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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 reveals the tool's read-only informational nature by describing its output, but it does not explicitly state that it has no side effects or mention any operational constraints.

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 packs all necessary information—output contents and usage guidance—without any filler. The key details are front-loaded and every clause 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?

Given no parameters, no output schema, and no annotations, the description fully covers what an agent needs: what the tool returns and when to call it. Nothing essential is missing for correct selection and invocation.

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 100%, so there is nothing for the description to add about parameters. The baseline of 4 for a no-parameter tool is appropriate.

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 indicators, row count, and provenance banner—for the Perdiemo dataset, and states its role as the schema-discovery entry point. This clearly distinguishes it from siblings like dataset_provenance 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 instruction 'Call this first to learn the schema' provides explicit usage context and sequencing relative to other tools. It does not enumerate alternatives or exclusions, but the guidance is clear and actionable.

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 that the tool filters rows by a column matching any of the values and preserves the input order, which are non-obvious behaviors. However, it does not specify exact-match behavior, case sensitivity, or the format of the returned comparison, so transparency 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?

The description is a single sentence and is concise, but it packs multiple ideas into awkward syntax, making 'whose column is any of the given values' potentially confusing. Better structure would clearly separate the filtering condition from the ordering guarantee.

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 2-parameter query without an output schema, the description provides the essential selection logic and ordering guarantee. It omits output format, matching semantics (exact vs partial), and any edge-case behavior, so an agent is left to infer the response shape. Overall it is 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 description coverage is 0%, so the description is the only source of meaning. It explains that 'column' is the field used for filtering and 'values' are the set of values to match, and that output ordering follows the values order. This compensates for the lack of schema descriptions, though the phrasing is slightly ambiguous ('whose column is...' instead of 'whose value in the column is...').

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: retrieving rows from the Perdiemo dataset filtered by a column matching any of the supplied values, in the given order. The title 'Compare rows side by side' clarifies its comparison intent, which distinguishes it from siblings like 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' gives a direct when-to-use cue for comparison scenarios. It does not name alternative tools or explicitly state when not to use it, but the context is clear enough for an agent to select it for side-by-side comparisons.

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 has to carry behavioral disclosure. It communicates that the tool returns provenance metadata and implies a read-only citation lookup, but it does not describe the response format or any operational caveats.

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 covers the tool's content and its intended use. The title adds useful context 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 parameterless metadata lookup, the description covers the essential content and the main use case. There is no output schema, so a touch more detail about the exact return shape would make it fully complete, but nothing necessary to invoke the tool 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?

There are no parameters, so the description does not need to document them. It still adds meaning by specifying what fields the provenance result covers, beyond the empty 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 explicitly names the resource (Perdiemo dataset) and the content it provides (source, computation date, licence, citation). It lacks an explicit verb describing the retrieval, but the title and 'Read this to attribute a figure correctly' make the tool's function clear.

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?

It gives a concrete trigger: call this when a figure needs to be attributed to the Perdiemo dataset. It does not name sibling alternatives or exclusions, but for a zero-parameter provenance tool the guidance is sufficient.

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 does disclose two meaningful behaviors: exact equality and case-insensitive matching. It does not clarify whether all matching rows are returned, what happens when there is no match, or how the result is shaped, leaving some ambiguity.

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 states the resource, match criterion, and case sensitivity with no repetition or filler. It is appropriately sized for a simple two-parameter lookup tool.

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 and has only two string parameters, but an agent is still left to infer multiple-match behavior, no-result behavior, and which columns are valid on the Perdiemo dataset. This is adequate for a first attempt 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 coverage is 0%, so the description must add meaning to the bare 'column' and 'value' properties. It explains that the column must equal the provided value exactly and case-insensitively, which is useful relational context, but it does not enumerate valid column names or 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 title 'Look a row up by an exact key' supplies a specific action, and the description names the resource and predicate: rows of the Perdiemo dataset where a column equals a value exactly. The explicit exact-match and case-insensitive semantics distinguish it from sibling dataset_search without needing to open the schema.

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?

Use is implied by the exact-match semantics: this is the tool to call when you need a row by an exact key value. However, the description gives no explicit when-not-to-use guidance and does not point to dataset_search for fuzzy or partial matches.

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 of behavioral disclosure. It usefully reveals case-insensitive matching and the 50-row ceiling, but does not mention output shape, ordering, match semantics beyond substring containment, or pagination 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?

A single sentence that is front-loaded with the core behavior and contains no filler. Every phrase 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 simple two-parameter search tool, the description covers the essential semantics: what is searched, how matching works, and the result cap. It is slightly incomplete regarding the exact return format and default limit, but this is minor for the tool's complexity.

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 50%, with query documented but limit only given as a numeric range. The description's 'up to 50' adds some meaning to the limit parameter, but it does not explicitly explain how the limit parameter controls the result count or what the default behavior is.

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 behavior: return rows of the dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from siblings like dataset_row (specific row), dataset_top (top rows), and dataset_stats (statistics).

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 use case is implied: use this when you need rows matching arbitrary cell content. However, it does not explicitly name alternatives or state when not to use it, leaving some inference to the 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 provided, the description carries the full behavioral burden. It discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which is genuinely useful. It does not describe the exact response structure or edge cases like an all-non-numeric column, but the main behaviors are 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 entire definition is a single, well-structured sentence that front-loads the computed statistics, then adds the dataset scope and data-cleaning behavior in a parenthetical. There is no redundancy with the schema and no wasted words.

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 only one parameter and no output schema or annotations, the description covers what is computed, on which dataset, and how malformed values are treated. A small gap remains around the exact return format and edge-case behavior, but the definition is largely complete for a simple stats 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 input schema only says the parameter is a non-empty string. The description adds meaningful semantics: the column must be a numeric column of the Perdiemo dataset and will be cleaned before computation. It could have mentioned how to discover valid column names, but it provides substantial guidance 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 description specifies the resource (a numeric column of the Perdiemo dataset), the operation (summary statistics), and explicitly enumerates the outputs: count, min, max, mean, median, and sum. This clearly distinguishes it from sibling tools like dataset_columns, 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?

It clearly implies when to use the tool: when summary statistics of a numeric column are needed. It also explains how dirty numeric values are handled. However, it does not explicitly name alternative sibling tools or state when not to use it, so it falls short of a 5.

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 mentions sorting by highest/lowest but doesn't disclose handling of ties, null values, whether all columns are returned, or if the tool is read-only. The schema has no descriptions for column and limit, and the description adds little beyond the basic operation.

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 front-loads the primary purpose and includes a helpful example in plain language. It contains no filler, though it is arguably too brief to convey all necessary context.

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, but with no output schema and no annotations, the description leaves critical gaps: it does not specify what the returned rows look like (all columns or just the ranking column), whether ties are broken, or whether only numeric columns are valid. An agent cannot fully predict the behavior from this description alone.

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 does not explain 'column' beyond implying a numeric column, nor does it clarify 'limit'. It adds no parameter-level detail beyond what the schema already provides, so it fails to compensate for the low coverage.

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 ranks rows by a numeric column and identifies it as returning the highest or lowest rows, with a helpful plain-language gloss ('which is the most/least X'). It distinguishes itself from siblings like dataset_search and dataset_stats, though it doesn't explicitly say it returns full rows.

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 alternatives. There is no mention of alternatives or conditions that would select this tool over dataset_stats or dataset_search. The description only explains what it does, not when it's appropriate.

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