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

No annotations are provided, so the description carries the full burden. It discloses the concrete contents the call returns (columns, numeric indicators, row count, provenance banner) and frames it as an initial schema-discovery step. It does not explicitly say it is read-only, but the content makes that strongly 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?

Two short sentences, front-loaded with the output contents and immediately followed by a clear call-order instruction. No wasted wording.

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 description gives enough for an agent to know when to call this tool and what to expect: schema discovery via columns, numeric flags, row count, and provenance banner. It does not specify the exact response shape, but with zero parameters and no output schema, the coverage is adequate.

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 has zero parameters)Skip; there is no parameter detail to add. The description usefully names the target dataset ('GPA Grade Compare dataset'), providing context 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 names a concrete set of outputs—columns, numeric flags, row count, and provenance banner—for a specific dataset. It clearly identifies the tool as a schema-discovery call ('Call this first to learn the schema'), though it does not explicitly contrast it with similar siblings like 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 clear positioning within the toolset history, but it does not state when to prefer a sibling tool or when not to use this one.

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 transparently discloses two nontrivial behaviors: matching is by 'any of' the supplied valuesaft and returned rows follow the provided value order. It does not cover edge cases like missing values, duplicate handling, or exact-match case sensitivity, but the core behavior is clearly stated for a read-oriented comparison 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?

One tight sentence that front-loads the dataset and filter behavior, then appends the use case. There is no repetition of schema fields and no filler; every clause adds information an agent needs.

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 tool, this description covers the essentials: dataset, filtering column, value list, ordering, and intended comparison use case. The lack of an output schema leaves minor uncertainty about the exact rendered output, but the definition is adequate 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?

Schema coverage is 0%, so the description is the only source of parameter meaning. It explains that 'column' is the field being matched and 'values' are the list of allowed matches, and that the order of 'values' determines output order. It does not enumerate valid columns or matching details, but it goes well 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?

Names the exact resource ('GPA Grade Compare dataset') and the selection rule: rows whose column matches any of the given values, in the given order. The 'X vs Y' framing and title clearly indicate a side-by-side comparison tool, distinguishing it from generic row/search siblings. It lacks an explicit action verb and direct sibling comparison, so it stops short of a top score.

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 concrete trigger condition: 'for "X vs Y" questions.' This implies it should be used when comparing a small set of values side by side. It does not name alternative tools or explicitly state when not to use it, leaving only implied excluding 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 disclosure burden. It indicates an informational read operation and lists the returned content, but does not mention format, whether the dataset name is fixed, or any other operational details. For a parameterless lookup this is acceptable but not rich.

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 short sentences, front-loaded with the key content list and followed by a practical usage cue. Every word earns its place; no redundancy or 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 zero-argument metadata lookup with no output schema, the description is complete: it names the dataset, the kind of data returned, and the intended use case. Nothing else is required to invoke it successfully.

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 all schema coverage is effectively complete non-information, so no parameter explanation is needed. The description still contributes by naming the specific pieces of provenance returned.

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 distinct resource ('the GPA Grade Compare dataset') and precisely states what the tool provides: source, computed date, licence, and citation. This clearly separates it from dataset_columns, dataset_stats, and the other 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 final sentence, 'Read this to attribute a figure correctly,' tells the agent when to use the tool. It does not explicitly list when not to use it or name alternatives, 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, the description must carry behavioral weight. It discloses the matching semantics: exact equality, case-insensitive, potentially multiple rows. It does not disclose no-match behavior, returned fields, ordering, or pagination.

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 concise, front-loaded sentence states the dataset, operation, and key matching rule with no filler. The case-insensitivity qualifier is efficiently included.

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 exact-match lookup this is mostly sufficient, but it omits behavior when no row matches, whether multiple matches are returned, and any row limit or ordering. No output schema or annotation compensates for that.

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 low, and the description does clarify that 'column' and 'value' participate in an equality comparison. However, it does not explain valid column names, expected value types, or behavior with ambiguous/multiple matches.

