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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 behavioral burden. It discloses what the tool returns (columns, numeric flags, row count, provenance banner), which is meaningful for a zero-parameter exploration tool, but it never explicitly states the operation is a safe read with no side effects. The disclosure is adequate but not rich.

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 with zero waste. The first front-loads the full content scope, and the second delivers a crisp usage directive. Every word earns its place and nothing is redundant with the title.

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 schema-exploration tool, the description covers the return contents and the recommended call order, which is nearly everything an agent needs. It could explicitly note the read-only/safe nature, but nothing critical for invoking the tool correctly 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 the baseline of 4 applies — there is no schema burden for the description to compensate for. The description appropriately spends its words on return values instead of parameters, which is the correct allocation for a no-argument 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 enumerates the exact outputs (columns, which are numeric, row count, provenance banner) for the Retainvo dataset, making the resource and scope clear. It distinguishes from siblings by what it returns — schema-level info versus rows (dataset_row), stats (dataset_stats), or search results. A specific verb is implied rather than stated, so it stops short of 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 sentence 'Call this first to learn the schema' gives an explicit, actionable usage directive that tells the agent this is the entry-point tool. It lacks explicit exclusions or named alternative tools, but the directive effectively routes the agent to the right first call among the 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, the description must carry behavioral disclosure. It clearly conveys row filtering and value-order preservation, but it does not describe the return presentation, whether the operation is read-only, or any output limits. The title's 'side by side' comparison behavior is implied, not fully specified.

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?

One dense sentence packs all essential information: the target dataset, filtering mechanism, ordering behavior, and intended use case. No wasted words, and the core 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?

The tool is simple (two parameters, no nested objects, no output schema), and the description covers the core invocation details. The main gap is clarifying what 'compare rows side by side' means in the output, which the title implies but the description does not detail.

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 compensate. It effectively explains that 'column' is the filter field and 'values' are the matching values, adding the crucial detail that row order follows the given value order. This provides meaning beyond the raw 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 states a specific operation: return rows whose column matches any given values, in a specified order. This distinguishes it from siblings like dataset_search (keyword search) and dataset_row (likely a single row fetch).

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' gives a clear, useful context for when this tool is appropriate. However, it does not explicitly name alternatives or conditions when not to use it.

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 full burden. It discloses what information will be returned (source, date, licence, citation) and implies a read-only operation. It does not mention potential errors or performance, but such concerns are minor for a provenance/metadata 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?

Two concise sentences pack all necessary information: the purpose and the use case. The essential content is front-loaded, 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 no-parameter, no-output-schema tool, the description lists the expected returned fields. It explains when to use it (attribution) and what it provides, which is sufficient for an agent to invoke it correctly. Minor gap: no mention of output format, but the listed fields imply a structured 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?

The tool has no parameters (schema coverage 100% vacuously). The description does not need to explain parameters, and the baseline for zero parameters is 4. No additional meaning is required.

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 provenance metadata (source, date computed, licence, citation) for the Retainvo dataset. This specific verb+resource is unambiguous and distinct from sibling tools that operate on data rows, columns, or 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 phrase 'Read this to attribute a figure correctly' explicitly identifies when to use the tool. It implies a distinct use case from data querying, though it does not explicitly name alternatives or exclusion conditions.

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 disclosure burden. It does usefully disclose exact equality and case-insensitivity, but it does not state whether the result is a single row or all matching rows, how no-match results behave, or whether 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 title and single-sentence description are compact, with the most important qualifier ('exactly', 'case-insensitive') front-loaded. The grammar is slightly awkward, but there is no filler or redundant material.

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 no output schema and no annotations, so the description is the sole source of behavior. It defines the match predicate well but leaves out return cardinality and no-match behavior, which an agent would need to confidently use the tool 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?

Schema description coverage is 0%, so the description must compensate. It implicitly explains that 'column' is the dataset field to match and 'value' is the exact comparison value, and it adds the case-insensitivity detail. It does not explicitly walk through each parameter, but the schema already constrains both to required non-empty strings.

