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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 states what is returned (columns, numeric flags, row count, provenance banner) but does not explicitly mention whether the operation is read-only or has side effects, nor any error conditions. Since it is a schema-discovery tool, read-only behavior is implied but not stated.

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 the primary output list in the first sentence and the usage guidance in the second. It is front-loaded, contains no filler, and 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 simple tool with no parameters and no output schema, the description adequately covers what it returns and when to call it. It does not describe the output structure or format, but that is not critical for an agent to invoke it successfully. The provenance banner concept is mentioned but not explained, which is a minor 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 tool has zero parameters, so the schema provides no parameter documentation. The description adds meaning by explaining what the tool returns, which is the sole source of information about its behavior. This exceeds the baseline for a zero-parameter tool.

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 the columns, which are numeric, row count, and provenance banner, and explicitly positions it as the first call to learn the schema. It is not a tautology and conveys a specific purpose, though it does not name sibling tools to 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 phrase 'Call this first to learn the schema' provides explicit usage context, telling the agent when to invoke this tool relative to others. It lacks explicit exclusions or alternative conditions, but the guidance is clear 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 carries the behavioral burden. It discloses two important behaviors: matching uses 'any of' the given values, and rows are returned in the order the values are given. However, it does not mention output format, empty-result behavior, or matching nuances like case sensitivity or exact-match semantics.

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 core behavior and appends the intended use case. Every part earns its place, with no redundant or filler content.

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 essential filter-and-order behavior is present. However, with no annotations and no output schema, the return shape and edge-case behavior remain unclear. The title's promise of 'side by side' comparison output is not reflected in the description body.

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 does add meaning: 'column' is the field to match, and 'values' are the candidate values to match against, with row order following the order of values. This goes beyond the raw schema, though it could specify matching details more precisely.

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 what the tool does: it retrieves rows from the Reqnix dataset where a specified column matches any of the given values, preserving the given order. The 'X vs Y' framing adds useful intent. It doesn't explicitly differentiate from sibling dataset tools, so it's not 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?

The description gives clear context by framing the tool for 'X vs Y' comparison-style questions. This helps an agent know when to select it. It does not provide exclusions or explicitly name alternative sibling tools, but the context is sufficient.

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 describes the content returned (source, date, licence, citation) and uses the word 'Read', which implies a read-only operation. However, it does not explicitly state that it makes no modifications, nor does it mention return format or any potential error conditions. For a simple metadata tool, this is adequate but not richly transparent.

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 zero waste. The key facts (source, date, licence, citation) are front-loaded, and the usage hint follows immediately. Every word earns its place; no filler or redundant phrasing.

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 covers what an agent needs to know: what the tool provides and why to use it. The dataset name and the attribute purpose are stated. Nothing essential is missing 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 input schema is trivially complete. Per the rubric, a 0-parameter tool earns a baseline of 4. The description adds no parameter-specific meaning, but none is needed since there is nothing to configure. It correctly focuses on what the tool returns rather than how to call it.

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 (Reqnix dataset) and the specific information provided (source, computed date, licence, citation). It also states a concrete use case ('attribute a figure correctly'), which distinguishes it from sibling tools that handle columns, rows, search, or stats. The verb 'Read' plus the content list makes the purpose unambiguous.

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 ('Read this to attribute a figure correctly') and implies it is the tool for provenance/metadata queries. It does not explicitly mention when not to use it or name alternatives, but the sibling names (dataset_columns, dataset_row, etc.) make the distinction obvious. 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.

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

With no annotations, the description carries the full burden. It discloses a key behavioral trait: matching is exact but case-insensitive. However, it doesn't clarify behavior for multiple matches, no matches, or the exact structure of the returned data, leaving meaningful 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, front-loading the core behavior. Every word contributes value, making it both concise and efficient for an agent to parse.

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 annotations and no output schema, the description is reasonably complete but leaves ambiguity: the title says 'a row' while the description says 'rows', and the return format is unspecified. It could also clarify how it differs from dataset_top or dataset_provenance.

