site

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 does reveal the output contents and implies a read-only schema-inspection operation, but it never explicitly states that the tool makes no changes, nor does it describe any limitations, error behavior, or output format.

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 informative sentence plus a clear directive. Every phrase contributes value, and the most important information (what the tool returns and when to call it) is front-loaded with no redundancy.

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 metadata tool, the description is largely sufficient: it tells the agent what it will receive and that it should be called first. It does not specify the exact return structure or define 'provenance banner', and it does not connect the schema to sibling tools, but these are minor 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?

The tool has zero parameters, so the schema already fully covers the input contract. The description adds no parameter-specific meaning, but none is needed; the baseline of 4 for zero-parameter tools applies.

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 (Kickoffo dataset) and the delivered information: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate from siblings like dataset_stats or dataset_provenance, though the 'Call this first to learn the schema' framing gives it a distinct 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?

The description gives explicit context for when to call it ('Call this first to learn the schema'), which is strong usage guidance. It stops short of naming alternatives or stating when not to use it, so it lacks the exclusion criteria needed for a 5.

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 behavioral disclosure burden. It discloses the row-selection rule and ordering behavior, plus the 'any of the values' semantics. However, it does not mention whether matching is exact or partial, how missing values are handled, or what the output representation looks like.

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, efficient sentence states the core behavior first and the intended use case second. There is no filler, redundancy, or restating of the tool name.

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 low-complexity tool with two required parameters and no output schema, the description gives enough to call it correctly: which column to use, what values to pass, and what ordering to expect. Minor gaps remain around exact match semantics and output shape, but the core invocation is clear.

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 types with 0% description coverage. The description compensates by explaining that 'column' is the field to match and 'values' are the allowed values, and it adds meaningful ordering semantics tied to the values array.

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 behavior: return rows of the Kickoffo dataset whose column matches any of the given values, preserving the supplied order. This is specific enough to distinguish it from siblings like dataset_row, dataset_search, and dataset_top without needing their schemas.

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 a clear use case ('X vs Y' questions) but does not explicitly state when to prefer this tool over alternatives or when not to use it. The sibling names hint at other access modes, but the description gives no direct comparison or exclusion.

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 burden of explaining behavior. It discloses the tool is informational (read this to attribute) and lists the fields returned. This makes the read-only, non-mutating nature clear, though it does not describe the exact format of the citation or licence fields.

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 compact sentences with no filler. The first sentence lists the key content fields, and the second states the practical purpose. Every word contributes 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, read-only provenance tool with no output schema, the description fully covers what an agent needs to know: what information is available and why to use 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 and the schema has 100% coverage, so the baseline is 4. The description adds useful semantic context about what the returned information represents, which exceeds the empty input schema's contribution.

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 resource ('the Kickoffo dataset') and the specific information it returns: source, computed date, licence, and citation. It is immediately distinguishable from sibling tools like dataset_columns or dataset_search, which operate on data content rather than 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?

The description explicitly tells the agent when to use the tool: 'Read this to attribute a figure correctly.' This provides clear context for the intended use case. It does not name alternatives or exclusions, but for a zero-parameter metadata tool, the guidance 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?

With no annotations, the description must carry the full burden of behavioral disclosure. It does add one meaningful detail—case-insensitive matching—but it omits whether the tool returns a single row or all matching rows, what happens on no match, and what the response structure looks like. This leaves important behavioral ambiguity for an agent.

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 with no filler, front-loading the core lookup action and the case-insensitive matching rule. It is appropriately brief, though the noun-phrase construction ('The rows...') is slightly awkward and could be more direct as an imperative or verb-first statement.

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 exact lookup, the description covers the basic invocation and matching semantics. But with no output schema and no annotations, it should clarify whether the result is a row or a list of rows and how zero or multiple matches are handled. The case-insensitivity note helps, but the missing return-value behavior is a noticeable 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?

Schema description coverage is 0%, so the description must compensate. It does link the two parameters to their roles ('where a column equals a value exactly'), giving basic semantic clarity. However, it does not explain allowed column names, value formatting constraints, or error behavior, so it only partially fills the gap left by the absent schema descriptions.

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 that the tool returns rows where a column equals a value exactly, and the title 'Look a row up by an exact key' reinforces the intent. This goes beyond a tautology and the 'exactly (case-insensitive)' phrasing helps distinguish it from sibling tools like dataset_search. However, it is phrased as a noun clause rather than a clear verb+resource statement, so it is not maximally explicit.

