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

With no annotations, the description carries the responsibility of explaining behavior. It clearly discloses what the tool returns (columns, numeric indicators, row count, provenance banner) and implies a read-only introspection role through 'learn the schema.' It does not discuss side effects, but for a zero-parameter schema-info tool this is sufficient.

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, no filler. The content payload is front-loaded, and the actionable guidance 'Call this first' is placed at the end without being obscured.

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 and no output schema, the description fully explains what an agent will get and when to call it. The four return components are explicitly listed, making the tool 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, so the baseline is 4. The description adds context by naming the fixed resource, the Extinvo dataset, even though there is no input schema to clarify.

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 exactly what the tool returns: columns, numeric flags, row count, and provenance banner for the Extinvo dataset. It also gives an explicit instruction to call it first to learn the schema, which clearly distinguishes it from siblings like dataset_stats, dataset_row, and dataset_provenance.

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 clear usage context with 'Call this first to learn the schema,' telling the agent when in the workflow this tool should be invoked. It does not explicitly mention when not to use it or name alternative tools, so it stops 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?

With no annotations, the description carries the full burden of behavioral disclosure. It discloses meaningful behavior: rows are matched when the column equals any of the given values, and results are returned in the order of the values provided. However, it does not mention output format, exact-match semantics, case sensitivity, handling of missing values, or explicitly confirm 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, well-structured sentence with no filler. It front-loads the core behavior, includes the ordering detail, and adds a concise usage hint. Every phrase contributes meaning.

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 query tool, the description covers the core selection logic, ordering, and intended use case. The input schema supplies the remaining constraints such as min and max items. It does not describe the return structure, but the title and 'rows' language make the basic output shape reasonably inferable.

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 for the bare parameter names. It does so by explaining that 'column' is the dataset column to match against and 'values' are the values used for filtering and result ordering. This adds real semantic meaning beyond the schema, though it does not elaborate on edge cases like duplicate values or unmatched entries.

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 returns rows of the Extinvo dataset filtered by a specified column and set of values, in a given order, for comparison questions. It distinguishes itself from siblings like dataset_row and dataset_search through the multi-row 'X vs Y' framing, though it does not name any sibling explicitly.

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 contextual guidance on when to use this tool: when comparing specific rows side by side. It does not explicitly state when not to use it or name alternative tools, but the intended use case is clear enough to route an agent appropriately.

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 burden of explaining behavior. It discloses the exact output content (source, computed date, licence, citation) and the intended use, making the read-only, metadata-only nature clear.

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 sentences, each earning its place: the first states what the tool provides, and the second states when to read it. No redundant or vague 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 zero-parameter tool with no output schema, the description sufficiently lists the returned provenance fields and the reason to call it. The agent can decide to invoke it and understand the result without additional information.

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 is no schema information to elaborate on. The description appropriately focuses on what the tool returns rather than 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 names the exact resource ('the Extinvo dataset') and the specific provenance attributes it returns (source, computed date, licence, citation). This clearly sets it apart from the sibling tools, which focus on data columns, search, stats, and rows rather than provenance.

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 a clear trigger for use: 'Read this to attribute a figure correctly.' It does not explicitly state when not to use it or name alternatives, but the use case is unambiguous.

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 does disclose that matching is exact yet case-insensitive, which is a valuable nuance. However, it does not clarify whether all matching rows are returned or only one, nor what happens on zero matches or invalid 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 sentence with no filler. Key information—resource, matching condition, exactness, and case-insensitivity—is front-loaded and efficiently stated. There is no redundancy with the title that detracts from the description itself.

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, the description is close to sufficient, but the lack of an output schema and annotations leaves gaps. The singular 'a row' in the title conflicts with the plural 'rows' in the description, and there is no clarification of return shape, limits, or no-match behavior.

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 for the two parameters. The phrase 'where a column equals a value' maps column and value to their roles, but adds little beyond the property names. It does not explain column naming, value formatting, or edge cases, so parameter semantics are under-specified.