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 uses a specific operation ('look up') with a clear resource ('GPA Grade Compare dataset') and a precise condition ('where a column equals a value exactly'). The phrase 'exact key' helps define scope, though it doesn't explicitly distinguish itself from sibling lookup/search 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 word 'exactly' and 'case-insensitive' imply when this tool is appropriate: exact-match lookups rather than fuzzy or range searches. However, no alternatives or exclusions are named, so the guidance is only implicit.

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 and does disclose core search semantics: substring containment, case-insensitivity, and the 50-row cap. It does not mention result ordering, pagination beyond the cap, or whether full row objects are returned, but it is still informative for a simple read-only search.

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 resource, the matching rule, the case-sensitivity behavior, and the result cap. There is no filler or redundant repetition of schema details.

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, flat search tool, the description covers the target dataset, match condition, case sensitivity, and upper bound. The absence of an output schema is partially addressed by saying 'Rows,' but the lack of output-shape details and pagination behavior prevents a perfect score.

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% because 'limit' lacks a description. The tool description partially compensates by saying 'up to 50,' which hints at the limit parameter's effect, and the case-insensitive clause adds query semantics. However, it does not state the behavior when limit is omitted or explicitly connect 'up to 50' to the limit parameter, leaving some ambiguity.

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 a precise behavior: return rows from the GPA Grade Compare dataset where any cell contains the query, with case-insensitive matching and a 50-row cap. This clearly identifies the tool's purpose and differentiates it from sibling tools that handle columns, stats, provenance, or single-row access.

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 explicit guidance on when to use this tool versus alternatives like dataset_row, dataset_top, or dataset_stats. The description implies row-level text search but provides no exclusions or routing hints, leaving the agent to infer the appropriate context.

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 burden and delivers meaningful behavior: 'grouping commas and currency are handled; non-numeric rows are excluded and counted.' This discloses edge-case handling (format parsing and the dual exclusion/counting of non-numeric data) that the schema cannot convey. It stops short of describing the exact return shape, but for a read-only aggregator this is solid 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?

A single sentence of roughly 25 words front-loads the deliverable (the six statistics) before trailing with qualifiers. Every clause earns its place — the formatting claim and the non-numeric row policy are compact and non-redundant. There is 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 low-complexity single-parameter tool with no output schema, the description is nearly complete: it states the computed statistics, the input expectations, and the edge-case handling. The one residual gap is that it never spells out the return format, though the enumerated stats reasonably imply the response fields. Overall adequate coverage for 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?

Schema description coverage is 0%, so the description must compensate. It adds the useful constraint that the column must be numeric and references the GPA Grade Compare dataset, which the bare schema (a single string 'column') does not. However, it does not enumerate valid column values, and it never points to dataset_columns as the way to discover them, so it only partially compensates for the zero-coverage 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 names a specific verb (compute) and a precise resource: 'count, min, max, mean, median and sum of a numeric column of the GPA Grade Compare dataset.' Listing the exact statistics removes any ambiguity about output scope. It also clearly differentiates from sibling tools like dataset_columns, dataset_row, and dataset_search, which all imply raw data access rather than aggregation.

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 no guidance on when to choose this tool over siblings. It never mentions that dataset_columns should be consulted to discover valid numeric columns, nor does it contrast with dataset_top or dataset_compare, leaving an agent to infer selection intent from the stats list 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?

No annotations are present, so the description must disclose behavior. It states the tool returns highest or lowest rows, covering the ascending parameter contract. However, it does not mention error cases (e.g., invalid column, no data), how ties are resolved, or how many rows are returned when 'limit' is omitted. The behavioral disclosure is basic but not 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 one concise sentence with a clarifying 'which is the most/least X' addendum. It uses no words wastefully and front-loads the core ranking behavior. It could be slightly more structured with separation between operation and example, but it is appropriately sized.

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 output schema and no annotations, the description should clarify return format and edge cases. It does not state that the tool returns rows, their structure, or what happens with a non-numeric column. It also names a specific dataset but gives no context about that dataset's schema. For a 3-parameter tool, this description is thin.

