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

With no annotations, the description carries the full burden, and it does well by enumerating exactly what information the tool returns. As a zero-parameter read-only metadata operation, there are no side effects or hidden requirements to disclose; the only minor gap is not stating that it is read-only, but the content 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?

Two sentences with no filler. The first sentence front-loads the exact output contents, and the second gives a clear action 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 simple, zero-parameter metadata tool, the description is complete enough: it specifies the dataset, the exact fields returned, and the recommended call order. The lack of an output schema is not a problem since the description already states the returned elements.

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 baseline for zero-parameter tools is 4. The description appropriately focuses on the output rather than inputs, and no parameter semantics are needed.

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 flags, row count, and provenance banner — for the BioBricks dataset. It also states a clear directive ('Call this first to learn the schema'), which sets it apart from sibling tools like dataset_stats or dataset_provenance by identifying its role as the schema-discovery entry point.

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 guidance on when to invoke this tool before other dataset tools. It does not explicitly list exclusions or alternatives, but the 'first' ordering and the schema-learning purpose make the usage context clear.

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 that rows are filtered by 'any' matching values and that returned order follows the input value order. Still, it leaves exact-match semantics, case sensitivity, and the output/row shape unspecified, which an agent would need to predict results precisely.

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 dense sentence with no filler: the core behavior and ordering constraint are front-loaded, followed by the use-case cue. Every phrase earns its place, and nothing unnecessarily repeats 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 two-parameter read-style comparison tool with no output schema and no annotations, the description is minimally viable: it states what is returned, how rows are selected, and when to use it. The missing output-shape details and exact-match semantics are real gaps, but the simple filter-and-order behavior keeps it usable.

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 filtered and that 'values' are the match values whose order controls result ordering. It does not explain exact matching behavior or the min/max value constraints, leaving some semantics to the schema's type definitions.

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 operation: return BioBricks rows whose column matches any of the supplied values, preserving the order of the given values. The title 'Compare rows side by side' adds a clear comparative intent that distinguishes it from generic fetch, search, or stats tools. It does not explicitly contrast sibling tools, so it stops short of a 5.

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 an explicit scenario for choosing this tool over one-off row or aggregate queries. However, it does not mention any sibling tools or provide negative guidance about when not to use it, so it misses the full alternatives criterion.

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. It implies a non-mutating read operation with 'Read this', and lists the output content, but it does not describe behavior such as errors, availability, or absence of side effects. For a zero-parameter provenance query, this is acceptable but not fully 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 a single efficient sentence that front-loads the content and ends with the practical use case. Every clause earns its place, and there is no redundant repetition of the tool name or title.

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 no-parameter, no-output-schema tool, the description is sufficiently complete: it states what information is provided and why an agent would use it. It could add a bit more about citation format or licensing specifics, but nothing critical is missing for correct 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?

With zero parameters, the schema provides no semantics to complement. The description adds meaningful context by enumerating exactly what information will be returned (source, date, licence, citation), which fully compensates for the absence of parameters.

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 exposes: source, computation date, licence, and citation for the BioBricks dataset. It is distinct from sibling tools like dataset_search or dataset_stats because it focuses on provenance and attribution, though it does not explicitly name a sibling as the alternative.

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 an explicit use case for when this tool is appropriate. It does not discuss when not to use it or compare against sibling tools, but the context is clear enough for a simple read-only provenance tool.

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 must disclose behavior. It mentions case-insensitivity and that it returns rows (plural) matching the condition, but it does not state whether it is read-only, how results are ordered, or what happens if no matches exist. This is a minimal but non-contradictory 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?

The description is a single sentence, but it is not front-loaded with a clear verb; it begins with 'The rows of...' which is passive and slightly awkward. It conveys the necessary information without excess, but could be more direct and structured.

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 output schema, no annotations, and 0% schema coverage, the description is thin. It does not describe the return format (full rows, field names), any limits, or error behavior (e.g., no match). For a lookup tool, an agent would need to know whether it returns one or many rows, which is implied by 'rows' but not confirmed. It also lacks examples or guidance on column naming conventions. Overall, it is inadequate for a tool with such minimal structured support.

