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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 what the tool returns and implies a read-only schema-inspection call, but it does not explicitly state side effects, permissions, or return formatting. For a parameter-less schema tool this is adequate but not exhaustive.

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: the first lists the returned information, the second gives a crisp usage directive. No wasted words, and the key content 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?

With no parameters and no output schema, the description gives the essential return contents plus a clear usage instruction. It could go further by describing the exact output format, but that is a minor gap for a simple schema-probe 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?

The tool has zero parameters, so no parameter explanations are needed and the baseline of 4 applies. The description correctly avoids inventing parameter guidance where none exists.

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 outputs: columns, numeric columns, row count, and provenance banner of the Reputzo dataset. It clearly positions this as the schema-discovery entry point, though it does not explicitly contrast it with sibling tools such as 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?

'Call this first to learn the schema' gives an explicit, unambiguous invocation context: an agent should use this before other dataset tools. It doesn't spell out when not to use it or name alternatives, but the timing guidance is strong.

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 supplied, the description carries the behavioral disclosure burden. It usefully discloses that matching is 'any of' the given values and that rows follow 'the order given,' which are material behaviors. But it leaves unspecified what the returned report looks like, how duplicates or case sensitivity are handled, and whether all matching rows are always returned.

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 packs in the resource, the filter criterion, the ordering behavior, and the intended use case. There is no filler or repetition of schema details, and the most important scoping behavior 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 two-parameter tool the description is adequate, but with no output schema and no annotations, it omits the return format and edge-case behavior. The intended use case ('X vs Y' questions) is clear, but an agent still has to infer whether 'side by side' means a special rendering or simply an ordered list of rows.

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 explain the parameters itself. It does: 'column' is the field whose values are matched, and 'values' are the candidate values to select rows for, with order preserved. This adds meaningful relational semantics beyond the bare string types in the schema, though it does not annotate each parameter by name.

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 (Reputzo dataset rows) and the selection behavior (rows whose column matches any of the given values, preserving order), and the title adds 'compare rows side by side.' However, it uses a noun phrase rather than a direct verb like 'retrieves' or 'compares,' and it does not explicitly differentiate from sibling tools like 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 trailing phrase '— for "X vs Y" questions' gives a clear usage context, and the matching semantics are specific to comparisons across values. It does not, however, explicitly state when not to use this tool or name alternative sibling tools, so the guidance is clear but not exhaustive.

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 implies a safe read-only operation ('Read this') and lists what is returned. It does not explicitly state that there are no side effects or describe error behavior, but for a simple provenance lookup with zero parameters 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, and the key information (what and why) is front-loaded. Every word earns its place, and the purpose is immediately clear.

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 tool, the description fully covers what an agent needs: what the tool provides, for which dataset, and how to act on it. Nothing essential 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 there is nothing for the description to explain beyond what the schema already shows. The baseline of 4 applies, and the description adds context about what the returned provenance data contains, which is more than expected.

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 (the Reputzo dataset's provenance) and lists the exact attributes it covers: source, computed date, licence, and citation. This clearly distinguishes it from sibling tools that handle columns, comparison, rows, search, stats, or top values.

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 states a concrete use case: 'Read this to attribute a figure correctly.' This tells the agent when to invoke it. It does not explicitly name alternative tools or exclusions, but the clear attribute-focus implies when it is relevant versus when other dataset tools would be better.

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 does disclose one non-obvious behavior: matching is exact but case-insensitive, which is valuable. However, it does not state whether the result is a single row or multiple rows, what the output contains, error behavior, or whether this is explicitly read-only. The title implies a read operation, making this minimally viable.

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 of about 16 words with no filler, repetition, or irrelevant detail. The core operation and the key case-insensitive qualifier are immediately visible. This is appropriately sized and 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?

The tool has two parameters, no annotations, and no output schema, so the description must cover the essentials. It explains the lookup semantics but leaves the return structure ambiguous: 'rows' suggests multiple results, yet the title says 'a row,' and no-match behavior is unaddressed. For a simple lookup tool this is a noticeable but not severe gap.

