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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 full burden, and it does well by disclosing what the tool returns: column names, numeric indicators, row count, and provenance banner. It does not explicitly state there are no side effects, but the read-only introspection nature is strongly implied.

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 with no filler. The output contents are listed compactly, and the usage instruction is front-loaded as a direct call to action.

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 no-parameter, schema-introspection tool, the description is complete: it explains what is returned and when to use it. No output schema exists, so the enumerated return items are 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 100% schema coverage, so there is no parameter meaning that needs explanation. The description adds value by explaining what the tool will tell you rather than any parameter details.

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 resource ('Fitouto dataset') and the exact outputs: columns, numeric flags, row count, and provenance banner. It also gives the tool a distinct role among siblings by saying 'Call this first to learn the schema.'

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 usage context: call this tool first to learn the dataset schema. It does not enumerate exclusions or compare against siblings, but for an initial schema-introspection tool, the guidance is 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 provided, the description carries the full burden of behavioral disclosure. It usefully reveals that row ordering follows the order of the provided values, which is a meaningful behavioral detail. It does not mention exact-match semantics, case sensitivity, or the shape of the output, so the agent still has some uncertainty about the tool's 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?

The description is a single, compact sentence that packs in the dataset name, the filter logic, the ordering behavior, and the intended use case. Every part earns its place, with no filler 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 simple two-parameter tool, the description covers the core selection logic and ordering, but it omits output format and does not explain how this relates to the 'side by side' title. It also lacks explicit guidance distinguishing it from dataset_row or dataset_search, which could be important for correct 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 coverage is 0%, so the description must add meaning to both parameters. It effectively explains that 'column' is the field to match and 'values' are the candidate values to filter rows by, and that their order determines result order. This compensates well for the missing schema descriptions, though it does not explicitly name the parameters or their 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's function: retrieving rows from the Fitouto dataset where a specified column matches any of the given values, preserving the given order. It also ties this to 'X vs Y' questions, which differentiates it from broader sibling tools like dataset_search or dataset_stats. However, it lacks an explicit action verb like 'return' or 'select', making the phrasing slightly indirect.

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 use case ('for X vs Y questions'), implying when it should be used. It does not explicitly contrast with sibling tools or state when not to use this tool, leaving some inference needed.

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 explicitly lists what the tool returns (source, computed date, licence, citation) and signals a read-only intent via 'Read this.' This is sufficient for a simple provenance tool, though it does not describe exact output formatting.

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 carry all essential information: what the tool provides and when to use it. The key content list is front-loaded and there is no filler.

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

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

For a zero-parameter, no-output-schema provenance tool, the description is fully adequate. It names the dataset, the exact provenance fields available, and the intended use case, leaving no critical gap for an agent to call 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 there is no parameter semantics burden on the description. The baseline for no parameters is 4, and the description appropriately focuses on the tool's purpose rather than parameter explanation.

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

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

The description clearly states a specific resource ('the Fitouto dataset') and the content it provides: source, computed date, licence, and citation. It is immediately distinguishable from sibling tools like dataset_columns and dataset_row, which concern data structure/content, not 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 an explicit use case: 'Read this to attribute a figure correctly.' This tells the agent when to invoke the tool, though it does not mention alternatives or conditions for not using it. Clear context without exclusions.

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 full behavioral burden. It discloses exact and case-insensitive matching and that multiple rows may be returned, but it does not explicitly state read-only behavior, return format, empty-result handling, or error 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?

The description is a single efficient sentence with no filler. The core matching behavior is front-loaded, and the case-insensitive qualifier adds valuable information 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?

The tool is simple with only two string parameters, and the description conveys the essential lookup semantics. However, the singular title 'a row' conflicts with the plural description 'rows', and there is no output schema or annotation to clarify return shape, multiplicity, or safety.

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 has 0% description coverage, but the description gives both parameters meaning: 'column' is the field to match and 'value' is the target value. It does not explain allowed column names, formatting, or how case-insensitivity applies to the value beyond the match behavior.

