site

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

With no annotations present, the description carries the behavioral disclosure burden. It explains what the tool returns but does not explicitly state that it is read-only, side-effect-free, or describe any failure/performance characteristics. For a simple metadata tool this is acceptable but not fully 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 a single functional sentence with a clear front-loaded action instruction. There is no filler, and each piece of information contributes to the agent's 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 no-parameter schema-introspection tool, the description is largely complete: it lists the returned components and gives sequencing guidance. Because there is no output schema, some detail about the exact shape of the returned structure would be helpful, but the tool's simplicity keeps this from being a major 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 already covers everything needed. Even though the description adds no parameter-specific detail, none is required; the baseline 4 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 specifies what the tool returns: columns, numeric column flags, row count, and the provenance banner. It also establishes the tool's role as the schema-discovery entry point, distinguishing it from siblings like dataset_row or dataset_search, which operate on data rather than metadata.

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 instruction 'Call this first to learn the schema' gives clear, actionable context for when to use the tool. It does not explicitly name exclusions or alternatives, but the schema-first guidance is strong enough to route an agent 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 provided, the description carries the full burden of behavioral disclosure. It reveals two important behaviors: matching uses 'any of' semantics (OR-style filtering) and rows are returned 'in the order given.' It does not describe the output format or side-by-side presentation, but the core behavior is clearly communicated.

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, tightly worded sentence with no filler. It front-loads the main behavior and appends the usage qualifier efficiently.

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 two-parameter read-style tool, the description tells an agent what to pass and what to expect in terms of selection and ordering. It does not specify the exact return shape, but the absence of an output schema is partially mitigated by the straightforward nature of the operation.

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 define the parameters, and it does: 'column' is the field being matched and 'values' are the accepted values, with 'any of' clarifying the matching semantics. This adds genuine meaning beyond the plain schema types, though it leaves maxItems/minItems details to 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 specifies that the tool returns rows from the Scopedly dataset filtered by whether a column matches any provided values. The 'for "X vs Y" questions' qualifier gives it a distinct identity among the sibling data tools, though it does not explicitly contrast itself with 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 phrase 'for "X vs Y" questions' provides a clear usage context: this tool is for comparing sets of values in a column. It does not, however, explicitly state when not to use it or name alternative sibling 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 carries the full burden of behavioral disclosure. It enumerates the output fields (source, date, licence, citation) but does not explicitly state that the operation is read-only or has no side effects. For a simple provenance getter, this is acceptable but not fully 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 highly concise, containing only two short sentences. The main purpose is front-loaded in the first sentence, and the second provides a direct usage instruction. Every word contributes value, with no redundancy or unnecessary detail.

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 the absence of an output schema, the description adequately informs the agent of what the tool returns (source, date, licence, citation). It also gives a clear context for when to use it (attributing a figure). No missing information prevents correct invocation or interpretation.

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 description coverage is 100% (no parameters to document). According to the rubric, a baseline score of 4 applies for no parameters. The description does not need to add parameter details, and it does not, so no deduction is made.

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 provenance information (source, date, licence, citation) for the Scopedly dataset. It uses an instructive phrase 'Read this' and explicitly distinguishes itself from sibling tools by focusing on attribution, making it easy for an agent to select it for that need.

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 provides explicit when-to-use guidance: 'Read this to attribute a figure correctly.' While it does not name alternative tools, the context of sibling tools (e.g., dataset_columns, dataset_stats) implies that this tool is for provenance rather than data manipulation, giving adequate direction.

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 burden. It discloses case-insensitive exact matching, which is valuable. However, it does not disclose return shape, whether it returns a single row or all matching rows, or no-match behavior — a significant gap for a lookup tool with zero annotation coverage.

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 that front-loads the matching semantics with zero filler. It is appropriately compact, though slightly under-specified rather than optimally concise.

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?

