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

No annotations are provided, so the description carries the full burden. It discloses the exact output contents (columns, numeric flags, row count, provenance banner) and that it is a schema-inspection tool. It does not mention side effects, but as a read-only metadata tool, this is sufficient transparency.

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

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

The description is a single sentence, front-loaded with the key output details and the primary usage instruction. No wasted words; every clause earns its place.

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

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

For a zero-parameter metadata inspection tool with no output schema, the description fully covers what the agent needs to know: what it returns and when to call it. It is complete and self-sufficient.

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

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

The tool has zero parameters and the schema covers 100% (empty properties). The description adds value by explaining what the tool returns, which is more than the schema alone. With 0 params, the baseline is 4, and the description does not need to explain parameters.

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

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

The description clearly states the tool returns columns, numeric flags, row count, and provenance banner for a specific dataset (Taxyearvo), and explicitly says to call it first to learn the schema. This distinguishes it from siblings like dataset_stats or dataset_top.

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 usage context: 'Call this first to learn the schema.' It implies this is the initial step before using other tools, but does not explicitly name alternatives or exclusion criteria. Still, the guidance is direct and actionable.

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 carry the behavioral burden. It usefully discloses that rows follow the order of the given values and match when the column equals any value. However, it does not clarify whether all matching rows are returned or only one per value, nor does it describe matching exactness or output 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?

A single sentence front-loads the selection semantics and ends with a concise usage hint. There is no filler or repetition of schema details.

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

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

For a simple two-parameter tool with no output schema or annotations, the description gives the core selection criterion and ordering. The main gap is ambiguity around cardinality—whether all matching rows per value are returned or a single row per value—which matters for an X-vs-Y comparison workflow.

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 effectively explains that 'column' is the dataset column to filter on and 'values' are the allowed values, and it adds the non-obvious ordering semantics: returned rows follow the order of the given values. It does not restate the minItems/maxItems constraints, but those are already in 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 identifies the exact resource (rows of the Taxyearvo dataset), the filter criterion (column matches one of the given values), and the ordering behavior. Combined with the title 'Compare rows side by side', the purpose is clear, though it does not explicitly distinguish itself from dataset_row 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 phrase 'for "X vs Y" questions' provides clear context for when to use this tool: comparing specific values side by side. It does not mention alternatives or exclusions, so it falls short of a 5.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It enumerates the returned fields (source, date, licence, citation) and states the intended use, which is sufficient for a simple read-only tool. It does not discuss performance, authentication, or side effects, but none are expected.

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 with no filler. It front-loads the key content (source, date, licence, citation) and immediately states the usage case. Every word earns its place.

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

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

For a zero-parameter tool with no output schema, the description adequately specifies what the tool returns and why it is used. It does not detail the return format, but the enumerated fields imply the structure. This is complete enough for an agent to 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 schema description coverage is trivially 100%. The description correctly adds no parameter-specific details because none exist. Baseline for 0 parameters is 4, and the description does not conflict or add unnecessary information.

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

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

The description clearly states the tool provides the source, computation date, licence, and citation for the Taxyearvo dataset. It is specific about the resource and content, and while it does not explicitly name sibling tools, the purpose is distinct from the sibling tools that handle columns, rows, search, stats, etc.

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 explicit guidance: 'Read this to attribute a figure correctly.' This tells the agent when to use it (when attribution is needed) and implies it is not for other data operations. It does not list alternatives, but for a provenance tool the usage context is clear.

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 and does disclose a meaningful behavioral trait: case-insensitive equality. However, it leaves ambiguity about whether all matching rows are returned or just one (description says 'rows', title says 'a row'), and says nothing about no-match or multiple-match 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?

One sentence, front-loaded with the dataset and operation, with zero filler. The title reinforces the purpose without redundancy.

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

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

For a simple two-parameter lookup with no annotations or output schema, the description covers the core behavior but omits whether the tool returns one row or all matching rows and does not explicitly route to the search sibling. Minor gaps remain.

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. 'Where a column equals a value' clearly maps the two required parameters to their roles and adds case-insensitive semantics. It doesn't give format constraints, but the schema already supplies string 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 states a specific resource (rows of the Taxyearvo dataset) and a clear operation (exact column=value lookup), with the added qualifier 'exactly' and 'case-insensitive' distinguishing it from a fuzzy search sibling. It does not explicitly name a sibling, 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?