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 defines the tool's result: rows from the Fitouto dataset where a specified column exactly equals a provided value, with case-insensitivity. This distinguishes it from sibling search tools, though the description is phrased as a noun phrase rather than an explicit verb+resource statement.

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 exact-match and case-insensitive semantics imply when to use this tool, but it does not explicitly state when to prefer it over dataset_search or other siblings. There are no exclusions or alternative naming, leaving some inference to the agent.

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 behavioral disclosure. It usefully notes case-insensitive matching, cell-level containment, and the 50-row cap. It does not mention return structure, ordering, or behavior when no matches are found, leaving some gaps for a tool with no annotation support.

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

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

A single sentence conveys the core behavior, scope, case sensitivity, and result cap. It is tightly written with no filler, and the most important information 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 search tool with only two parameters and no output schema, the description covers the essential semantics needed to call it correctly. It could mention result format or no-match behavior, but these are less critical given the low complexity and the presence of sibling tools that likely handle distinct operations.

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%, and the description partially compensates by clarifying that query is searched case-insensitively across cells and that results are capped at 50. It does not explicitly explain the limit parameter's role beyond the cap, which is already in the schema maximum, so added value is moderate.

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 Fitouto dataset that contain the query in any cell, with case-insensitive matching. It is specific about the resource and operation, though it does not explicitly differentiate this from sibling tools like dataset_top or dataset_stats.

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 fairly obvious from the name and description: search the dataset for cell values. However, there is no explicit statement about when to use this tool versus alternatives such as dataset_top or dataset_columns, and no exclusions are mentioned.

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 and does well by disclosing key behaviors: grouping commas and currency are normalized, and non-numeric rows are excluded and separately counted. It does not cover return format or error behavior, but for a read-only statistical tool the main behavioral caveats are surfaced.

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 computed metrics and uses a parenthetical to convey edge-case handling. There is no redundant wording; every clause adds information relevant to correct invocation.

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 required parameter, and no nested schemas, the description is largely complete. It explains what is computed and how problematic rows are handled. It stops short of describing the exact output shape, but since no output schema exists, a brief note about return values would have made it 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?

The schema only defines a single string parameter named 'column' with 0% description coverage, so the description must compensate. It does by clarifying that the parameter refers to a numeric column and noting how formatting and non-numeric values are treated. It could be more explicit about exact column-name formatting or case sensitivity, but the core meaning is clear.

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 operation: computing count, min, max, mean, median, and sum for a numeric column of the Fitouto dataset. It distinguishes itself from siblings like dataset_columns, dataset_search, and dataset_top by focusing on numeric summary statistics, though it does not explicitly name an alternative.

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 for numeric columns and states that formatting is handled, which gives some contextual guidance. However, it does not explicitly explain when to prefer this over sibling tools like dataset_compare or dataset_search, nor does it list exclusions or prerequisites.

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 the core ranking behavior and the ability to get highest or lowest rows, but does not mention limit defaults, tie handling, behavior on non-numeric columns, or the exact shape of returned 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 compact and front-loaded: it states the action and resource immediately, then adds a brief, useful usage example. No wasted words or redundant restatements of the tool name.

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 there is no output schema and no annotations, so the description needs to explain return values and edge behavior. It says 'rows' are returned, which helps, but it omits the default limit and what happens with invalid or non-numeric input, leaving meaningful gaps.

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 low at 33%, and the description partially compensates by clarifying that the column should be numeric and that 'lowest' maps to ascending order. However, the 'limit' parameter is not explained at all, and the description does not fully document how the parameters combine.

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 tool returns the highest or lowest rows of the Fitouto dataset based on a numeric column, with the natural-language question 'which is the most/least X.' This clearly conveys the core purpose and mostly distinguishes it from siblings like dataset_row or dataset_stats, though it does not explicitly name alternatives.

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 you want the rows with the most or least values of a numeric column. However, it provides no explicit guidance about when not to use it or which sibling tools might be better for related tasks.

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