With two self-explanatory parameters and no output schema, the core purpose is clear. But the description omits output format, single-vs-multiple row behavior, and error handling, and there is a minor inconsistency between the title ('a row') and description ('the rows'). Adequate but with identifiable 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. The phrasing 'where a column equals a value' implicitly documents both parameters (column as the key field, value as the match target), adding semantic meaning beyond the bare string type. It doesn't fully compensate, though — it omits details like value formatting expectations.

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 resource ('rows of the Scopedly dataset') and states the specific filtering semantics ('column equals a value exactly, case-insensitive'), which distinguishes it from dataset_search and dataset_stats. The 'exactly' qualifier is a meaningful differentiator against sibling tools.

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 through the exact-match semantics — an agent can infer this is for precise lookups rather than fuzzy searches. However, no explicit when-to-use/when-not-to-use guidance or named alternative is provided, so the routing decision is left 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 behavioral disclosure burden. It discloses case-insensitive matching, that any cell can be searched, and a hard cap of 50 rows. Minor omissions include ordering and empty-result behavior, but the core 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?

A single efficient sentence that front-loads the key behavior and constraint. Every word earns its place, with no redundant 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?

For a simple, low-complexity search tool with two parameters and no output schema, the description is close to complete: it tells the agent what to search, how matching works, and the result cap. It is not exhaustive on return shaping, but 'rows' is explicit and the sibling context makes the tool's role 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?

Schema description coverage is only 50%: query is documented, limit is not. The description adds meaning by clarifying case-insensitive matching across cells and by indicating the 50-row cap, which maps to the limit parameter and adds value beyond the schema. It stops short of explaining limit's default behavior when omitted.

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?

States a specific resource (Scopedly dataset), a clear search action over cell contents, and a concrete scope (case-insensitive substring matches, up to 50 rows). This distinguishes it from siblings like dataset_columns or dataset_stats, which have different 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 by the search semantics: use when you need rows whose cells contain some text. However, it does not explicitly differentiate from dataset_row (single-row lookup) or dataset_compare, nor does it state 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 the full burden. It discloses handling of non-numeric rows and formatting, which is good. Yet it does not clarify the exact structure of the output (e.g., whether excluded count is included, or the order of statistics), so some behavior remains opaque.

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 all necessary details—what is computed, input handling, and edge-case behavior—without fluff. The parenthetical clarifications are efficient and relevant.

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 statistical tool, the description covers the core behavior, input format, and edge cases. It lacks an explicit output schema, but the listed statistics imply the output. Given the minimal parameter set, the description is sufficiently complete for an agent to invoke 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?

The only parameter 'column' is described as a numeric column, adding semantic detail beyond the schema's plain string type. It implies that the column must contain numeric data and hints at input formatting (commas, currency). However, it does not specify whether the column is referenced by name or index, leaving minor ambiguity.

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 function (computing summary statistics for a numeric column) and lists the specific statistics (count, min, max, mean, median, sum). It is distinct enough as a statistical aggregation tool, though it does not explicitly contrast with sibling tools like dataset_top 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 provides some usage guidance: handling of grouping commas and currency, and exclusion/counting of non-numeric rows. However, it does not explicitly state when to use this tool over others, leaving some ambiguity for an agent deciding between statistics and other dataset operations.

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 responsibility for disclosing behavior. It conveys a read-only ranking operation but does not mention side effects, error behavior, or return format. It is not misleading, but it is minimal.

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 an illustrative example. It front-loads the key behavior ('highest or lowest rows') and avoids unnecessary wording.

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 definition is adequate for a simple query tool but lacks details about the output structure (e.g., whether full rows are returned) and edge cases like ties or null values. Given no output schema, a bit more context would be helpful.

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?

Only one of three parameters (ascending) has an inline description; column and limit are undocumented. Schema coverage is low, and the description does not clarify that column must reference a numeric field or what limit defaults to. Additional parameter guidance is needed.

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 highest or lowest rows by a numeric column and provides a natural-language example ('which is the most/least X'). It is easily distinguished from siblings like dataset_row, dataset_search, and 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 implies use for ranking questions but does not explicitly state when to prefer this over alternatives such as dataset_row or dataset_stats. No explicit guidance is given about when not to use the tool.

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