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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 explaining behavior. It does so by revealing the exact information returned, including the non-obvious 'provenance banner.' It does not explicitly state that the call is read-only, but the schema-learning framing strongly implies a non-mutating query.

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 only two sentences long and front-loads the output contents before giving the usage directive. Every clause earns its place; there is no filler or repetition of schema data.

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 zero-parameter schema-inspection tool with no output schema, the description fully covers what to expect and when to call it. Nothing needed for a correct invocation is 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 tool has zero parameters, so the schema needs no explanation. The description does not need to elaborate on input semantics, and the baseline of 4 applies because there are no parameter documentation gaps to compensate for.

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 enumerates what the tool returns: columns, numeric flags, row count, and provenance banner of the Binstockly dataset. It also adds the useful purpose 'learn the schema,' which helps distinguish it from sibling tools even though it does not explicitly name any sibling.

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 instruction 'Call this first to learn the schema' gives explicit placement in the workflow. It does not state when not to use it or name alternatives, but for a schema-introspection tool with zero parameters, the timing guidance 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It usefully reveals that rows are filtered by 'any of the given values' and returned 'in the order given', but it says nothing about read-only status, output shape, missing-value behavior, or limits beyond what the schema encodes.

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 efficient sentence that fronts the core selection semantics and then names the intended use case. There is no filler, redundancy, or unnecessary background.

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 tool this is minimally viable, but with no annotations and no output schema, the description leaves ambiguity about what the agent will receive and how edge cases behave. The title hints at side-by-side comparison, but the description itself does not fully specify the return presentation or failure semantics.

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 clarify the parameters itself. It does this by explaining that 'column' is the field to match and 'values' are the accepted values, including the significance of their order. It could add more detail about value formatting or case sensitivity, but the core semantics are present.

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 identifies the resource (Binstockly dataset) and the operation as retrieving rows whose column matches any of the given values, preserving the provided order. The 'X vs Y' framing and title 'Compare rows side by side' distinguish it from sibling tools like dataset_row or dataset_search. It relies somewhat on the title for the verb, 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 phrase 'for "X vs Y" questions' gives a clear intended use context. However, it does not name alternative sibling tools or state when not to use this tool, so an agent must infer routing from the sibling list rather than being explicitly directed.

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 behavior disclosure. It explains the tool is read-only in effect ('Read this'), and enumerates the exact content it returns. It does not describe formatting, but for a zero-parameter metadata lookup this is not a significant gap.

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

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

Two short sentences, both purposeful: the first lists the returned data, the second states the usage scenario. No filler 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?

For a simple metadata lookup with no parameters and no output schema, the description adequately covers what the tool provides (source, date, licence, citation), why to use it (attribute a figure), and is complete enough for an agent to select and invoke it 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 parameter semantics are vacuous. The baseline of 4 applies, and the description properly names the dataset without inventing unnecessary parameter guidance.

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 specific resource (Binstockly dataset) and the specific information returned: source, computation date, licence, and citation. It also states the intended use ('to attribute a figure correctly'), making it clearly distinct from sibling tools like dataset_columns or dataset_search.

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 gives clear context for when to use the tool: when you need to attribute a figure correctly. It does not explicitly name exclusions or alternatives, but none are natural for a provenance lookup, and the instruction to read it for attribution is sufficient 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. It does disclose a genuinely useful behavioral trait — matching is exact and case-insensitive, and returns rows (plural). However, it does not state return format, behavior on no matches, error conditions, or whether case-insensitivity also applies to column names.

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 15-word sentence with no filler, and the title adds clarity. Slight grammatical awkwardness ('The rows... where...' as a noun phrase) and the singular/plural inconsistency between the title ('a row') and description ('rows') keep it from a 5.

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-string-parameter lookup tool with no output schema, the description covers the core retrieval semantics. However, it leaves ambiguity about whether one row or all matching rows are returned, and omits any statement about empty results or failure behavior, which an agent would need 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?

