site

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 does so by enumerating the returned content: columns, numeric columns, row count, and provenance banner. It does not describe output formatting or performance traits, but the central behavior is 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?

The description is two sentences with no filler. It front-loads the primary output content and ends with a direct usage instruction, 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 zero-parameter metadata inspection tool, the description gives enough context: what information is returned and the suggested first-call ordering. It does not detail the exact response format, but no output schema exists and the itemized list is reasonably 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?

The tool has zero parameters, so parameter semantics are inherently satisfied; the baseline for no-parameter tools is 4. The description adds no parameter confusion and the schema confirms an empty parameter set.

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 returns: columns, numeric column indicators, row count, and the provenance banner. The phrase 'Call this first to learn the schema' gives it a distinct role among siblings, though it does not explicitly contrast it with dataset_provenance 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 gives a clear usage cue: call this first to learn the schema. It does not explicitly name alternatives or say when not to use it, but the 'call this first' instruction provides clear context for when the tool is appropriate.

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 clearly explains the matching semantics ('column is any of the given values') and the ordering behavior ('in the order given'), which is valuable beyond the schema. It does not mention output format or edge cases, but the core behavior is 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?

A single sentence communicates the resource, filtering logic, ordering, and intended use case without any filler. It is compact and 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?

The tool is simple with only two parameters and no output schema. The description explains what is returned (rows), how they are filtered, and in what order, which is largely complete for an agent to invoke it correctly. Minor gaps such as exact-match semantics or output shape exist, but the core is well covered.

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 'column' is the field being matched and 'values' are the values to match, and that row ordering follows the values list ordering. This adds meaningful semantics beyond the bare 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 clear operation: retrieve rows from the Lobbyvo dataset where a column matches any of the provided values, preserving the given order. This is specific and informative, though it does not explicitly name or contrast 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 "for X vs Y questions" phrase gives a clear use-case context, signaling that this tool is for side-by-side comparisons of specific values. It does not mention exclusions or alternative sibling tools, so it stops short of full routing guidance.

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 discloses the contents of the provenance information, but does not describe the return format, whether it is a static metadata lookup, or any failure cases. This is adequate for a simple metadata tool but not richly 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 description is two sentences with no filler or redundancy. It front-loads the key content fields and then gives a direct usage instruction.

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 zero-parameter provenance tool, the description enumerates the expected fields and the purpose. While there is no output schema, the listed fields themselves give an agent enough understanding of what will be returned.

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 an empty input schema, so parameter documentation is unnecessary. The baseline of 4 applies because there is nothing for the description to compensate for.

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 exposing provenance metadata—source, computation date, licence, and citation—for the Lobbyvo dataset. It is distinguishable from siblings like dataset_row or dataset_stats by its focus on attribution rather than data access or analysis, though it lacks an explicit action verb.

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 'Read this to attribute a figure correctly' gives a concrete use case and tells an agent when this tool is relevant. It does not explicitly name sibling alternatives or state when not to use it, but the context is clear enough for a zero-parameter provenance lookup.

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 does add a useful behavioral trait: case-insensitive exact matching. However, it does not clarify whether one or multiple rows are returned (title says 'a row', description says 'rows'), nor does it describe limits, error behavior, or read-only guarantees. This is a partial disclosure for a simple lookup 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, compact sentence that quickly conveys the core behavior. It is front-loaded with the dataset name and matching logic, with no filler or redundant phrasing. The grammatical incompleteness is minor and does not hinder comprehension.

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 tool's simplicity, the description covers the essential invocation details: which parameters are used and the matching semantics. However, the absence of an output schema, combined with the singular/plural ambiguity, leaves the agent uncertain about the return shape. The description also does not mention prerequisites like knowing valid column names, which a sibling tool (dataset_columns) might provide.

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 explain the relationship between the two parameters ('column equals a value') and adds the case-insensitive detail, which is beyond the schema's bare string type and minLength constraints. However, it does not enumerate valid column names, value formatting, or edge cases, leaving some semantic gaps for the agent.

