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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 describes the returned content but does not explicitly state that the operation is read-only or side-effect-free, nor does it mention any performance implications. Since it only reports metadata, this is a minor gap; a read-only hint would have raised the score.

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 that packs all necessary information: what is returned (columns, numeric flags, row count, provenance banner) and when to call it. No wasted words; the usage hint 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 parameterless metadata tool, the description covers the output content and usage timing. It lacks an explicit return format (e.g., JSON structure), but given the simplicity and the absence of an output schema, the agent has enough to invoke it correctly. The 'call this first' guidance fills the contextual need.

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 the schema coverage is 100% (vacuously). With no parameters to document, the baseline is 4, and the description does not need to add parameter details. It correctly focuses on the output.

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, their numeric status, row count, and provenance banner for the Hardenvo dataset. It names the specific resource and distinct output elements, making it easy to distinguish from siblings like dataset_provenance or dataset_stats. The instruction to call it first further clarifies its role.

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?

It explicitly says 'Call this first to learn the schema,' giving clear guidance on when to use it. It does not mention alternatives or when not to use it, but the context is sufficient for an agent to know this is the entry point for schema discovery.

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 implies a read-only operation by describing row retrieval. It doesn't mention error handling or output format, but for a simple retrieval tool 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?

A single, front-loaded sentence that conveys the action, scope, and ordering. 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 simple tool with two parameters and no output schema, the description covers the core functionality and ordering. It doesn't describe the return format or edge cases, but those are not critical 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?

Schema coverage is 0%, so the description must explain parameters. It clarifies that 'column' is a column name and 'values' are the values to match, which adds meaning beyond the raw schema. It doesn't detail constraints like max items, but those 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 retrieves rows from the Hardenvo dataset filtered by column values, preserving order. This distinguishes it from siblings like dataset_search which likely does broader searches, and dataset_row which likely fetches a single row.

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 usage context, indicating it's for comparing rows across values. It doesn't explicitly exclude other uses or name alternatives, but the context is sufficient 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?

No annotations are provided, so the description carries the burden. It states what information is returned (source, date, licence, citation) and implies a read-only informational tool, but it does not explicitly state that no side effects occur or that the data is static. For a simple provenance lookup, 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 sentences with no wasted words. The first sentence lists the content, the second gives the use case. Information is front-loaded and every sentence 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 parameterless tool with no output schema, the description covers the essential content and purpose. It is sufficient for an agent to know when to call it and what to expect. Minor gaps like whether the date is current or how to interpret the licence are not critical for basic usage.

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 the schema is empty (100% coverage). Per the rubric, a baseline of 4 applies when there are no parameters to explain. The description correctly does not attempt to add 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 states the tool provides source, computed date, licence, and citation for the Hardenvo dataset, with a specific purpose (attributing a figure). It is distinct from siblings like dataset_columns or dataset_stats by nature, though it does not explicitly name alternatives. The verb 'Read' and resource are clear.

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 concrete usage context: 'Read this to attribute a figure correctly.' This implies when to use it (when citing or attributing) but does not explicitly mention when not to use it or compare it with alternatives. The guidance is present but could be more explicit about 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 carries the full burden. It discloses case-insensitivity but omits whether the operation is read-only, whether it returns a single row or multiple, pagination, error behavior, or performance characteristics. The read-only nature 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 a single concise sentence with no redundancy. It front-loads the operation and the key qualifier (exact, case-insensitive). While short, it avoids filler and is appropriately sized for a simple lookup.

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 lacks an output schema and annotations, and the description does not specify the return format, whether all matches are returned, error handling, or how to obtain valid column names. An agent may know to call dataset_columns, but the description provides no such context, leaving critical 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 0%, so the description must compensate. It merely restates that a column equals a value, adding little beyond the parameter names. It does not clarify column naming conventions, value formatting, or return 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 states the tool's function: retrieving rows from the Hardenvo dataset where a specified column equals a given value exactly, with case-insensitivity. The title reinforces this with 'exact key,' and the description differentiates from fuzzy-search siblings by emphasizing 'exactly.'

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 guidance is provided on when to use this tool versus alternatives like dataset_search or dataset_top. The description implies exact-match use but never explicitly states conditions or exclusions, leaving the agent to infer.

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 must carry the full behavioral burden. It discloses case-insensitivity and a 50-row cap, which are valuable traits. However, it does not mention ordering, read-only nature, or behavior when no matches occur. The disclosed traits are useful but not exhaustive for a tool with no annotation safety net.

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 packs core behavior, case-insensitivity, and the result cap into minimal words. It is front-loaded and contains no filler. This is an ideal example of efficient, structured description.

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 a clear schema and no output schema, the description covers the essential behavior: searching across cells, case-insensitivity, and limit. It implies the return type ('Rows') and does not require extensive elaboration. Minor gaps like ordering or pagination are not critical for successful invocation, so the description is adequately 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 coverage is 50%: query has a description, limit does not. The description adds meaning by stating that rows contain the query in any cell and that results are limited to 50, which directly clarifies the purpose of both parameters. This compensates for the missing limit description and gives the agent enough context to use them correctly.

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 that it returns rows of the Hardenvo dataset whose cells contain the query, case-insensitively, up to 50 rows. This is a specific verb-resource pair (searching dataset rows) and distinguishes it from siblings like dataset_columns or dataset_row. The name and description together leave no ambiguity about the tool's function.

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 (search for rows matching text) but provides no explicit guidance on when to prefer this tool over alternatives like dataset_row or dataset_top. It does not mention exclusions or alternative conditions. The agent can infer usage from the name, but there is no direct routing.

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 does disclose that non-numeric rows are excluded and counted and that commas/currency are handled, but it does not mention read-only status, error behavior, or what happens if the column is missing or entirely non-numeric.

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 a useful parenthetical. It front-loads the computed statistics and avoids unnecessary 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 enumerates the returned statistics and explains the data-cleaning behavior well enough for a simple tool. It does not describe the exact return shape, but no output schema exists and the listed values are sufficient for 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 schema only defines column as a string, and the description adds meaning by indicating it refers to a numeric column in the Hardenvo dataset. However, it does not specify whether the column must exist, case sensitivity, or behavior when given a non-numeric column.

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 names the action (computes summary statistics) and the specific outputs (count, min, max, mean, median, sum) for a numeric column. This distinguishes it from the sibling tools such as 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 description gives processing details about commas, currency, and non-numeric rows but does not state when to use this tool versus the sibling alternatives. No explicit comparison or selection guidance is 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 is the only behavioral disclosure. It adds ordering semantics ('highest (or lowest) rows by a numeric column') and implies a read-only query, but it does not address return shape, tie-breaking, or row-count 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 compact, one sentence plus an intent-framing quote, and the ranking concept is front-loaded. The quote is mildly redundant with the main sentence but does not waste much space.

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 three-parameter tool with no output schema or annotations, the description provides the core ranking idea but omits response format and count semantics. It is adequate for a simple ranking tool but not fully self-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 description enriches 'column' by specifying it must be numeric and maps 'highest (or lowest)' to the ascending flag. It adds no meaning for the required 'limit' parameter, whose semantics are left to the schema's min/max 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 states a specific operation: returning the highest or lowest rows of the dataset ordered by a numeric column. This clearly distinguishes it from sibling tools like dataset_stats (summary) or dataset_row (single row), though it does not name an alternative 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?

The phrase 'which is the most/least X' implies a natural-language trigger for ranking questions. However, it does not state when to prefer this over dataset_stats or dataset_search, nor does it offer any exclusions or alternative routing.

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