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%, but the description references the parameters by name ('column', 'value') and explains their relationship. It adds the case-insensitive matching semantic, which is meaningful, but does not fully elaborate parameter semantics beyond what schema names and types already suggest.

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 precise behavior: returning rows where a column exactly equals a value, explicitly noting case-insensitivity. The title and description together make it distinct from sibling tools like dataset_search, which likely performs fuzzy or broader 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 the tool is for exact case-insensitive lookups, which hints at when to choose it over dataset_search, but it never explicitly states when to use it versus alternatives or when not to use it. The guidance is only implied, not spelled out.

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 discloses case-insensitive matching and the 50-row cap, which are key behaviors. It does not mention pagination, ordering, or error handling, but for a simple search tool this is adequate. No contradiction with annotations since none exist.

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 that immediately communicates the core function and key constraints. It is efficient and well-structured, with no wasted words.

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 search tool with a simple schema and no output schema, the description covers the essential details: what it searches, case-insensitivity, and a result cap. It does not describe the exact response format, but given the dataset context and sibling tools, the row structure is likely understood. Overall, it is complete enough 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 query parameter is well described in the schema ('text to look for in any cell') and the description reinforces it. The limit parameter lacks a schema description; the description says 'up to 50' which could be misinterpreted as a fixed cap rather than a configurable parameter. The description adds some meaning but does not fully clarify the limit parameter's role.

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: it returns rows of the Reqnix dataset that contain the query string, with case-insensitivity and a maximum of 50 results. This is a specific verb and resource, and it distinguishes from siblings like dataset_stats (aggregations) and dataset_columns (schema listing).

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 usage: use this when you need to find dataset rows containing specific text. However, it does not explicitly mention when not to use it or point to alternatives like dataset_row for a single row or dataset_stats for aggregates. The 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, the description discloses meaningful behavior beyond the basic stats: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This gives an agent a realistic picture of how the tool pre-processes data, though it stops short of describing edge cases like all-non-numeric columns.

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?

Everything is in one tightly worded sentence: the computed statistics are front-loaded, and the data-handling caveats follow. There is no redundant elaboration 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?

Given this is a simple one-parameter tool with no output schema, the description covers the essential return values (the listed statistics) and key data-cleaning behaviors. It does not detail the exact response format or all-non-numeric edge cases, but for this low complexity that is a minor 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 has 0% description coverage, so the description must define the 'column' parameter. The phrase 'numeric column of the Reqnix dataset' clarifies that the parameter should reference a column containing numeric values. This is sufficient for a single parameter, though it does not explicitly name the 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 lists exact outputs (count, min, max, mean, median, sum) and identifies the specific resource (a numeric column of the Reqnix dataset). This clearly distinguishes it from sibling tools like dataset_search or dataset_top by the aggregation operation.

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 title 'Summary statistics for a numeric column' implies when to use the tool, but the description gives no explicit when-not-to-use guidance or references to alternative siblings. Users must infer that this is for aggregating numeric data rather than retrieving rows or columns.

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 only states the basic ranking behavior and does not cover edge cases like ties, null values, ordering stability, or whether the operation is read-only. It also omits any mention of potential side effects or prerequisites.

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 core purpose and a clarifying quote. It is concise and to the point, though it could benefit from a brief note on default parameters without sacrificing brevity.

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 and no output schema, the description leaves important gaps: no default limit, no indication of return format, and no discussion of edge cases like ties or missing values. The description is too sparse to fully guide an agent through correct invocation 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?

Schema description coverage is only 33% (only 'ascending' has a schema description). The tool description adds minimal parameter context—'numeric column' hints at column type but does not explain 'limit' or default behavior, nor does it clarify the exact meaning of 'ascending' beyond the schema. The description does not compensate for the low schema 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 the tool ranks rows by a numeric column and returns the highest or lowest rows, distinguishing it from sibling tools like dataset_stats (aggregates), dataset_search (filtering), and dataset_row (single row). The phrase 'which is the most/least X' provides a concrete use case.

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 guidance on when to use this tool versus alternatives. The description implies a use case (ranking top/bottom rows) but does not mention when not to use it or point to specific sibling tools, leaving the agent to infer the appropriate context.

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