site

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

With no annotations, the description carries the full burden. It clearly states the output contents and that it is a schema-discovery call, implying a read-only operation. It does not mention potential errors or performance characteristics, but this is sufficient for a simple metadata call.

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 concise (two sentences) and front-loaded with the key information about what is returned and when to call it. The sentence structure is clear and direct, aside from a minor typo ('Mandatzo').

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 no parameters and no output schema, the description provides all necessary context: what the tool returns, its role as the first call, and how it fits into learning the dataset. 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 description provides no parameter details. Per the baseline rule for 0 params, a score of 4 is appropriate; there is nothing to explain.

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 explicitly lists what the tool returns (columns, numeric flags, row count, provenance banner) and states its purpose as learning the schema. This clearly distinguishes it from sibling tools like dataset_search or 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 provides direct usage guidance: 'Call this first to learn the schema.' This tells the agent when to use it (before other dataset operations) and implies it is the entry point for understanding the dataset.

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 carries full responsibility. It mentions the filtering and ordering behavior but omits side effects, error conditions (e.g., unknown column, missing values), and read-only guarantees. This is a significant gap for a data-access 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?

A single concise sentence captures the core behavior and purpose without unnecessary words. The structure is front-loaded with the primary action and then supported by the ordering detail and intended use case.

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 simple parameters and no output schema, the description is sufficiently complete for typical use. It covers filtering, ordering, and the comparison use case. It could mention the limit of 10 values, but that is already enforced in the schema, so it is not a critical omission.

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 explains that 'column' selects the attribute to match and 'values' are the target values, which gives partial semantics. However, it does not clarify whether column is a name or index, or what value types are expected. With 0% schema coverage, more detail would be beneficial.

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 compares rows side-by-side by filtering a specified column for any of the given values, preserving the provided order. This distinguishes it from siblings like dataset_row (single row) and dataset_search (general 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' gives a clear use case, but it does not explicitly contrast with alternatives (e.g., when to use dataset_search or dataset_row instead). The guidance is implied through purpose but lacks direct 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 burden, and it does so by naming the fields returned and framing the tool as a read/attribution action. It does not explicitly say 'read-only', but 'Read this' and the metadata-only purpose make the behavior 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?

Two concise sentences, front-loaded with the returned data and ending with the use case. There is no filler, repetition, 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?

For a zero-parameter, no-output-schema provenance lookup, the description is complete: it names the dataset, the specific provenance fields, and the reason to call it. Nothing operationally important 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 is 4 and there is no parameter documentation burden. The description instead clarifies the content of the provenance information, which is the only relevant semantic detail here.

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 provides: source, computation date, licence, and citation for the Mandatzo dataset, and connects it to the action of attributing a figure correctly. This clearly distinguishes it from sibling dataset tools such as 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 gives a clear context for use: when you need to attribute a figure correctly. It does not explicitly list alternatives or exclusions, but for a zero-parameter provenance lookup the intended situation is obvious.

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 mentions case-insensitivity and exact matching, but omits details like return behavior (e.g., whether it returns one row or many, what happens on no match, ordering, or limits). This is thin for a tool with no annotation support.

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 behavior ('rows where a column equals a value exactly') and adds the case-insensitive note at the end. Appropriate length for the tool's simplicity.

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 lookup tool with no output schema and no annotations, the description is incomplete. It doesn't clarify how to specify the column (by name? index?), the expected return format, or edge cases like zero matches or multiple matches. An agent would likely need to guess or test.

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 implies 'column' and 'value' but gives no specifics—e.g., what a column identifier looks like (name or index) or the value format. The sentence barely adds meaning beyond the parameter names.

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 clear purpose: returning rows where a column equals a value exactly, with case-insensitivity noted. It distinguishes from sibling tools like dataset_search by specifying 'exactly' rather than fuzzy matching, but it doesn't explicitly name 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?

No guidance on when to use this tool versus dataset_search or other siblings. It doesn't mention alternatives or conditions that would favor one tool over another, leaving the agent to infer usage from the title 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, the description carries the full behavioral disclosure burden. It does disclose meaningful behavior: case-insensitive matching and a 50-row cap. It does not mention return format, ordering, or no-match behavior, but these are relatively minor for a simple search 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?

The description is a single sentence with no filler. It front-loads the search behavior and includes the key constraints without 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 two-parameter search tool with no output schema, the description covers the matching behavior, case sensitivity, and result cap. The lack of explicit return-format details is a minor gap given the simple 'rows' framing.

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 documents the query parameter, while the limit parameter is left with no description. The tool description adds value by clarifying case-insensitive matching for the query and implying that results are capped at 50, which gives the limit parameter meaning.

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 the tool returns rows whose cells contain the query, with case-insensitive matching and a maximum of 50 results. This is specific enough to distinguish it from sibling tools like dataset_columns or dataset_top, which suggest schema or aggregation operations.

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 usage is inferable: search across all cells for matching text. However, there is no explicit when-to-use guidance, no mention of alternatives, and no exclusions, so the agent must infer the boundary against sibling tools on its own.

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. It discloses key behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds value beyond the schema. It does not specify the return format or error handling, but for a simple stats tool, the disclosed behaviors are significant.

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 lists the statistics first, then adds the data-handling notes. No unnecessary words or repetition. It is concise and effective.

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 one parameter and no output schema, the description covers the main points: what it computes and how it handles data quirks. It could mention the return format (e.g., a JSON object with those fields) or error behavior, but it is sufficient for an agent to invoke it correctly in most cases.

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 clarifies that the 'column' parameter refers to a numeric column of the dataset, adding meaning beyond the plain string type. It does not specify case sensitivity or exact matching, but given a single parameter, it provides adequate context.

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 clear verb (compute summary statistics) and resource (a numeric column of the Mandatzo dataset), listing specific statistics (count, min, max, mean, median, sum). This distinguishes it from siblings like dataset_row or dataset_search, which handle raw data retrieval, so an agent can immediately identify its purpose.

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 for numeric column analysis but does not explicitly state when to use this tool over siblings (e.g., 'use for summary statistics, not raw row access'). No alternatives or exclusions are mentioned, leaving some inference to the agent, though the purpose is clear.

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 only mentions ranking direction but omits default limit behavior, return format (full rows vs values), and whether sorting is descending by default. The schema has some info on ascending but the description does not connect it.

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, which is concise, but it is structurally weak—the phrase 'The highest (or lowest) rows' is grammatically awkward and could be clearer. It does not front-load the key action.

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 3-parameter tool with no output schema and no annotations, the description is too sparse. It does not mention default limit, return structure, or example use, leaving significant ambiguity for the agent.

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% (only ascending has a description). The description adds no parameter details, failing to explain what 'column' means or how 'limit' behaves. Since coverage is low, the description must compensate but does not.

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 the tool returns the highest or lowest rows by a numeric column, which is a specific ranking operation. It distinguishes from siblings like dataset_row (single row) and dataset_stats (aggregate), though the phrase 'highest or lowest rows' is slightly ambiguous.

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 siblings. It does not mention alternatives or conditions for selection, leaving the agent to infer usage from the name and title alone.

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