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

With no annotations, the description carries the behavioral disclosure burden. It discloses the output contents (columns, numeric indicators, row count, provenance banner) and implies a read-only metadata operation, but it does not explicitly state that calling it has no side effects or that it always reflects the full current schema.

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 brief and informative, with no wasted words. The main content is a single sentence, though the usage instruction is placed at the end rather than 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 low-complexity tool with no parameters and no output schema, the description adequately explains what the agent will receive and why to call it. It might benefit from stating the return format, but the expected contents are enumerated well enough.

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 are no parameter semantics to document. The baseline for a zero-parameter tool is 4, and the description appropriately focuses on what the tool returns instead.

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 exposes the dataset's columns, numeric flags, row count, and provenance banner, so an agent knows what it returns. It doesn't explicitly name a sibling to differentiate from, but the 'call this first to learn the schema' line gives it a distinct role among the 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?

The description gives clear usage context: 'Call this first to learn the schema.' This tells an agent when to invoke it, though it does not explicitly describe when to use other tools such as dataset_compare or dataset_search instead.

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 reveals the ordering behavior but omits side effects (none expected, but not stated), return format, pagination, error handling, or what happens when no rows match. For a read-only operation, this is thin disclosure.

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 a clarifying dash clause. It is front-loaded with the core behavior and ends with a usage context. No fluff, but the dash fragment is slightly informal.

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 filter tool, the description covers the essential semantics, but given no output schema and no annotations, it should state that this is a read operation, describe the result shape (e.g., rows preserving original order), and possibly mention limitations (e.g., only matches exact values). The description is adequate but leaves gaps an agent would need to infer.

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 compensate. It does explain that 'column' is the field to filter on and 'values' are the list of acceptable values, which maps to the schema properties. However, it doesn't clarify parameter constraints (e.g., minItems=2, maxItems=10) beyond what schema already specifies, and it doesn't mention the 'column' must exist in the dataset.

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 operation (retrieve rows), the target (PTOroom dataset), the filter condition (column equals any of given values), and ordering. It distinguishes itself from sibling tools like dataset_search by explicitly describing the 'X vs Y' use case, though it doesn't 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?

The description gives a usage hint ('for "X vs Y" questions') which implies when to use it, but doesn't explicitly contrast with alternatives like dataset_search or dataset_row. No exclusions or prerequisites are mentioned, leaving some ambiguity about when this is preferred over similar 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly lists the output fields (source, date, licence, citation) and the directive 'Read this' implies a read-only operation. However, it doesn't explicitly state that no modifications are made or describe any side effects, though for a provenance tool these are minimal. It adds value by specifying what data is returned.

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 two sentences with no redundancy. The key content (source, date, licence, citation) is front-loaded, and the usage directive follows succinctly. Every word earns its place, making it easy for an agent to parse quickly.

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 no parameters and no output schema, the description provides enough information for an agent to know what it returns (the provenance fields) and when to use it (attribution). The scope is clearly defined, and there are no missing details that would impede correct invocation. The simplicity of the tool matches the completeness of the description.

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 trivially 100% (empty properties). Baseline for no parameters is 4. The description doesn't need to explain parameters, and it doesn't repeat schema content. It effectively communicates that no input is required, which is consistent with 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 resource (PTOroom dataset) and the specific content it provides (source, date computed, licence, citation). It distinguishes itself from siblings by focusing on provenance rather than data operations like searching, comparing, or top values, so an agent can tell it apart without opening 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 phrase 'Read this to attribute a figure correctly' explicitly tells the agent when to use this tool (for citation/attribution). While it doesn't name alternatives or exclusions, the tool is unique among siblings (provenance vs data queries), so the context is clear. It could be improved by explicitly stating not to use it for row-level data queries.

