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

With no annotations provided, the description carries the burden of disclosing side effects, but it does not explicitly state whether the tool is read-only or whether it has any permissions or side effects. The content strongly implies a safe schema-inspection operation, but explicit transparency is absent.

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, focused sentence that lists the exact outputs and the recommended usage. There is no redundant or irrelevant information.

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

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

Since there is no output schema, the description adequately explains the expected return contents: columns, numeric flags, row count, and provenance banner. It also provides the broader context of learning the schema, making the tool's role clear.

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, so there are no parameter semantics to explain. The description focuses on what the tool returns, which is appropriate given the empty input 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 returns the columns, numeric flags, row count, and provenance banner for the HostingByStack dataset. It also explicitly frames the tool as the first step to learn the schema, making its purpose unmistakable.

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 direct usage guidance by saying 'Call this first to learn the schema,' which tells the agent when to use this tool in a workflow. It does not explicitly contrast it with sibling tools, but the 'first' instruction provides clear placement.

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 burden. It discloses the selection criteria ('whose column is any of the given values') and ordering ('in the order given'), but it does not mention what happens when a value has no matching row, how output is structured, or whether the comparison is truly displayed side-by-side.

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 sentence conveys the core behavior, the dataset, the filtering logic, the output ordering, and the intended use case. 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?

The description covers the essential invocation details—dataset, column, values, and order—but with no output schema and no annotations, it does not specify the result format or edge-case handling (e.g., missing values, duplicates). This leaves moderate ambiguity for an agent deciding whether the output will be truly comparable side-by-side.

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%, so the description must explain the parameters. 'Column' is clearly the field to match against, and 'values' are the list of values to include, with the output order tied to their given order. This adds functional meaning beyond the bare schema 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 states a specific operation: it returns rows from the HostingByStack dataset matching any of the given values, in the order provided. The phrase 'for X vs Y questions' clearly distinguishes this from siblings like dataset_row or dataset_top, which serve different query patterns.

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 a clear context for use: 'for "X vs Y" questions,' implying a side-by-side comparison of specific values. It does not explicitly name alternatives or state when not to use the tool, but the intended scenario is 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?

With no annotations, the description carries the burden of explaining behavior. It implies a read-only informational lookup by describing the provenance fields and the attribution purpose, but it does not explicitly state that the tool has no side effects or that it never modifies 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 description is brief and to the point, consisting of two short sentences. It contains no redundant wording and immediately conveys the tool's purpose and the data it returns.

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 sufficiently explains what information is returned (source, date, licence, citation) and why it matters (attribution). It does not describe the output format, but there is no output schema and the content focus is clear enough for an agent to use 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 tool accepts zero parameters, which corresponds to the baseline score of 4 for parameter semantics. The description does not need to explain parameters because there are none.

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 that the tool provides provenance metadata — source, computed date, licence, and citation — for the HostingByStack dataset, and the title reinforces this purpose. It is easily distinguishable from sibling tools that handle columns, search, stats, or submissions.

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 'Read this to attribute a figure correctly' gives clear contextual guidance on when to use the tool. It does not explicitly name alternative tools or state when not to use it, but the intended use case is still clear enough.

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 behavioral disclosure burden, yet it only states the matching rule. It does not clarify whether one row or all matching rows are returned, what happens when nothing matches, what the response shape is, or whether the operation is read-only.

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 filler, and the essential matching condition is stated up front. The title reinforces the purpose without adding 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 two-parameter lookup, the core idea is present, but the description omits return-cardinality, error behavior, and any pointer to dataset_columns or dataset_search for supporting context. It is minimally viable but not fully complete for an agent operating without additional tool descriptions.

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, and the description does map 'column' and 'value' to the equality condition while adding case-insensitivity. However, it does not specify valid column names, whether column matching is also case-insensitive, or whether multiple matching rows are allowed.

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 title supplies the action ('Look a row up') and the description names the exact resource ('HostingByStack dataset') plus the matching condition ('a column equals a value exactly'). It does not explicitly compare itself to the sibling dataset_search, but the emphasis on exact match gives reasonable 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 explicit guidance is given about when to choose this tool instead of a sibling such as dataset_search. The description only implies exact-key lookup, leaving the agent to infer exclusionary cases like fuzzy or partial matching.