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 gives a clear verb and resource ('Look a row up'), and the description sharpens the semantics to exact, case-insensitive equality on a column. It does not explicitly name a sibling to differentiate from, and the singular 'a row' vs plural 'the rows' creates a slight definitional ambiguity.

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 phrasing 'exact key' implies this is the precise-lookup tool rather than a broader search or comparison, giving an agent some contextual signal. However, it never names dataset_search or dataset_compare as alternatives, nor states when not to use this tool.

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 explicitly reveals case-insensitive matching and a 50-row cap, which are meaningful behavioral traits. However, it does not disclose output format, ordering, or what happens when no rows match, but for a read-only search, this is fairly 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?

A single, concise sentence that front-loads the core action and key constraints. There is no filler or repetition; 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?

The description is adequate for a simple search tool but leaves gaps: it does not state the default limit when the parameter is omitted, the ordering of results, or whether rows are returned as full objects or just references. With no output schema, these details would help the agent invoke it correctly and interpret results.

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%: query has a description, limit does not. The description adds 'case-insensitive' (not in schema) and mentions 'up to 50', which hints at the limit parameter but does not explicitly explain how the limit parameter controls the cap. It adds some value but does not 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 states a specific verb ('Rows...whose cells contain the query'), identifies the resource (the Retainvo dataset), and adds behavioral specifics (case-insensitive, up to 50). It clearly distinguishes itself from siblings like dataset_row (which fetches a specific row) and dataset_top (which returns top rows). No tautology or ambiguity.

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 guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or scenarios where dataset_row or dataset_top would be more appropriate. The only context is the search behavior itself, which is implied but not stated as a selection rule.

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 reveals important parsing behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a bare 'compute stats' claim and informs the agent about data-cleaning effects.

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 dense sentence that front-loads the metric list before the data-handling caveats. There is no filler or redundant restatement of the name or title.

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 read-only stats tool with no annotations and no output schema, the description is nearly complete: it enumerates the returned metrics and the input parsing behavior. Minor ambiguity remains about whether 'count' refers to numeric rows or total rows and how excluded rows are reported, but for this low-complexity tool the gaps are small.

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 single column parameter has no schema-level description (0% coverage), so the description must compensate. It does by clarifying that the column must be numeric and belong to the Retainvo dataset, and by noting input formatting quirks. It does not state exact-match or case requirements, but for one string parameter 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 title and description clearly define the tool as computing summary statistics for a numeric column, enumerating the exact metrics: count, min, max, mean, median, and sum. It identifies the specific resource (Retainvo dataset), though it does not explicitly distinguish itself from sibling tools like dataset_top or dataset_compare.

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 case is clear: call this tool when numeric summary statistics for a Retainvo column are needed, and the metric list makes the scope explicit. It does not name alternatives or state when not to use it, but the context is clear enough for an agent to select it over row/search/compare 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 provided, the description carries the full burden of behavioral disclosure. It states the core ranking behavior but does not disclose default limit behavior, tie handling, null handling, or whether the result is a sorted list or a truncated top-N set. The description adds little beyond the title and tool name.

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 one concise sentence with the key ranking idea front-loaded. It is not bloated, though the parenthetical quote adds only marginal value and partially duplicates the title.

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 top-N ranking tool, the description covers the essential purpose and the need for a numeric column. However, without an output schema or annotations, it does not fully clarify return shape, default limits, or edge-case behavior, so it is only minimally 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?

Schema description coverage is only 33%, so the description needs to compensate. It usefully explains that 'column' must be numeric, but it does not explain the 'limit' parameter or clarify the default ordering beyond what the schema already says for 'ascending'. This leaves a meaningful parameter-semantics gap.

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: returning the highest or lowest rows of the Retainvo dataset based on a numeric column. The phrase 'which is the most/least X' makes the intent easy to grasp, though it does not explicitly distinguish it from sibling tools like dataset_stats 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 description implies the use case: ranking rows by a numeric value to find extremes. However, it does not mention when to prefer this over sibling tools or when not to use it, leaving the routing decision mostly to the agent's inference.

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