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 (Extinvo dataset), the action (exact lookup/equality match), and the distinguishing semantics (exactly, case-insensitive). It clearly differentiates from the sibling dataset_search by emphasizing exact rather than fuzzy or partial matching.

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 the tool: when a row must be found by an exact column/value match. However, it does not explicitly state when not to use it or mention sibling alternatives such as dataset_search or dataset_compare. Usage guidance is present only by implication.

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 available, the description carries the behavioral burden. It meaningfully discloses case-insensitive matching, matching against any cell, and the 50-row limit—details not fully present in the schema. It omits result ordering and pagination, but covers the core behavior well for a simple search tool.

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 front-loaded sentence with no redundant wording. It efficiently communicates the dataset, search condition, case-insensitivity, and result cap in minimal space.

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 search tool with no output schema, the description conveys the essential operation. Missing details such as the default limit when the parameter is omitted, result ordering, and the exact shape of returned rows keep it from being fully complete for an autonomous agent.

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 50% because only query has a description. The description indirectly explains limit via 'up to 50' and clarifies that query is searched in cells. However, it does not explicitly define default limit behavior or fully compensate 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?

The description is specific and action-oriented: it explains that the tool returns rows from the Extinvo dataset matching the query in any cell, is case-insensitive, and caps results at 50. This clearly distinguishes it from sibling tools like dataset_row or dataset_stats, which serve different purposes.

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?

There is no guidance on when to use this tool versus the sibling tools. It does not mention alternatives, exclusions, or preferred scenarios. While the name implies content search, the description never tells the agent how it relates to dataset_row, dataset_top, or other 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 present, the description carries the behavioral disclosure burden. It usefully discloses that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. It does not mention error cases or output structure, but the read-only nature of statistics is clear from the content.

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 dense sentence with no filler. It front-loads the computed statistics and adds relevant handling details about commas, currency, and non-numeric rows 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 one-parameter tool, the description covers the core purpose, the target dataset, the list of metrics, and important input quirks. It lacks explicit output format and behavior when the column is missing or entirely non-numeric, but these are minor gaps given the tool's simplicity.

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 clarify the parameter. It does indicate that 'column' refers to a numeric column of the Extinvo dataset, but it does not explain how column names are specified or how to discover valid columns. The single parameter is simple, but the lack of format details leaves some ambiguity.

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 specifies the tool's function: computing count, min, max, mean, median and sum for a numeric column. It names the exact dataset (Extinvo) and distinguishes itself from siblings like dataset_search or dataset_top by focusing on summary statistics for a single numeric column.

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 usage context is implied rather than explicit: an agent can infer this tool is for summarizing a numeric column. However, the description does not state when to use this tool over siblings such as dataset_top or dataset_columns, nor does it provide exclusion criteria or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does convey the core behavior: ranking rows by a numeric column and returning either highest or lowest. However, it does not mention limit defaults, output shape, null handling, or error behavior, leaving notable gaps.

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

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

The description is a single, focused sentence with no filler, and it front-loads the key behavior and use case. It is concise and readable, though the title and description repeat the same basic idea.

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

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

The tool is simple, but with no output schema and no annotations, the description should provide more detail about what the returned rows look like and how the optional limit behaves. The current description is adequate for a basic understanding but incomplete for confident invocation in edge cases.

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%, since only 'ascending' is described. The description adds important semantics by stating that 'column' must be numeric, which goes beyond the schema's 'string' type. However, it adds nothing about 'limit' and relies on the schema for 'ascending', so it only partially compensates for the low coverage.

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 returns the highest or lowest rows of a dataset by a numeric column, with the 'which is the most/least X' framing making the intent unmistakable. This is specific enough to distinguish it from sibling tools like dataset_stats, dataset_search, and dataset_row.

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

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

The description provides clear context for when to use the tool: when answering 'which is the most/least X' questions about a numeric column. It does not explicitly name alternatives or state when not to use it, but the use case is clear enough for an agent to select it appropriately.

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