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 (e.g., readOnlyHint), the description must carry the burden of disclosing side effects or safety. It does not explicitly state that the tool is read-only or that it performs no modifications. While the wording implies a query operation, the lack of an explicit statement about side effects or permissions leaves transparency incomplete.

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

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

The description is a single, compact sentence that conveys the core functionality without any unnecessary verbosity. It is well-structured and immediately understandable, making it an excellent example 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?

For a simple search tool with no output schema, the description covers the essential behavior: it explains the search scope (all cells), the case-insensitive nature, and the result limit. It does not mention default behavior when 'limit' is omitted or the order of results, but these are minor gaps given the tool's low complexity and the absence of 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 'query' parameter is described as 'text to look for in any cell', which clarifies its purpose. The 'limit' parameter has no description in the schema, but the tool description's 'up to 50' provides indirect context that suggests it controls the maximum number of rows returned. Since schema description coverage is 50% and the description partially compensates, the score is average.

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 returns rows from the HostingByStack dataset that match a case-insensitive query, with a maximum of 50 results. However, it uses a noun phrase ('Rows of...') rather than an explicit verb like 'Search' or 'Retrieve', which slightly reduces clarity. It effectively distinguishes itself from sibling tools like dataset_row or dataset_top by describing the filtering behavior.

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 its siblings (e.g., dataset_top, dataset_row, or dataset_compare). It does not mention whether it is preferred for full-text search across all cells or when to use a different tool. Users must infer the appropriate use case solely from the tool's name and the description's content.

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 does disclose meaningful behaviors: handling of grouping commas and currency, and exclusion/counting of non-numeric rows. However, it does not mention whether the tool is read-only, how errors are handled (e.g., missing column), or the exact format of the response. While the data-cleaning behavior is useful, the description is not fully transparent about all 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 a single, compact sentence that front-loads the core purpose (the list of statistics) and then adds two important behavioral notes (handling of commas/currency and exclusion of non-numeric rows). There is no wasted wording, and every part of the sentence contributes value. It is appropriately sized for a simple one-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?

Given the tool's simplicity (one parameter, no output schema), the description covers the main operation and data-handling nuances. However, it omits the output format (e.g., whether it returns an object with keys like count, min, max, etc.) and does not address error conditions or edge cases (e.g., what if the column does not exist). Since there is no output schema, the description should have specified the return shape 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?

Schema description coverage is 0%, so the description must compensate. It implicitly refers to a 'numeric column' of the dataset, which likely corresponds to the 'column' parameter, but it does not explicitly state that the parameter is the column name or that it must be a column from the HostingByStack dataset. The description adds some meaning beyond the bare schema (which only has a minLength constraint), but it leaves the parameter's exact role and constraints somewhat implicit.

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: it computes count, min, max, mean, median, and sum for a numeric column of a specific dataset (HostingByStack). This is a specific verb+resource combination that distinguishes it from sibling tools like dataset_columns (listing columns) or dataset_row (fetching rows). The metrics are enumerated explicitly, leaving no ambiguity about what the tool returns.

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

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

The description implies the tool is used when summary statistics on a numeric column are needed, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or when not to use it. There is no reference to sibling tools or conditions that would route an agent to a different tool. The usage context is implied rather than 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, the description carries the full burden of explaining behavior. It correctly conveys that rows are sorted ascending or descending by a numeric column and that the output consists of the highest or lowest rows. However, it does not mention default limits, tie handling, invalid column behavior, or the shape of the returned result.

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 short and front-loads the core behavior: ranking rows of a specific dataset by a numeric column. The clarifying phrase "which is the most/least X" is slightly redundant with the main sentence but still helps convey user intent without adding meaningful bulk.

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 read-only ranking tool, the core behavior and parameters are mostly covered, and the schema documents constraints on limit and ascending. Still, there is no output schema and no description of the return format, default limit, or error behavior, leaving some ambiguity for an agent invoking 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 description coverage is only 33%, so the description must compensate. It does add meaningful semantics by clarifying that the column must be numeric and that ascending controls lowest-first ordering. However, the limit parameter is left undocumented in both the description and the schema apart from its numeric constraints.

