site

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 does reveal the specific output types (columns, numeric flags, row count, provenance banner) and implies a read-only metadata operation, but it does not explicitly state safety, side effects, or any limitations. The lack of an explicit 'read-only' statement leaves a minor gap, though the tool's nature makes it unlikely to be misinterpreted.

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 short sentences with no filler. The first sentence enumerates the four output components in a clear list, and the second sentence adds a valuable usage directive. Every word earns its place.

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 parameterless, read-only schema tool, the description covers the key return elements: columns, numeric identification, row count, and provenance banner. The term 'provenance banner' is not fully explained, and there is no explicit mention of return format, but the description is adequate for an agent to decide to invoke it and interpret basic results.

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 provides no semantic content. The description compensates by detailing exactly what the tool returns, which helps an agent understand the result structure even without an output 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 that the tool returns columns, numeric flags, row count, and provenance banner for the Pickpathly dataset, which is a specific verb+resource. It does not explicitly differentiate from sibling tools like dataset_provenance, but the phrase 'Call this first to learn the schema' signals its distinct role as a schema discovery tool.

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' provides explicit when-to-use context, positioning it as the initial step before other dataset tools. It does not mention alternatives or exclusions, but the clear sequencing guidance is sufficient for a tool with no parameters.

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 the core selection logic (match any value in a column) and ordering (in the order given), which is helpful. However, it does not specify output format, exact-match semantics, case sensitivity, or behavior when no rows match.

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 conveys purpose, scope, ordering, and use case with no wasted words. Every clause adds information.

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 read-style tool with no output schema, the description is largely complete: it explains what rows are returned, in what order, and when to use it. Minor omissions around return representation and edge cases keep it from a 5.

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: 'column' is clearly the field to match on, and 'values' are the allowed match values with output order tied to their given order. This adds real meaning beyond the bare schema types.

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 (rows of the Pickpathly dataset) and the selection behavior (rows whose column matches any of the given values, in the order given). It is distinguishable from siblings like dataset_search or dataset_row, though it lacks an explicit action verb like 'returns' or 'fetches'.

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: use this when comparing specific values side by side. It does not explicitly name alternative tools or state when not to use it, but the intended scenario is clear enough.

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 disclosure burden. It names the exact information returned (source, computation date, licence, citation) and frames the operation as a read-only lookup. This is adequate for a simple metadata tool, though it does not explicitly state that no data is modified.

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 short sentences with no filler. The core content is front-loaded in the first sentence, and the second sentence adds practical guidance about when to use it. Every word earns its place.

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 tool, the description lists the important returned fields and the intended use case. It could be slightly more explicit about the output format, but the stated fields are sufficient for an agent to interpret the result and cite the dataset 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 tool has zero parameters, and the schema confirms this. According to the rubric, a no-parameter tool starts at baseline 4, and no parameter description is needed. Nothing is missing 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 clearly identifies the tool as providing provenance information for the Pickpathly dataset: source, computed date, licence, and citation. It is easily distinguished from sibling data-access tools like dataset_row or dataset_stats, though it lacks an explicit verb such as 'returns' or 'retrieves'.

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' gives a clear usage context: use this tool when citation or attribution is needed. It does not name alternatives or explicitly say when not to use it, but the context is specific enough for an agent to route correctly.

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 behavioral burden. It does disclose the key matching behavior: exact equality, case-insensitively. However, it does not state whether it returns one row or many, whether it is read-only, or what happens when no row 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?

The description is a single, direct sentence with no filler and front-loads the resource and operation. It earns high marks for conciseness, though it does not use additional structure to incorporate usage guidance or exclusions.

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 lookup tool, the description is adequate but not complete. It lacks explicit return-value details, multiplicity of results, guidance on sourcing column names, and clarity on edge cases like no matches or case-insensitive matching across all column types. The presence of sibling tools like dataset_columns and dataset_search adds context that could have been leveraged.

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 supply meaning for the parameters. It maps 'column' and 'value' into the equality predicate, which is helpful but minimal. It does not explain how to discover valid column names, how the value should be formatted, or any additional constraints beyond the schema's string type.