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 operation: returning rows from the Lobbyvo dataset where a column exactly matches a value, with a specific case-insensitive behavior. It distinguishes itself from a general search by emphasizing exact match, though it does not name a sibling alternative. The title's singular 'a row' slightly conflicts with the plural 'rows' in the description, creating minor ambiguity.

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 explicit guidance is provided about when to use this tool versus siblings like dataset_search or dataset_compare. The exact-match phrasing implies a use case, but the description does not state exclusions or conditions that would route an agent to an alternative. The agent must infer the appropriate context from the tool 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.

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

With no annotations, the description carries the burden and does add useful behavior: case-insensitive substring matching in any cell and a 50-row cap. However, it does not disclose ordering, default behavior when limit is omitted, pagination, or what happens on zero matches, so it is only partially 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?

One sentence covers resource, matching behavior, case handling, and result limit without filler. The most important information is 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 gives the core contract, but there is no output schema, no usage guidance, and no stated default for the optional limit. An agent can invoke it, but may not know whether one result set or paging is expected or which sibling to choose in ambiguous 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 50%; the query property has a description, but limit does not. The description adds case-insensitivity and the 50-row cap, which clarifies result behavior, but it does not explain the limit parameter's default or semantics beyond the schema's min/max.

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 concrete resource (the Lobbyvo dataset) and a precise behavior: returning rows whose cells contain the query, case-insensitively, up to 50. This is specific enough that an agent can distinguish it from siblings like dataset_columns or dataset_stats, even though no sibling is named.

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?

There is no guidance on when to prefer this tool over alternatives. The description implies a text-search use case but never states exclusions, alternatives, or conditions such as 'use dataset_row for a single row'.

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 meaningfully discloses behavioral details: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a bare statement of purpose, though it does not cover edge cases like null/empty values 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?

One focused sentence provides the core behavior first and then key parsing/exclusion details in a parenthetical. Every part adds value 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?

For a single-parameter numeric-column stats tool, the description covers the essential inputs, outputs, and notable parsing behavior. There is no output schema, but the listed statistics effectively describe the return values. Minor gaps include missing/null handling and exact output formatting, but these are not severe for this 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 only defines 'column' as a non-empty string with 0% schema description coverage, so the description must carry the semantic weight. It adds that the column must be numeric and that currency/grouping formats are parsed, but it does not clarify column naming conventions or how to obtain valid column names. The parameter is simple enough that the lack of examples is not critical.

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 of the Lobbyvo dataset. It names the specific resource and output metrics, making it easy to distinguish from sibling tools like dataset_row or dataset_columns.

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 when to use the tool: whenever summary statistics for a numeric column are needed. However, it does not explicitly state when not to use it or point to an alternative, such as dataset_top for non-numeric summaries or dataset_search for filtering.

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 explaining behavior. It does disclose that rows are ordered by a numeric column and that ascending/descending selection is possible. However, it does not mention limit semantics, tie handling, behavior on non-numeric data, or the return shape, leaving some behavioral gaps.

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 tight sentence plus a clarifying phrase. It is front-loaded with the core action and contains no filler or redundant restatement of 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?

For a simple three-parameter tool this is mostly adequate, but without an output schema and with sparse parameter documentation, the description could do more to explain what rows are returned, how limit behaves, and edge cases. It is functional 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 only 33%, so the description must compensate. It adds meaningful semantic value by specifying that column must be numeric and by mapping ascending/descending to lowest/highest. Yet the limit parameter's purpose is left entirely to inference from its name and schema constraints.

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 specify a clear verb and resource: rank rows of the dataset by a numeric column and return the highest or lowest records. The 'which is the most/least X' phrasing makes the purpose concrete and distinct from siblings 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 gives a clear use case: answering 'which is the most/least X' questions by ranking a numeric column. It does not explicitly name alternatives or exclusions, but the provided context is enough for an agent to know when this tool fits.

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