response content. However, it does not mention error handling, access requirements, or behavior when the ISSN is invalid, leaving some 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 sentence that packs essential information without unnecessary words. It clearly states the resource, the key identifier, and the content of the response, demonstrating excellent 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?

For a simple retrieval tool with two parameters and no output schema, the description provides the core purpose and response contents. However, it lacks explanation of the 'view' parameter's effect on the response detail level, and it does not clarify the distinction from the sibling serial_title_search, leaving some context 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?

The input schema provides descriptions for all parameters (100% coverage), so the baseline is 3. The description adds little beyond what the schema already states; it only reiterates the ISSN as the identifier and mentions CiteScore, which is already represented in the schema and enum.

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 'Retrieve' and clearly identifies the resource: a specific journal or serial publication by its ISSN. It also lists what is included (publisher, subject areas, CiteScore metrics), distinguishing it from the sibling serial_title_search which likely focuses on searching by title.

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 this tool is for when you have an ISSN and need detailed information, providing a clear usage context. However, it does not explicitly mention alternatives or when not to use it, though the sibling names suggest a search tool exists for lookups without an ISSN.

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 states the search capability without mentioning output format, pagination behavior, authentication requirements, or any limitations. This leaves critical behavioral information undisclosed.

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 that is direct and front-loaded with the core action and resource. There is no wasted wording or unnecessary detail.

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 10 parameters, no output schema, and no annotations, the description is too sparse to provide complete context. It does not specify what the response contains, how pagination works, or any constraints on combining filters. For a tool of this complexity, more behavioral and contextual detail would be expected.

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

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

All 10 parameters have complete descriptions in the schema (100% coverage), so the baseline is 3. The description names several filters (title, ISSN, publisher, subject area, open access) but this only summarizes the schema, adding no new semantic meaning beyond what is already structured.

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 journals and serial publications in Scopus, specifying multiple search criteria (title, ISSN, publisher, subject area, open access). This verb+resource construction is distinct from siblings like serial_title_retrieval (likely metadata retrieval for a specific serial) and article 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 implies the tool is for finding serial publications, contrasting with scopus_search which likely searches documents. However, there is no explicit when-to-use guidance or mention of alternatives, leaving the agent to infer appropriate usage from the tool name and description 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?

There are no annotations, so the description carries the full burden. It states the return content ('classification codes, abbreviations, and descriptions') and implies read-only behavior through 'Retrieve.' However, it does not clarify output format, pagination, or any potential restrictions (e.g., whether all classifications are returned by default). The disclosure is adequate but not rich.

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

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

The description is two sentences, front-loaded with the core purpose and immediately followed by a practical use case. Every word earns its place with no filler 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?

For a simple lookup tool with six well-documented parameters, the description provides sufficient context: it states what the tool retrieves, why it is useful, and what it returns. There is no output schema, but the description compensates by naming the output fields. It lacks explicit mention of default behavior or result coverage, but the schema's parameter descriptions fill in most gaps.

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

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

The schema descriptions cover 100% of the six parameters, so the baseline is 3. The tool description adds no additional parameter semantics beyond what the schema already provides, merely echoing 'codes, abbreviations, and descriptions' that correspond to the field options.

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 subject area classifications from Scopus or ScienceDirect, a specific verb-resource pairing that distinguishes it from sibling tools like scopus_search or abstract_retrieval. The phrase 'Retrieve subject area classifications' is unambiguous and the tool is dedicated to this lookup.

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 frames when to use the tool: 'Useful for finding subject codes to filter searches.' This gives clear context for usage without explicitly naming alternatives, but it is distinct enough from siblings that no exclusion is necessary.

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