site

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

With no annotations provided, the description carries the burden of explaining behavior. It discloses the main outputs: columns, numeric indicators, row count, and provenance banner. It does not discuss side effects or failure modes, but as a zero-parameter read-style metadata tool, this level of transparency is adequate.

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 wasted words. It front-loads the core output contents first and then gives clear usage guidance. 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 introspection tool with no parameters and no output schema, the description covers enough: what the tool returns and when to call it. It leaves minor details like exact output formatting unspecified, but the essentials for correct invocation are present.

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. There are no parameter semantics to clarify, and the description appropriately focuses on the output rather than inputs.

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's purpose: learn the schema of the Hydrantly dataset by returning columns, numeric flags, row count, and provenance banner. It is specific about the resource and what is returned, though it does not explicitly differentiate itself from sibling tools like dataset_provenance, which may overlap on the provenance banner.

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' gives a clear and explicit usage context: this is the entry point for understanding the dataset. It does not mention alternatives or when not to use it, but for a zero-parameter schema-inspection tool this 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, the description carries the behavioral disclosure burden and does disclose useful semantics: matching is by 'any of' the given values and output order follows the given values. It does not, however, describe the returned structure, side-by-side formatting, or any limitations such as duplicate 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 no filler. It front-loads the resource and action, then adds the key behavioral detail about ordering and the intended comparison 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?

For a simple two-parameter selection tool, the description explains what rows are returned, how matching works, and the ordering behavior. It does not fully address output formatting or edge cases, but the scope is modest and the core information is present.

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, and it largely does: 'column' is the field to match on and 'values' are the values used for matching, with order affecting output order. This adds meaningful semantics beyond the bare property names in 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 states a specific operation: selecting rows from the Hydrantly dataset whose column matches given values, in a specified order, and ties it to 'X vs Y' comparison questions. This is clear and useful, though it does not explicitly name sibling tools or contrast itself with them.

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 implied guidance about when to use the tool, and the ordering requirement suggests a comparison-oriented use case. However, there is no explicit statement about when not to use it or which alternative tool should be chosen 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?

With no annotations, the description carries the behavioral burden. It states what the tool returns and that it is meant for reading, implying a safe, read-only operation. It does not explicitly mention side-effect absence, but the described purpose makes that 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 compact sentences, no filler: the first lists the return content, the second states the intended use. Key information is front-loaded and every sentence 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 metadata tool with no output schema, the description is complete. It names the dataset, the specific metadata fields, and the scenario in which the tool should be used, leaving no critical gap for 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?

There are zero parameters, so the schema already fully covers this dimension. The description correctly focuses on the returned provenance information rather than adding unnecessary parameter detail.

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 identifies a specific resource (the Hydrantly dataset) and a distinct purpose: retrieving source, computed date, licence, and citation. It clearly separates this metadata tool from the sibling data-exploration 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 an explicit trigger: 'Read this to attribute a figure correctly.' It does not explicitly name alternatives or exclusions, but the attribution use case is distinct enough for an agent to know when to invoke it.

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 usefully discloses that matching is exact but case-insensitive. However, it leaves ambiguity about whether multiple matching rows are returned (title says 'a row', description says 'rows') and does not mention limits, errors, or read-only 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 compact sentence with no filler, and the core matching behavior is front-loaded. The phrasing is grammatically awkward ('The rows... where a column equals'), but it earns a high score for efficiency.

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, the description gives the essential operation and the case-insensitivity behavior. However, the singular/plural mismatch, lack of output format details, and absence of any disambiguation against sibling search tools leave the agent with an incomplete picture.

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 is the only source of parameter meaning. The phrase 'a column equals a value' maps directly to the 'column' and 'value' parameters, indicating their roles. Yet it does not explain column naming format, whether any column can be used, or value formatting beyond the schema's generic 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 resource (rows of the Hydrantly dataset) and the operation (filter where a column equals a value exactly), adding the case-insensitive qualifier. The title 'Look a row up by an exact key' reinforces the purpose. It is not a perfect 5 because it lacks an explicit verb in the description and does not explicitly contrast with the sibling search 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?

No guidance is given about when to use this tool versus dataset_search, dataset_top, or other siblings. The exact-match condition implies a use case, but there are no stated exclusions or alternative routing instructions, leaving the agent to infer when this 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 reveals case-insensitive matching, substring containment, and a 50-row cap, but does not mention return structure, ordering, default limit behavior, or empty-result behavior, leaving notable 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, focused sentence that front-loads the core behavior and constraints. Every word contributes to the tool's meaning, with no redundant phrasing.

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 matching semantics and result cap. However, without an output schema it does not explain the shape of returned rows, the default or optional limit behavior, or ordering, so an agent is left with some uncertainty when invoking it.

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 at 50%: query has a description, limit does not. The description adds the case-insensitive detail for query and indicates an upper bound of 50 results, which partly compensates for the undocumented limit parameter, though it does not state a default or how limit interacts with the cap.

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 does: it returns rows of the Hydrantly dataset that contain the query in any cell, case-insensitively, capped at 50 results. This is specific enough to distinguish it from sibling tools like dataset_row or dataset_top, although it does not explicitly name or contrast them.

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 usage context is implied rather than stated: use this tool when you need to search across cells for a text query. There is no explicit guidance about when not to use it or when a sibling tool would be preferable, so the agent must infer the appropriate choice.

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 burden of behavioral disclosure. It meaningfully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which is valuable beyond the title. It does not mention read-only status or edge cases like empty columns, but the disclosed parsing behavior is a strong positive.

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 primary functionality and adds important parsing/exclusion details in a parenthetical. There is no redundant or filler text; every phrase 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 one-parameter tool with no output schema, the description is largely complete: it enumerates the computed statistics and key data-cleaning behaviors. Minor gaps remain around edge cases such as all-non-numeric columns or nonexistent column names, but these are not critical for basic 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 schema has only a 'column' string parameter with no description, and schema description coverage is 0%. The description clarifies that the column must be numeric and that values like commas/currency are parsed, but it does not specify how column names should be provided, whether they are case-sensitive, or what values are valid.

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 (Hydrantly dataset) and the exact set of statistics computed (count, min, max, mean, median, sum) for a numeric column. It is specific about what the tool does, though it lacks an explicit verb and does not differentiate itself from sibling tools like dataset_top or dataset_compare.

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 use is implied: use this when you need summary statistics for a numeric column. However, there is no explicit guidance about when to prefer this over sibling tools, nor any exclusions or alternative recommendations.

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 names the ordering behavior (highest/lowest) but omits limit defaults, tie handling, null behavior, and whether this is a read-only query. Given zero annotation support, this is a significant gap.

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 one terse, front-loaded sentence that captures the core ranking behavior and includes a concrete example question. There is no filler, no repetition of the tool name, and 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?

With no annotations and no output schema, the description must supply essential invocation context. It leaves out the default limit and ordering behavior, does not explain the returned row shape, and gives no guidance on when to use this rather than sibling tools.

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%, with only 'ascending' documented. The description adds that the column must be numeric, but it says nothing about the limit parameter's default or maximum and does not enrich the meaning of 'ascending' beyond what the schema already states.

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 a verb and resource: it returns the highest or lowest rows of the Hydrantly dataset by a numeric column, answering 'which is the most/least X.' This distinguishes it from row/search/stats siblings by its ranking focus, though it does not explicitly name a sibling.

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 'which is the most/least X' implies a concrete use case, so an agent can infer when to use this tool. However, there is no explicit guidance about when to prefer this over dataset_search, dataset_stats, or dataset_row, and no exclusions or prerequisites are mentioned.

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