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 0% description coverage, so the description must explain the parameters. It clarifies that 'column' is a column name and 'value' is the comparison value, and that the match is exact and case-insensitive. However, it does not specify valid column names, value formatting, or that the column must exist in the dataset. It provides basic meaning but not enough to fully compensate for the schema gap.

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 action (retrieving rows) and the target (BioBricks dataset) with a specific matching condition (exact, case-insensitive). It distinguishes from fuzzy search by emphasizing 'exactly', though it doesn't name the sibling tool. The phrasing is slightly awkward but the intent is 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?

The description implies that this tool is for exact matches and case-insensitive lookups, which hints that dataset_search is for fuzzy queries. However, it does not explicitly state when to prefer this over siblings or any exclusions. The guidance is implied rather than stated.

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. It usefully discloses case-insensitive matching, cell-level containment, and the 50-result cap, which go beyond the input 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?

One sentence delivers the core behavior with no fluff. The distinguishing details—case-insensitivity, cell matching, and limit—are front-loaded and each one 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 search tool, the description covers the essential behavior: what is searched, what is returned, and the result cap. It would be slightly stronger with an explicit default-limit note, but nothing critical is missing for invoking a basic search.

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 only 50%, leaving the 'limit' parameter undocumented in the schema. The description partially compensates with 'up to 50' and clarifies query behavior, but it does not explain the default limit or how the 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 states exactly what the tool does: returns rows of the BioBricks dataset whose cells contain the query, case-insensitive, capped at 50. This clearly separates it from 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 intended use is implied: use this when you need text search across dataset cells. However, it does not explicitly say when not to use it or name alternatives, leaving some selection reasoning 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does reveal that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. However, it omits the output format (e.g., JSON structure with field names), error behavior for entirely non-numeric columns, and whether the tool requires the column to exist. These are significant gaps for an unannotated 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, dense sentence that efficiently lists the statistics, specifies the dataset, and includes the two data-cleaning behaviors in parentheses. It front-loads the core purpose and avoids any fluff or repetition, making it highly concise and well-structured.

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 (one parameter, no output schema), but the description is not fully complete for an agent. It covers what statistics are computed and how data is cleaned, but it does not specify the return format (e.g., JSON keys) or behavior for edge cases like an empty column or all-non-numeric data. Given no output schema and no annotations, the description should provide more operational detail to be fully actionable.

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 string with minLength 1, with no description. The description adds that the column must be numeric and that formatting (commas, currency) is handled, which clarifies expected input semantics. This meaningfully compensates for the 0% schema description coverage, though it does not explicitly state that the value should be a column name from 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 clearly states the operation: computing summary statistics (count, min, max, mean, median, sum) for a numeric column of the BioBricks dataset. It lists specific metrics and mentions handling of formatting and non-numeric rows, which differentiates it from sibling tools like dataset_row or dataset_search that retrieve data rather than aggregate.

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 provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as dataset_columns, dataset_top, or dataset_search, nor does it state conditions that would favor one over the other. The intended use case is implied (when summary stats are needed) but not explicitly articulated.

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 must carry behavioral disclosure. It states the basic sort/rank behavior and high/low orientation, but does not mention default limit, tie handling, error behavior for non-numeric columns, or what the returned rows look like.

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 with a helpful illustrative phrase. It is front-loaded with the tool's core behavior and contains 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 simple 3-parameter tool this is adequate but not complete: an agent can infer the call shape, but the optional `limit` behavior, default output size, and returned row format are left unspecified.

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 only 33%, so the description needs to add parameter meaning. It adds the useful notion that `column` must be numeric and that high/low corresponds to `ascending`, but it does not clarify the meaning or default of `limit`.

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 a ranking operation ('highest (or lowest) rows ... by a numeric column') and attaches it to the BioBricks dataset. It distinguishes the tool from sibling search/stats/row tools conceptually, though it does not name any sibling explicitly.

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', implies when to use the tool for ranking questions. It does not provide explicit guidance on when not to use it or which sibling (e.g., dataset_search, dataset_stats) would be a better alternative.

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