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

No annotations are provided, so the description carries the burden of disclosing behavior. It states the operation is a fetch, implying read-only, but does not elaborate on potential errors (e.g., invalid DOI), network dependencies, or whether the DOI should be URL-encoded. The behavior is straightforward, but the description adds no context beyond the basic action, earning 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 two lines, front-loaded with the action and scope, and includes an example for the parameter. Every word earns its place; there is no wasted text. It is a model of 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 (one parameter, no annotations, no nested objects) and the presence of an output schema, the description is complete enough. It tells the agent exactly what the tool does and provides an example DOI, leaving no significant gaps for invocation. The 'full metadata' phrase sets appropriate expectations for the return value, and the output schema covers 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?

The only parameter 'doi' has zero schema description coverage, but the description provides an example ('10.1037/0003-066x.59.1.29'), which adds format guidance and clarifies expectations. This efficiently compensates for the missing schema detail, though it does not fully explain DOI formatting rules or validation.

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 'Fetch full CrossRef metadata for a single work by its DOI' – a specific verb ('Fetch'), a clear resource ('CrossRef metadata'), and scope ('single work by DOI'). This distinguishes it from sibling tools like search_crossref, which is a search operation, and other get_ tools for different providers.

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 use when you have a DOI for a specific work and need its complete metadata. It does not explicitly mention alternatives or when not to use, but given the sibling search_crossref tool, the context is clear enough for an agent to infer appropriate usage. No exclusion criteria are provided, but none are critical for this simple case.

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 verb 'Fetch' indicates a read-only operation, but no additional behavioral context (e.g., auth requirements, error handling, rate limits) is disclosed. This is adequate for a simple lookup but not richer than the bare minimum.

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: a single sentence defining the action and a short Args block. Every element serves a purpose 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?

For a simple get-by-ID tool, the description covers the purpose, the parameter, and an example. An output schema is present, so return values are documented separately. No further context is necessary for reasonable 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%, but the description fully compensates by explaining the parameter in the Args section: 'The ERIC document identifier, e.g. "EJ1234567" or "ED123456".' It provides clear meaning and examples, adding significant value beyond the schema's minimal type/title.

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 ('Fetch') and a specific resource ('full ERIC metadata') with a clear scope ('a single record by its ERIC ID'). It also gives an example ID format, making it easy to distinguish from sibling search tools like search_eric or crossref getters.

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 is the tool to use when you have a known ERIC ID and need the full metadata for that single record. It does not explicitly name alternatives, but the context (single record vs. search) is clear enough for an agent to decide.

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 discloses key behavior: it returns the best OA copy plus alternatives, and sets is_open_access false when no free version is known. This goes beyond a generic 'find' statement, though it could mention more about potential errors or output format.

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 and concise: purpose, usage, and argument definition in three tight sections. Every sentence earns its place with 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?

For a single-parameter tool with an output schema, the description sufficiently covers the workflow and return behavior. It explains what the tool does, when to use it, and what to expect in the response.

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 only parameter, doi, is described in detail with a concrete example, fully compensating for the absent schema description (0% coverage). This adds real meaning beyond the schema's bare type declaration.

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: 'Find a legal open-access PDF for a DOI via Unpaywall.' This clearly distinguishes it from sibling search/get tools for other 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?

It explicitly advises 'Use this after CrossRef/OpenAlex/ERIC turns up a paper you want to read in full,' giving a clear when-to-use context. It does not name alternatives or exclusions, but the tool is unique among siblings for OA PDF retrieval.

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 explicitly says 'Fetch' indicating a read-only action, and elaborates on accepted ID formats (OpenAlex ID, bare DOI, doi: prefix). This is useful behavioral context beyond the bare schema, though it does not cover error handling or rate limits.

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

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

The description is two sentences: the first states the tool's purpose, the second details the parameter. Every sentence is necessary, and the structure is front-loaded. No wasted words.

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

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

Given a single parameter, a clear purpose, and an existing output schema (which defines return values), the description is complete. It covers the essential what and how without needing extra elaboration.

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

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

The input schema only names 'work_id' with no description, so the schema coverage is 0%. The description compensates fully by explaining the three accepted formats with examples, providing essential meaning for the parameter.

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 'Fetch full OpenAlex metadata for a single work' with a specific verb, resource (OpenAlex), and scope (single work). It distinguishes itself from siblings like search_openalex (searching) and get_crossref_work (Crossref-specific).

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: you use this tool when you need full metadata for a specific OpenAlex work. However, it does not explicitly mention alternatives or when not to use it, such as when to prefer get_crossref_work or search_openalex.

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 accepted ID formats and indicates a read-only fetch, but does not detail behavior on invalid IDs, rate limits, or authentication requirements. The term 'full' is vague about what fields are returned, though an output schema exists.

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 clear one-sentence purpose, followed by a compact args section. Every sentence is necessary and there is no redundancy. It is appropriately concise for a simple single-parameter tool.

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

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

For a simple single-ID lookup, the description covers the essential use case and input format. The presence of an output schema means return values need not be described. It is missing explicit usage comparison, but overall it is complete enough for the tool's low 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?

The input schema only provides the parameter name and type ('string'). The description compensates fully by explaining the structure of paper_id, including examples of prefixed external IDs (DOI, ARXIV, PMID, CorpusID). This is comprehensive and adds all necessary semantic meaning.