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 looks up rows in the Pickpathly dataset by exact column-value equality, including the case-insensitive nuance. It does not explicitly name sibling tools, but the exact-match semantics distinguish it from dataset_search. The title's singular 'row' and description's plural 'rows' create slight ambiguity about return cardinality.

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 exact-match and case-insensitive wording implies this tool is for precise lookups rather than fuzzy or partial searches. However, it does not explicitly say when to prefer this over dataset_search or other siblings, nor does it mention any exclusions or fallback conditions.

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 useful behavior: case-insensitive matching and a maximum of 50 rows. It does not disclose ordering, default limit behavior when omitted, empty-result behavior, or confirm that the operation is read-only, though the name 'search' strongly implies 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, compact sentence that front-loads the main behavior and adds the key constraint (case-insensitive, up to 50) without wasted words. Every word contributes to understanding the tool's behavior.

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 and no output schema, the description is mostly complete: it specifies what is searched, the matching rule, and the result cap. It is slightly lacking on default limit behavior and result ordering, but these are minor gaps given the tool's simplicity.

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 only the query parameter; the description adds case-insensitive matching semantics for query and clarifies the 'up to 50' cap tied to limit. It enriches the schema's sparse documentation, though it does not state the default limit when the optional limit parameter is absent.

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 searches the Pickpathly dataset for rows containing the query string in any cell, with case-insensitive matching. It communicates the core filtering behavior and result cap. It does not explicitly differentiate from siblings like dataset_row or dataset_top, but the resource and action are evident.

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 this tool is for free-text substring search across cells, which gives the agent a sense of when it applies. However, it provides no explicit guidance on when to prefer this over sibling tools such as dataset_columns or dataset_compare, and no exclusions or alternative conditions are mentioned.

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 present, the description carries the behavioral burden and handles it well. It discloses that grouping commas and currency are parsed, and that non-numeric rows are excluded and counted. This gives the agent meaningful expectations beyond the tool's basic purpose, though it does not describe output structure 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, dense sentence that front-loads the computed metrics and immediately follows with parsing/exclusion behavior. 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 one-parameter tool with no output schema and no annotations, the description covers the essential invocation context: what is computed, which dataset/column it applies to, and how messy data is handled. The only minor gap is the exact return format, but the listed metrics strongly imply the output contents.

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 defines column as a non-empty string, so the description adds valuable meaning: the column refers to a numeric column in the Pickpathly dataset, and its values may contain currency/grouping formatting. This helps the agent understand what input is valid and how it will be interpreted.

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 defines the tool's operation: computing count, min, max, mean, median, and sum for a numeric column of the Pickpathly dataset. This distinguishes it from sibling tools like dataset_search or dataset_row, and the title reinforces the exact resource and action.

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. However, it does not explicitly state when to prefer this tool over siblings such as dataset_top or dataset_compare, and there are no exclusions or alternative conditions mentioned.

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 state the core behavior: returning top or bottom rows by a numeric column. But it does not mention default limits, handling of non-numeric columns, tie behavior, or the exact shape of the returned rows, leaving several behavioral details implicit.

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 with no filler. The natural-language gloss "which is the most/least X" earns its place by making the tool's intent immediately accessible.

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 read-only ranking tool, the description covers the essential outcome and the numeric column requirement. However, with no output schema and no annotations, the absence of guidance on limit defaults, return structure, and edge cases leaves some gaps for an agent that needs to invoke it independently.

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 useful meaning by saying the column must be numeric and by linking "lowest" to the ascending flag. However, it does not add context for the limit parameter or clarify how column names are expected to be supplied, so compensation is only 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 that the tool returns the highest or lowest rows of the Pickpathly dataset by a numeric column, which is a specific operation on a specific resource. The phrase "which is the most/least X" further clarifies the intent and distinguishes it from sibling tools 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 "most/least X" framing implies the intended use case for ranking or finding extreme rows. However, the description does not explicitly explain when to choose this tool over siblings like dataset_stats or dataset_search, and it offers no 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.