Usage context is implied by the exact-match qualifier and the sibling name dataset_search, but there is no explicit when-to-use/when-not-to-use statement or named alternative. An agent must infer that non-exact searches belong elsewhere.

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

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

The description discloses two behaviors beyond the schema: case-insensitive matching and a result cap of 50. However, with no annotations, it does not mention ordering, default limit when omitted, empty-result behavior, or output format, leaving the operational profile partly unspecified.

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 16-word sentence that front-loads the dataset and action, with no filler. Every phrase adds operational detail (case-insensitive, any cell, 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?

The description communicates the core contract (match criterion, case handling, result cap) and states that rows are returned, which is sufficient given the tool's simplicity. But with no output schema and no annotations, it omits default-limit behavior and row shape; these are notable but non-critical gaps for a simple search.

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 covers 50% of parameters: query has a description, limit does not. The description adds meaning by stating 'up to 50', clarifying that limit bounds the number of returned rows, and reinforces query semantics with 'any cell'. This compensates for the undocumented limit 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?

Description states a specific search operation: returning rows from the Taxyearvo dataset where any cell contains the query, case-insensitively, capped at 50. This clearly separates it from sibling tools like dataset_row (single row), dataset_columns (column list), or dataset_stats (aggregates).

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 when-to-use or when-not-to-use guidance is provided, and no sibling alternatives are named. The intended use is only implied by the phrasing 'Rows ... whose cells contain the query,' which suggests full-text search, but the agent is left to infer when this is appropriate versus dataset_top or dataset_row.

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

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

With no annotations, the description carries the full burden. It discloses that grouping commas and currency are handled, and non-numeric rows are excluded and counted. This is valuable behavioral context beyond the bare operation. It doesn't cover error conditions or output format, but the disclosed handling is significant.

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, information-dense sentence. The list of statistics is front-loaded, and the parenthetical adds processing details. It's efficient with no fluff, though the parenthetical makes it slightly longer than necessary.

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

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

Given one parameter, no output schema, and no annotations, the description covers the main operation, data handling, and dataset reference. It doesn't describe the return format explicitly, but listing the statistics implies the output structure. It's largely complete for an agent 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 0%, so the description must compensate. It indicates the parameter is a numeric column of the Taxyearvo dataset, giving the agent context that it's a column name. Though it doesn't explicitly say 'the name of a numeric column', the phrasing 'numeric column of the Taxyearvo dataset' conveys the required semantic.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. It distinguishes from siblings by specifying the exact output (stats) and the dataset name, making it obvious it's not about column listing, search, or row 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 when to use it (for numeric column statistics) but does not explicitly mention alternatives or exclusions. While the sibling names give context, the description itself doesn't state when to prefer this over dataset_top or dataset_search. The usage guidance is implied but not 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 provided, the description carries the full disclosure burden, but it only conveys the core ordering behavior (highest or lowest by numeric column). It does not address tie handling, null values, behavior for non-numeric columns, or whether full rows or single values are returned. The 'ascending' default is only visible in the schema, not the description.

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

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

The description is compact and the core operation is front-loaded. However, the sentence is a fragment and the embedded 'Taxyearvo' dataset reference reads as environment-specific hardcoding that may confuse an agent. The quoted use case at the end is a worthwhile addition that earns its place.

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

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

For a tool with no annotations and no output schema, the description omits the return shape (full rows vs. values only) and failure behavior for non-numeric columns. The required parameter is adequately constrained ('numeric column'), so an agent can make a first call, but nothing explains defaults or edge cases. A sentence on return format and ordering defaults would complete it.

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 should compensate. It adds one useful constraint — the column must be numeric — but says nothing about the 'limit' parameter, whose semantics are left to inference from its name and schema bounds. The 'ascending' parameter is already adequately documented by the schema's own description.

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 concrete operation: returning the highest or lowest rows of a dataset by a numeric column, framed as 'which is the most/least X.' This distinguishes it from siblings like dataset_stats (aggregation), dataset_row (single row), and dataset_search (matching). Clarity is dented by the hardcoded 'Taxyearvo' dataset name and a sentence-fragment construction with no main verb.

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 offers no guidance on when to choose this tool over sibling tools such as dataset_row, dataset_stats, or dataset_search. The quoted use case ('which is the most/least X') is essentially a restatement of purpose, not a selection rule. No prerequisites, exclusions, or alternative conditions are given.

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