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

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

The description clearly states a specific action ('Fetch full Semantic Scholar metadata') on a specific resource ('a single paper'). This distinguishes it from search_semantic_scholar, which presumably searches for papers, and other get_* sibling tools for 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 when you have a paper ID, but does not explicitly state when to use this tool over alternatives or mention any exclusions. It provides acceptance criteria for the paper_id but 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the search action and lists parameters; it does not describe output format, pagination behavior, access constraints, or any side effects. The term 'Search' implies read-only but adds little beyond that.

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: a clear one-sentence purpose followed by an Args list. Every line adds value, with no repetition of schema details or filler. The most important information (what the tool does) 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?

Given the tool has an output schema (per context signals) and the params are well described, the description is adequate for constructing basic calls. However, it lacks usage guidance and behavioral context (e.g., when to prefer this tool, result characteristics). For a moderately complex search tool with no annotations, this is a minimal but not comprehensive 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 schema has 0% coverage for parameter descriptions, but the description compensates fully. It explains every parameter: query (free-text keywords), rows (range 1-100), filter (CrossRef filter string with example), and sort (allowed fields with examples). This adds substantial meaning beyond the raw schema types/defaults.

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 verb and resource: 'Search CrossRef for scholarly works (articles, books, proceedings, etc.)'. This distinguishes it from sibling search tools (e.g., search_eric, search_openalex) by naming the specific database.

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

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

No explicit guidance on when to use this tool vs alternatives. It does not mention when Crossref is preferred over ERIC, Semantic Scholar, or OpenAlex, nor does it state any exclusions or context for choosing this tool.

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

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

With no annotations, the description carries the burden of disclosing behavior. It adds context about Solr-style query syntax (with a concrete example), row limits (1-100), and start-offset pagination. It omits information on authentication, rate limits, or error behavior, but provides meaningful operational detail.

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 brief, front-loaded with the tool's purpose, and uses a compact Args block. Every sentence adds value; 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?

The combination of a clear purpose, parameter semantics, and an output schema covers most of what an agent needs. It does not explicitly relate to sibling tools like get_eric_record for retrieving full records, but the core search behavior is adequately described for its moderate 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?

All three parameters receive semantic explanations beyond the schema: query is described as a free-text Solr-style search with an example, rows is given a numeric range, and start is defined as a pagination offset. Schema coverage is 0%, so this description-level coverage is essential and well done.

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 opening verb 'Search' plus the specific resource 'ERIC database (education research literature)' clearly identifies the tool's function. The reference to api.ies.ed.gov and the explicit database name distinguish it from sibling search tools for other scholarly 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 establishes a clear use case: searching ERIC for education research literature, which implies when to select this tool over Crossref, Semantic Scholar, or OpenAlex. It does not explicitly name alternatives or provide exclusion criteria, so it misses the top bar for explicit guidance.

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

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

With no annotations provided, the description must disclose behavioral traits itself. It only states 'Search' and documents parameters, but does not mention behavior such as API key requirements, pagination, rate limits, error handling, or whether results are read-only. The examples imply functionality but do not explicitly disclose side effects or state changes.

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 Args list, each parameter explained concisely and with practical examples. It is slightly lengthy but every sentence contributes value, and the examples aid understanding without unnecessary 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 tool's simplicity (a search with four parameters) and the presence of an output schema, the description covers the essential invocation details. All parameters are documented with examples, and the output schema presumably describes return values. The main gap is the lack of usage context relative to sibling tools, but this is not critical 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?

The schema has zero description coverage, leaving the description as the sole source of parameter meaning. It thoroughly explains each parameter (query, rows, filter, sort) with examples and defaults, going far beyond the bare schema. 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 starts with a clear, specific statement: 'Search OpenAlex for scholarly works across all disciplines.' This identifies the action (search), resource (OpenAlex), and scope (scholarly works), and inherently distinguishes it from sibling search tools like search_crossref and search_eric 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 provides detailed parameter-level guidance (e.g., filter and sort examples) but offers no direction on when to use this tool versus alternative search tools like search_semantic_scholar or search_crossref. There are no explicit when/when-not conditions or named alternatives.

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

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'Search Semantic Scholar for papers' and does not mention result formats, pagination, rate limits, or any limitations. This is a significant gap for a tool with no annotation support.

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 single leading sentence followed by a clear Args list. Every sentence adds value, and the parameter details are directly relevant. There is no waste 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?

Given the tool's simplicity (2 params, output schema exists), the description covers the core purpose and parameters adequately. The output schema handles return value documentation, so the description doesn't need to explain that. However, the lack of behavioral context and usage guidelines slightly hinders full contextual 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?

Schema description coverage is 0%, but the description compensates by explaining both parameters: 'query' as free-text search (title/topic/author keywords) and 'rows' as max results with a 1-100 range. This adds meaning not present in the schema, which only lists types and defaults.

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 Semantic Scholar for papers across all disciplines.' It uses a specific verb+resource pattern and the scope ('across all disciplines') distinguishes it from other search tools in the sibling list, such as search_crossref or search_eric, and from get_semantic_scholar_paper which retrieves a single 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 implies usage for general academic paper searches in Semantic Scholar, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The lack of explicit guidance keeps it at 'implied usage' rather than a 4 or 5.

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