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 ranks rows of the HostingByStack dataset by a numeric column, using terms like "highest" and "lowest" that match the title. It does not explicitly name sibling alternatives, but the purpose is specific enough to avoid obvious confusion with search, stats, or row-lookup 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 gives no guidance on when to use this tool versus alternatives such as dataset_row, dataset_search, or dataset_stats. There is no mention of when ranking is preferred over filtering or aggregation, so an agent must infer usage 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?

Despite having no annotations, the description transparently explains what the tool does not do ('Nothing is bought, ordered or paid; no quote is guaranteed; it is free') and what it returns (who receives details, consent wording, confirmation method). This fully clarifies the tool's behavior and avoids misconceptions.

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, consisting of three sentences that each serve a purpose: the directive, the core function, and the clarifications. It avoids unnecessary details and efficiently communicates the essential information.

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

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

The description provides sufficient context for an agent to understand when and why to use this tool. It explains the tool's role in relation to submit_enquiry, what it returns, and key limitations, making it complete despite lacking 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 input schema is empty (0 parameters), so there are no parameters to describe. The description adds no parameter information, but since 0 parameters have a baseline score of 4, no additional explanation is required.

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 that the tool 'states plainly what submit_enquiry does on HostingStack', specifying the action (stating), the resource (submit_enquiry), and the context (HostingStack). It also explicitly clarifies what it is not (not a purchase, not a guaranteed quote), 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 phrase 'Read first' provides an explicit usage directive, suggesting this tool should be used as a preliminary step. It also describes what the tool returns, giving context on when it is appropriate. However, it does not explicitly compare against sibling tools like 'enquiry_fields', so it lacks direct alternative-based 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, the description carries the burden of behavioral disclosure. It clearly states what the tool returns (the fields with their properties), which is the key behavioral output. It does not explicitly mention that it is read-only or side-effect-free, but given the nature of the tool (listing fields) and the zero-parameter schema, this is implicitly safe. The description adds context about the output structure beyond what the empty schema provides.

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 enumerates the content, the second provides usage guidance. It is concise, front-loaded with the essential information, and every sentence adds value. 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?

For a tool with no parameters, no output schema, and a straightforward purpose (listing fields), the description is complete. It covers what the tool returns, the attributes included, and how to apply the results. The sibling tools are distinct, and nothing essential is missing for an agent to call this 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 tool has zero parameters, so the schema already covers everything (100% coverage). The baseline for 0 params is 4, and the description does not need to explain parameters. It does mention 'Pass answers to submit_enquiry keyed by field key,' which relates to a sibling tool rather than this tool's parameters, so it does not add parameter semantics beyond the baseline.

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

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

The description clearly states the tool's purpose: it lists every field of the HostingByStack enquiry, enumerating specific attributes (key, label, type, required, help text, options). This is a specific verb+resource (returns fields) and distinguishes it from siblings like submit_enquiry (which submits answers) and enquiry_describe (which likely describes the enquiry as a whole). It is not a tautology and provides concrete information.

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 a clear usage directive: 'Pass answers to submit_enquiry keyed by field key.' This tells the agent how to use the output, which is a practical guideline. It does not explicitly state when not to use this tool or mention alternatives, but the purpose is self-evident for retrieving field structure. The instruction adds value beyond a generic description.

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 does so thoroughly: it discloses validation, the summary/consent line/token returned in step 1, the side effect of step 2 (submission plus an email with a click-before-seen link), and the exact consent wording. This goes well beyond the schema.

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?

Although long, the description is dense and purposeful. It front-loads the core action and caveat, then organizes the two steps explicitly. The quoted consent text is necessary and not padding.

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-step side-effectful tool with no output schema and no annotations, the description covers what the agent needs: when to call, what each call returns, what triggers submission, and the consent requirement. It is complete enough to 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful dynamic context: answers must be keyed by field key from enquiry_fields, the same answers must be reused in both calls, and confirmation must be the token obtained after the person approves the summary.

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

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

The description names the exact verb and resource ('submits an enquiry to HostingByStack'), explicitly says what it is NOT ('NOT a purchase, NOT a guaranteed quote'), and references enquiry_fields, separating it from siblings like enquiry_describe and enquiry_fields.

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 provides a clear two-step usage protocol: call once to validate and get a token, then call again only after the person agrees. It also includes a firm when-not signal by stating this is not a purchase or guaranteed quote. However, it does not name a specific alternative tool for purchases, so it stops short of a full when/alternatives guide.

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