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 provides only parameter names and minLengths, and description coverage is 0%, so the description must compensate. It does connect the two parameters meaningfully by explaining that a column is compared to a value exactly and case-insensitively. It does not enumerate valid column names or explain result shape, but for two simple string parameters this is adequate.

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 function: returning rows from the Reputzo dataset where a column equals a value exactly. The exact-match and case-insensitive qualifiers distinguish it from broader siblings like dataset_search, and the title supplies the missing 'look up' verb. It is specific but not phrased as a complete imperative sentence.

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 about when to use this tool rather than dataset_search, dataset_top, or other siblings. The exact-match phrasing implies it is for precise key lookups, but no explicit when-to-use or when-not-to-use guidance is provided, leaving selection 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 burden and does disclose useful traits: matching is case-insensitive and the result is limited to 50 rows. However, it does not state whether the operation is read-only, what the output format is, or how results are ordered, leaving part of the behavior implicit.

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 conveys operation, scope, matching semantics, and cap with 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 two-parameter search tool with no annotations or output schema, the description covers the core behavior, matching condition, and result cap. It does not specify output shape or ordering, but this is modest for a simple search 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 50%; the description compensates partially by clarifying that 'query' is matched against any cell and that results are capped at 50, which also clarifies the 'limit' parameter. It does not add detail such as the default limit or the exact meaning of 'rows.'

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 operation implicitly: it returns rows whose cells contain the query in the Reputzo dataset. It is specific about matching behavior (case-insensitive) and a 50-row cap, which distinguishes it from siblings like dataset_row or dataset_top, though it does not name a 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?

There is no explicit when-to-use guidance or mention of alternatives. The description only defines what the tool does; an agent must infer that it should be used for text-based search, rather than for retrieving rows by ID or computing stats.

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 usefully discloses data-cleaning behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. That adds real behavioral context beyond merely 'compute stats', though it doesn't cover return format or error cases.

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 with no redundancy. The output metrics are front-loaded, and the parenthetical data-handling details are compact and valuable.

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 one-parameter tool with no output schema and no annotations, the description covers the essential inputs and expected outputs. It's missing explicit return structure and failure behavior, but the listed metrics give the agent a clear picture of what to expect.

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 adds the key semantic that the column must be numeric and that formatted values are tolerated. However, it does not clarify whether 'column' is a name, index, or other identifier, leaving 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?

Description states a specific operation: computing count, min, max, mean, median, and sum for a numeric column. It names the exact resource (the Reputzo dataset) and the outputs, making it immediately distinguishable from sibling tools like dataset_search or dataset_row without needing to open their definitions.

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 clearly implies when to use the tool (when numeric summary statistics are needed), but it never explicitly contrasts it with alternatives or states when not to use it. No exclusions or sibling routing are provided.

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 present, so the description carries the disclosure burden. It explains the core rank-ordering behavior (highest or lowest by numeric column) but does not disclose the default when limit is omitted, tie-breaking behavior, or how invalid or non-numeric columns are handled.

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 conveys the behavior, scope, and a concrete query example with no filler. Every phrase 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 simple signature this is mostly adequate, but the optional limit has no stated default and there is no output schema describing what the ranked rows look like. An agent could make a valid call but might not know what response or default row count to expect.

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 33%, so the description needs to compensate. It adds the key constraint that the column must be numeric and clarifies highest vs. lowest ordering, but it leaves the limit parameter to be inferred from its name and schema bounds.

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?

Title 'Rank rows by a numeric column' states a specific verb and resource, and the description elaborates on highest/lowest rows and the 'which is the most/least X' use case. It is clear enough to distinguish from sibling tools like dataset_search or dataset_stats, though it does not explicitly name a 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 description gives a clear use-case trigger: ranking rows by a numeric column to answer 'which is the most/least X.' It does not explicitly list exclusions or alternative tools, but the context for when to use this tool is readily apparent.

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