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

With no annotations, the description carries the behavioral disclosure burden. It clearly communicates this is a read-oriented schema discovery call and enumerates the returned information. It does not describe potential latency or side effects, but none are expected for a no-parameter metadata tool, and nothing in the description suggests mutation or hidden 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 two sentences with no filler. It front-loads the concrete outputs in the first sentence and gives the key usage directive in the second. Every word 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 zero-parameter, no-output-schema tool, the description provides a complete picture of what the agent will receive: columns, numeric indicators, row count, and provenance banner. It could further explain how this differs from dataset_provenance and dataset_stats, which also touch provenance and counts, but the 'call first' framing largely resolves that ambiguity.

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 does not need to explain parameter meaning because there are none to document. The schema is empty and fully covered, so there is no semantic gap for the description to fill.

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 Nofovo dataset. It also frames the tool as the schema-learning entry point, which distinguishes it from siblings like dataset_row or dataset_stats. The imperative 'Call this first' makes its purpose unmistakable.

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 timing guidance with 'Call this first to learn the schema.' It clearly positions this as the initial discovery step before other dataset tools. It does not explicitly mention when not to use it or name alternatives, but for a zero-parameter metadata tool the 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?

No annotations are provided, so the description carries the full burden. It discloses the ordering behavior ('in the order given') and the filtering logic, but omits whether the operation is read-only, what happens if no rows match, or the structure of the returned rows. This is a moderate gap 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?

The description is a single, concise sentence that front-loads the core behavior and ends with the use-case hint. No extraneous words; every element serves a purpose.

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 tool with no output schema and no annotations, the description gives the essential logic but omits details like the return format, error behavior (e.g., invalid column), and edge cases (no matches). An agent can infer basic usage but lacks full expectations for the result.

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?

With 0% schema description coverage, the description compensates by explaining that 'column' is the field to match and 'values' are the list of matching values. It clarifies the relationship between the parameters, though it doesn't detail constraints like string types or the max of 10 values, which are 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 clearly states the tool returns rows from the Nofovo dataset filtered by a column matching any of the given values, preserving the given order. This distinguishes it from siblings like dataset_row (likely a single row) and dataset_search (broader search), and aligns with the title's 'Compare rows side by side' intent.

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 this tool is appropriate, implying comparison of specific entities. It doesn't explicitly name alternatives or exclusions, but the purpose is specific enough for an agent to infer when to choose it over other dataset tools.

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 must disclose behavior. It states the exact information returned (source, date computed, licence, citation), indicating a read-only retrieval operation with no side effects. It does not mention any error cases or rate limits, but for a simple zero-parameter lookup, this is adequate.

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, front-loaded with the key content (what it provides) followed by a usage note. No wasted words; every clause contributes. The structure is efficient and immediately scannable.

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 read-only tool with no output schema, the description is complete. It tells the agent what data will be returned (source, date, licence, citation) and when to use it. No additional context is needed for correct invocation.

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 is empty. Per the baseline for 0 params, the description doesn't need to explain parameter details. The description adds value by specifying the dataset name (Nofovo) and the nature of the returned data, which is more than necessary.

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 purpose: to provide the source, computed date, license, and citation for the Nofovo dataset. It uses a specific verb ('read this') and names the resource, making it distinct from sibling tools like dataset_columns 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 description gives explicit usage guidance: 'Read this to attribute a figure correctly.' This tells the agent when to use it, though it doesn't explicitly contrast with alternatives or state when not to use it. The context of sibling tools (columns, stats, etc.) implies this is for provenance only.

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 usefully discloses exact, case-insensitive matching, but does not mention whether multiple rows can be returned, empty results, ordering, or any other runtime 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 with no filler, and the title clearly front-loads the operation. Every phrase contributes to understanding the tool.

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, the core operation is described adequately, but there is no return-shape information and no relationship to dataset_search. Given the sibling list and no output schema, a bit more context would help.

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%, but the description maps both parameters by saying a column equals a value, making their roles clear. It adds little beyond the schema, though the exact/case-insensitive semantics give some extra meaning.