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 meant for exact, case-insensitive row lookup, so an agent can infer a basic use case. But it never explicitly states when to prefer this tool over siblings like dataset_search or dataset_top, nor does it mention any exclusions or alternatives. The guidance is implicit rather than direct.

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 behavioral burden. It does disclose key behavior: case-insensitive substring matching, search across all cells, and a result cap of 50 rows. It does not mention sorting, pagination, or what happens when more than 50 rows match, but for a simple search tool these omissions are minor.

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, efficient sentence with no filler. It front-loads the core operation and embeds the most important constraints (case-insensitivity, result cap). Every word contributes to understanding.

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 only two parameters and no output schema, the description provides the essential context: what is searched, how matching works, and the maximum result count. The main missing piece is the exact shape of returned rows and default behavior when limit is omitted, but an agent can likely infer these from the dataset context and sibling tool names.

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 50%: query is documented in the schema, but limit is not. The description adds semantic value by relating the 'up to 50' cap directly to the limit parameter and clarifying that matching is case-insensitive across any cell. This partially compensates for the missing schema description of limit.

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 action: return rows of the dataset whose cells contain the query, with case-insensitive matching. It distinguishes the tool from siblings like dataset_row or dataset_stats by focusing on search behavior across all cells. The resource and verb are specific and 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 context is clear: use this tool when you need to find dataset rows matching arbitrary cell content. However, it does not explicitly mention when not to use it or call out alternatives such as dataset_row for row-by-ID retrieval or dataset_top for highest-ranked rows. The implied usage is still 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 provided, the description carries the full burden and does real work: it discloses that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. These are the key non-obvious behaviors for a stats tool over a messy dataset, though it stops short of covering null handling or behavior when the entire column is 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?

One dense sentence that front-loads the computed statistics and appends the two data-handling caveats in a parenthetical. Every element earns its place with no filler or redundancy.

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 low-complexity tool (one parameter, no nested objects) with no annotations and no output schema, the description covers purpose, parameter semantics, and preprocessing behavior; the output shape is reasonably inferable from the enumerated stats. The main gap is edge-case behavior (missing column, all non-numeric column) and exact output format.

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% and the schema only states 'column' is a non-empty string, so the description must add meaning. It does: 'column' is a numeric column of the Kickoffo dataset, which is the essential semantic. It does not specify whether names are case-sensitive or must match dataset_columns output, but for a single simple 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 description states a specific action (computes count, min, max, mean, median, sum) on a defined resource (a numeric column of the Kickoffo dataset), leaving no ambiguity about what the tool does. The enumerated statistics clearly differentiate it from siblings like dataset_columns, dataset_top, and dataset_search, which serve other purposes.

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?

Usage is implied: call this tool when you need summary statistics for a numeric column of the dataset. However, it does not explicitly state when not to use it or name alternatives (e.g., dataset_top for top rows, dataset_search for row-level queries), so an agent must infer the selection from the enumerated stats alone.

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 explaining behavior. It communicates the core behavior: returning the highest or lowest rows ordered by a numeric column. It does not disclose limit defaults, tie handling, null behavior, or the exact shape of the returned result, but the primary ranking behavior is clear.

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, front-loaded sentence that conveys the ranking semantics without unnecessary padding. The 'most/least' clause is somewhat redundant with 'highest/lowest', but the overall length is appropriate for a simple 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 tool with three parameters and no output schema, the description plus schema gives enough to make a reasonable call with `column`, `limit`, and `ascending`. It is less complete on output expectations, defaults, and explicit sibling differentiation, so an agent may need to infer some 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%, so the description must compensate. It adds useful meaning by specifying that `column` must be numeric and by linking `ascending` to highest/lowest ordering through 'most/least X'. However, it adds no explanation for `limit` beyond the Schema's type/range, leaving part of the parameter semantics unaddressed.

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 a specific action ('Rank') and resource ('rows ... by a numeric column'), and the 'highest/lowest ... most/least' phrasing makes the ranking intent clear. It is distinguishable from siblings like dataset_search or dataset_stats by focusing on ordering rather than filtering or aggregation, though it does not explicitly name those alternatives.

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 usage context: answering ranking questions over a numeric column. However, the description provides no explicit when-to-use or when-not-to-use guidance, and it does not mention any alternative tool or exclusion condition.

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