Schema description coverage is 0%: the parameters only have type and minLength, so the description must compensate. It maps column and value to the predicate 'a column equals a value,' which establishes their roles, but it provides no format guidance (e.g., valid column name syntax, whether value should be quoted, or whether case-insensitivity applies to both).

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 resource (rows of the Binstockly dataset) and the matching semantics (column equals a value exactly, case-insensitive), so an agent can tell it looks up rows by an exact key. It lacks a leading verb and does not explicitly differentiate from siblings like dataset_columns or dataset_compare, but the purpose is clear enough.

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 on when to use this tool versus the sibling tools (dataset_columns, dataset_compare, dataset_provenance). There are no exclusions, alternatives, or conditions stated, so the agent must infer usage entirely from the tool name and title.

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 burden. It discloses case-insensitive matching and a 50-result cap, which is useful, but it does not explicitly confirm the operation is read-only or describe ordering, pagination, or empty-result 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?

A single, front-loaded sentence with no filler. Every clause adds meaningful information: rows, dataset, cell matching, case-insensitivity, and result cap.

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 search tool with no output schema, the description covers the essential invocation details: what to search, how matches are determined, and the maximum result count. Minor gaps remain around return row shape and ordering, but these are unlikely to prevent correct 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?

Schema coverage is only 50%, but the description partially compensates: the query is clearly the search text and the 50-row statement explains the intent of the limit parameter. It also adds the case-insensitive detail, which enhances query semantics 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 states a precise behavior: return rows of the Binstockly dataset where any cell contains the query. It adds case-insensitivity and a 50-row cap, going well beyond the title and clearly distinguishing this from siblings like dataset_stats or dataset_columns.

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 to find rows by arbitrary text across any cell. However, it gives no explicit guidance about when not to use it or which sibling tool to choose instead, leaving the decision to inference.

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 well by explaining that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This adds real context beyond the schema, though it omits details about error cases or return shape.

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 entire description is a single sentence that lists the computed statistics and the key data-handling behaviors. Every clause earns its place, and the main purpose 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 one-parameter statistics tool, the description covers input selection, preprocessing behavior, and the metrics returned. It could be more explicit about the exact return format, but the listed statistics make the expected output fairly 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?

Schema coverage is 0%, so the description must compensate. It narrows the 'column' parameter to a numeric column and mentions preprocessing behaviors, which helps. However, it does not specify whether the value is a column name, label, or index, or how column names map to the dataset.

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 computing count, min, max, mean, median, and sum for a numeric column of the Binstockly dataset. This is specific and actionable, though it does not explicitly contrast itself with sibling tools such as dataset_top or dataset_search.

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 intended use is reasonably implied: call this when summary statistics for a numeric column are needed. The description also clarifies how messy numeric input is handled. However, it does not state when not to use it or name alternatives among the siblings.

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 clearly states the core behavior (returning top rows by a numeric column), but it does not mention default limit, default ordering direction, null/non-numeric handling, or explicitly confirm the operation is read-only. No contradiction with annotations 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 a single sentence with an illustrative quote, containing no redundant words. The key details—ranking by a numeric column, highest/lowest behavior, and the intended question—are front-loaded and each phrase adds value.

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

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

For a simple 3-parameter tool without an output schema, the description conveys the high-level purpose and return concept ('rows'), but it omits important context such as default limit, output shape, and how to choose among sibling tools. It is adequate for basic understanding but not fully complete for an agent anticipating edge cases or 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?

Schema description coverage is only 33% (ascending has a description). The description adds meaning by indicating the column must be numeric and linking 'lowest first' to the ascending flag, but it says nothing about the limit parameter. Since schema coverage is low, the description partially compensates but leaves limit semantics 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 title 'Rank rows by a numeric column' states a specific verb and resource, and the description clarifies it returns the highest or lowest rows for a numeric column, with a clear use-case example ('which is the most/least X'). However, it does not explicitly distinguish itself from sibling tools like dataset_search or dataset_stats, 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 when to use this tool via the phrase 'which is the most/least X' and specifies that the ordering column must be numeric. Yet it provides no explicit guidance on when not to use it or which sibling tool to choose instead, leaving the routing decision to inference.

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