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 provides the verb 'look up' and the description names the resource (rows of the Nofovo dataset) and the exact matching condition. It clearly signals exact-key lookup, though it does not explicitly distinguish itself from 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 'equals a value exactly' implies use for exact-match lookups, and the sibling name dataset_search hints at the alternative. However, the description gives no explicit when-to-use or when-not-to-use 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, the description carries the behavioral disclosure burden. It mentions case-insensitive matching and the 50-row cap, which are useful, but it does not disclose ordering, default limit behavior, pagination, or what a non-matching query returns.

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 contains the core behavior and constraints without filler. 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?

The description is adequate for a basic substring search call with a required query and optional limit. It lacks an explicit statement of default behavior, output shape beyond 'rows,' or guidance on when the 50-row limit applies, but the core invocation is understandable.

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 already describes query as text to look for in any cell; the description adds case-insensitivity and an implicit 50-result cap, which helps clarify the limit parameter's purpose. The meaning of limit is still mostly inferred rather than explicitly stated.

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 that the tool returns rows from the dataset whose cells contain the query, including the case-insensitive behavior. This distinguishes it from sibling tools like dataset_stats or dataset_columns, though it does not explicitly differentiate itself.

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 implied: when an agent needs rows matching a text query across all cells. However, it does not state when to prefer this over sibling tools such as dataset_row, nor does it mention exclusions or fallback behavior.

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?

Given no annotations are provided, the description carries the full transparency burden. It discloses important parsing behavior (grouping commas and currency handled) and data-cleaning behavior (non-numeric rows excluded and counted), which go beyond obvious expectations. It does not detail error or edge-case handling, but for a simple stats tool this is meaningful disclosure.

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 one compact sentence that leads with the output statistics and adds two key handling caveats. Every word contributes; there is no redundancy or 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?

The description is adequate for summoning the tool but leaves gaps: with no output schema, it does not state the return structure, and it is ambiguous whether 'count' refers to numeric rows only or includes the non-numeric count. Error or empty-column behavior is also unaddressed, which matters because the description is the only source of guidance.

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?

With 0% schema description coverage, the description must explain the lone 'column' parameter, and it does: it must be a numeric column of the Nofovo dataset. It also implies that formatted values (commas, currency) are acceptable. It does not enumerate valid column names, but that is dynamic and presumably discoverable via dataset_columns.

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 explicitly lists the exact statistics computed (count, min, max, mean, median, sum) and names the resource (Nofovo dataset), making the purpose unmistakable. This distinguishes it from sibling tools like dataset_search, dataset_top, or dataset_row, none of which produce summary statistics.

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 makes it clear this is the tool for summary statistics, so usage is strongly implied. However, there is no explicit statement about when to choose this over siblings or what conditions would make a different tool more appropriate; it leaves 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 provided, the description carries the full burden of behavioral disclosure. It mentions 'highest (or lowest)' but does not explain tie-breaking, return format (full rows or just values), default ordering, or behavior when the column is non-numeric. The description also does not mention that the ascending parameter controls order or that limit is capped at 50. These gaps leave the agent guessing about important execution details.

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 short sentence, which is concise and front-loaded with the core purpose. However, it is so terse that it sacrifices necessary detail. It is not verbose, but the brevity is more under-specification than disciplined conciseness.

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 three parameters, no output schema, and no annotations, the description is incomplete. It does not mention the return format, default ordering, or edge cases such as missing columns or empty results. An agent would need to infer too much to call the tool correctly, especially regarding limit and ordering 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 only 33% (only ascending has a description). The description clarifies that column must be numeric but does not explain the limit parameter or its range, nor does it elaborate on column format or requiredness beyond the schema. Since coverage is low, the description should compensate, but it adds little for limit and nothing for column semantics beyond 'numeric'.

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 ranks rows by a numeric column and returns the highest or lowest, with an illustrative question ('which is the most/least X'). The title confirms the verb 'Rank', and the resource (dataset rows) is explicit. It is distinct from sibling tools that handle columns, comparison, provenance, single rows, search, and statistics.

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 no guidance on when to use this tool versus siblings like dataset_search or dataset_stats. It offers an example question but does not specify when not to use it or mention alternatives. An agent cannot determine whether this or another tool is more appropriate for a given request without additional context.

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