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 does add value by surfacing the case-insensitive exact-match behavior, but it does not clarify whether one row or multiple rows are returned (title says 'a row', description says 'rows'), nor does it describe output shape or error 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 a single efficient sentence that front-loads the resource ('rows of the PTOroom dataset') and then adds the key behavioral qualifier ('equals a value exactly, case-insensitive'). Every word contributes signal; 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 simple 2-parameter lookup with no output schema, the description covers the basic purpose and the main behavioral nuance. However, the singular/plural ambiguity about return cardinality and the lack of guidance on how to discover valid columns leave the definition adequate but not fully 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 descriptions are absent (0% coverage), so the description must supply meaning for `column` and `value`. It explains that the column is compared exactly and case-insensitively, but it does not list valid column names or value formatting, and the schema's string type for value could be misleading for numeric 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 states that the tool returns rows of the PTOroom dataset filtered by an exact (case-insensitive) column/value match, which clearly distinguishes it from text-search siblings like dataset_search. It lacks an explicit verb like 'returns' or 'selects', but the meaning is 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 description gives no indication of when to prefer this tool over sibling tools such as dataset_search or dataset_compare, and does not state any exclusions or prerequisites. An agent must infer that exact-key lookup is the intended use case from the name and behavior 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 burden of behavioral disclosure. It does disclose the matching mode (case-insensitive substring containment) and the maximum result count ('up to 50'). It does not mention result ordering, whether all columns are returned, the default limit when omitted, or behavior when there are 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, front-loaded sentence: it names the resource first, then the containment condition, case-sensitivity, and cap. There is zero filler or redundant restatement 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 simple search tool with two parameters, this is minimally viable: an agent knows what is searched and the row cap. However, with no output schema, it should clarify whether returned rows include all columns and how results are ordered; it also leaves the default limit ambiguous. These gaps matter for correctly invoking and parsing the 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?

The description adds 'case-insensitive' to the query parameter, which the schema only describes as 'text to look for in any cell'—this is genuinely new semantic meaning. It also clarifies that the limit parameter caps returned rows at 50, giving purpose to a parameter with no schema description. It stops short of specifying the default limit when not provided.

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: it returns rows from the PTOroom dataset where any cell contains the query, case-insensitively, capped at 50. This clearly distinguishes it from siblings like dataset_row, dataset_stats, and dataset_top without needing to inspect 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?

No guidance is given about when to choose this tool over dataset_row, dataset_columns, or dataset_top. The description implies full-cell search but does not explicitly state alternative conditions or exclusions, so an agent must infer usage solely from the name.

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 disclosing behavior. It reveals important details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This goes beyond the bare operation and helps the agent understand edge-case handling.

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 the key statistics front-loaded and the parsing details in a parenthetical. Every word earns its place; there is no unnecessary fluff.

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 one-parameter tool with no output schema, the description covers the input, the computed metrics, and edge-case handling. It does not explicitly state the output format, but the enumerated statistics make it obvious. Overall it is sufficiently complete for an agent to call the tool 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 compensate. It clarifies that the 'column' parameter refers to a numeric column in the PTOroom dataset, adding meaning beyond the plain string type in the schema. It does not fully explain naming conventions or error behavior, but the core parameter semantics are clear.

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 computes count, min, max, mean, median, and sum for a numeric column, which is a specific verb and resource. It distinguishes itself from sibling tools like dataset_search and dataset_top by focusing on summary statistics rather than lookup or row retrieval.

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 clearly identifies when to use this tool: when summary statistics of a numeric column are needed. It does not explicitly mention alternatives or exclusions, but the purpose is stated directly enough that an agent can 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 full burden of behavioral disclosure. It discloses the core ranking behavior and ascending/descending intent, but it does not mention default limits, tie-handling, response shape, or what happens if the 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?

The description is a single, efficient sentence that front-loads the core behavior and adds a clarifying example. Every part earns its place; there is no wasteful verbiage.

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 relatively simple with three optional-ish parameters and no output schema, so a minimal description can be sufficient. Still, it lacks guidance on default limit behavior, expected output format, and how this differs from dataset_stats or dataset_row, making it only minimally 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 description coverage is low (33%), so the description must compensate. It adds the important meaning that 'column' must be numeric, but it does not explain 'limit' beyond the schema's min/max, and the 'ascending' semantics are mostly already covered by 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?

Description clearly states a specific operation: return the highest/lowest rows of the PTOroom dataset by a numeric column, with a concrete use-case phrase ('which is the most/least X'). It is clear and specific, but it does not explicitly contrast itself with sibling tools like dataset_stats or dataset_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 quoted use case 'which is the most/least X' gives an agent a clear context for when to invoke this tool. It does not provide explicit exclusions or mention alternatives, but the intended query scenario is understandable enough to guide selection.

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