site

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

With no annotations, the description carries the burden of behavioral disclosure. It explains what information will be returned and that it should be called first, but it does not explicitly state that the operation is read-only or describe how the output is structured.

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, dense sentence with no filler. It front-loads the important output scope and ends with a useful invocation hint.

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-argument schema-introspection tool without an output schema, listing the exact pieces of information returned is sufficient. An agent has what it needs to call the tool and interpret its 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 zero parameters, so there is no parameter ambiguity. The no-parameter baseline of 4 applies; no description-level compensation 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 identifies the exact resource (Tachovo dataset) and the specific outputs: columns, numeric flags, row count, and provenance banner. It is not a tautology and gives a clear purpose, though it does not explicitly name sibling tools to differentiate from.

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 usage context and sequencing. It does not mention when not to use it or explicitly contrast it with sibling tools, but the guidance is direct enough for an agent.

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 burden. It discloses two important behaviors: any-of matching (OR semantics) and output order matching the input order. However, it omits the return structure, whether full rows or just the column are returned, and exact-match/case-sensitivity details, so the behavioral picture is incomplete.

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 front-loads the core selection logic and the ordering guarantee. No wasted words and no redundancy with the schema.

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 alone must convey what the agent gets back. It tells how rows are filtered and ordered but not what the response contains (full rows, columns included, row structure, pagination), leaving a significant gap for a 2-parameter tool.

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 does explain the relationship between column and values: column is the field being matched and values are the list of candidates. This adds meaning beyond the bare schema types, though it doesn't elaborate on formatting or matching rules.

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 is specific: it returns rows from the Tachovo dataset filtered by a column matching any of the given values, with ordering preserved. It clearly signals a comparison/selection tool, distinguishing it from siblings like dataset_search (general search) or dataset_top (aggregates).

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 context for when to use the tool, implying side-by-side comparison of specific values. It does not explicitly mention alternatives or when not to use it, but the context is unambiguous enough for basic 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?

No annotations are provided, so the description carries the full burden. It discloses the informational payload (source, computed date, licence, citation) and implies a read-only action via 'Read this', but it does not explicitly state that no side effects occur or whether the response is structured text or an object.

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 filler. The key content is front-loaded, and the purpose statement is immediately actionable. The title also reinforces the description without being redundant.

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, parameterless metadata-access tool, the description is complete. It names exactly what information is available and why an agent would call it. No output schema is present, but the listed fields suffice for correct invocation and expectation-setting.

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. The description adds no parameter details, but none are needed; a baseline of 4 is appropriate for a parameterless 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 states exactly what the tool returns: source, date computed, licence, and citation for the Tachovo dataset. It also identifies the intended purpose ('attribute a figure correctly'), making it clearly distinct from siblings like dataset_search, dataset_stats, 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 description gives a clear condition for use: read this when you need to attribute a figure correctly. It does not explicitly name alternatives, but the specialized provenance purpose is clear and distinct enough among the sibling list that confusion is unlikely.

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 is the only source of behavioral detail. It usefully discloses that the match is exact but case-insensitive. However, it does not explain what the tool returns (one row vs multiple rows), what happens when no row matches, or whether column names must match exactly.

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 sentence with no filler, and the core operation is front-loaded. Minor awkwardness in 'The rows of the Tachovo dataset where...' does not hurt clarity.

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?

Even though this is a simple two-parameter tool, the lack of an output schema and annotations puts the burden on the description. It leaves ambiguity about singular vs plural return ('a row' in title vs 'rows' in description), output shape, and no-match behavior, so an agent cannot fully predict the invocation 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 schema only provides 'column' and 'value' with minLength=1; the description clarifies that 'column' is the field to test and 'value' is the comparison value for an equality match. It adds the case-insensitive matching detail, but does not list valid column names or value constraints beyond 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 title and description clearly identify a lookup-by-exact-value operation: 'Look a row up by an exact key' and 'where a column equals a value exactly (case-insensitive).' The 'exact' qualifier distinguishes it from dataset_search, though it does not explicitly name sibling 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 word 'exact' implies the intended use case (precise key lookup) versus broader search, but no explicit guidance says when to prefer this tool over siblings such as dataset_search. The usage context must be inferred rather than stated.

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 does disclose case-insensitivity and a 50-row cap, which is useful, but it does not mention return shape, ordering, pagination, or behavior with no matches.

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 with no filler. The core behavior, matching semantics, and result cap are all 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 two-parameter search tool, the description is reasonably complete, but the absence of an output schema and any usage alternatives leaves some ambiguity about result format and tool selection. Adequate for basic invocation, 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 schema describes the query parameter and constrains limit, but the description adds meaning by noting case-insensitive matching, cell-level search, and the effective 50-row ceiling. This compensates for the lack of a limit description 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 states a specific operation: returning rows of the Tachovo dataset whose cells contain the query. It is not a tautology and clearly differentiates itself from row-fetching or stats tools by describing cell-level searching.

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 is implied by the description, but there is no explicit guidance about when to choose this tool over siblings like dataset_row, dataset_stats, or dataset_top. An agent must infer it from the search semantics.

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 disclosure burden. It covers two nontrivial behaviors: handling of grouping commas and currency, and exclusion-and-counting of non-numeric rows. It does not describe error behavior for a missing column or the exact output shape, but the enumerated statistics imply the return content.

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 ~30-word sentence, front-loaded with the computed statistics and ending with the parsing/exclusion caveats. Every element earns its place with no 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 tool is simple — one required parameter, no output schema, no annotations. The description lists the six returned statistics and the key input quirks, covering most of what an agent needs to call it correctly. A precise return structure and behavior for an entirely non-numeric column are the only notable omissions.

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 add meaning to the single 'column' parameter. It clarifies that the parameter is a column name in the Tachovo dataset and that it is expected to be numeric, going beyond the bare string type. It stops short of telling the agent how to discover valid column names (e.g., via dataset_columns).

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 computation (count, min, max, mean, median, sum) applied to a numeric column of the Tachovo dataset, which is enough to distinguish it from siblings like dataset_columns, dataset_row, and dataset_search. The title 'Summary statistics for a numeric column' reinforces the resource and operation precisely.

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 sibling tool is named and no when-to-use/when-not-to-use guidance is provided. An agent can infer applicability from the enumerated statistics, but there are no explicit 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.

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

No annotations are provided, so the description carries the burden of disclosing behavior. It conveys that the tool returns a subset of rows sorted by a numeric column and can be ordered highest or lowest. However, it does not explicitly state that the operation is read-only, describe the output structure, or address ties and 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?

The description is a single, front-loaded sentence that immediately states the behavior and includes a memorable query exemplar. It contains no filler and does not repeat schema details, earning full credit for conciseness and structure.

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 ranking tool, the description plus schema covers the core invocation needs: column, limit, and ascending. But with no output schema and no annotations, some operational details remain unspecified, such as the exact return format, default limit behavior, and error cases, leaving it slightly incomplete.

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 should compensate. It adds meaning for 'column' by specifying it must be numeric and for 'ascending' via 'highest (or lowest)'. The 'limit' parameter is not elaborated, but its name and schema min/max make it reasonably clear, leaving the added value partial.

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 returns the highest or lowest rows of the dataset ranked by a numeric column. The example 'which is the most/least X' makes the tool's purpose unambiguous and distinguishes it from siblings 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 phrase 'which is the most/least X' gives a clear usage context for when to select this tool. It does not explicitly mention alternatives or exclusions, but the ranking-by-numeric-column framing is enough to separate it from the statistical, search, and row-lookup siblings.

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