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' having a description. The description adds that the column must be numeric, which helps, but it does not explain the 'limit' parameter, its default, or the meaning of the boolean. The description does not compensate enough for the lack of schema descriptions.

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 'rank' and a resource: rows of the GPA Grade Compare dataset by a numeric column. The phrase 'which is the most/least X' clarifies the intended outcome. It does not explicitly name sibling tools but the focus on top/bottom rows differentiates it from the other dataset 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 a clear use case ('which is the most/least X') but does not explain when to prefer this over alternatives like dataset_search, dataset_stats, or dataset_row. There is no explicit when-not or exclusions, so it only implies usage rather than defining decision boundaries.

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 present, so the description carries the behavioral burden. It clearly discloses that 'Nothing is bought, ordered or paid; no quote is guaranteed; it is free,' and lists what the returned description covers: who receives details, consent wording, and confirmation method. It does not explicitly say the tool itself has no side effects, but the read-only describing nature is strongly implied.

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 compact and front-loaded with 'Read first,' and each sentence adds distinct information about the enquiry behavior and return content. Minor filler like 'States plainly' and overlap with the title keep it from a 5.

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-argument documentation tool, the description is nearly complete: it explains what the tool does, what it returns, and the key caveats about cost, purchase, and quote guarantee. The lack of an output schema or an explicit example of the returned description is a minor gap.

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 0 parameters and schema coverage is 100%, so there is no parameter burden to carry. The baseline of 4 applies; no additional parameter explanation is 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 makes the tool's purpose explicit: it 'States plainly what submit_enquiry does' rather than performing an enquiry, and it frames the output as describing a human enquiry flow where providers quote directly. It also distinguishes this from a purchase or guaranteed quote, so an agent can tell it apart from the sibling submit_enquiry.

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 only usage cue is 'Read first,' which implies consulting this before acting, but the description never states when to choose this tool over siblings like submit_enquiry or enquiry_fields. There is no explicit alternative condition or exclusion, so an agent must infer the intended sequencing.

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 explains what data is exposed (key, label, type, help text, allowed options) but never explicitly states that this is a read-only metadata call, how the data is ordered, or whether the field set varies. The behavior is largely implied rather than disclosed.

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 tight sentences. The first enumerates the contents; the second gives the practical downstream use. No filler, no repetition, and the most important identifying 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 zero-parameter, read-only metadata endpoint, the description covers the scope ('GPA Grade Compare enquiry'), the contents, and the relationship to a sibling tool. There is no output schema, so explaining the field attributes here is sufficient context for an agent to call and use the result.

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 there is little for the description to add. It still adds useful cross-tool semantics by explaining that the field key is what should be used to key answers in submit_enquiry, which helps an agent connect the two tools.

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 as 'GPA Grade Compare enquiry fields' and enumerates exactly what it contains (key, label, type, required, help text, allowed options). It lacks an explicit verb like 'list' or 'return,' but the meaning is unambiguous. It distinguishes itself from dataset-focused siblings by anchoring to the enquiry and to submit_enquiry.

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 actionable guidance: retrieve these fields so answers can be passed to submit_enquiry using field keys. This signals the intended workflow. It does not explicitly state when not to use this tool or contrast it with enquiry_describe/dataset_columns, so it misses the highest bar.

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 and does so thoroughly. It discloses that this is not a purchase or guaranteed quote, that consent must match the exact quoted statement, that a confirmation token is required, and that providers only see the enquiry after the person clicks an email link.

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 long but dense; every sentence contributes workflow logic, consent requirements, or behavioral caveats. The step labels and quoted consent line are essential content, with no redundant or filler 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?

There is no output schema, yet the description explains what Step 1 returns (summary, consent line, confirmation token) and what Step 2 produces (email link before provider access). This gives the agent everything needed to invoke the tool correctly and understand downstream effects.

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 100%, but the description adds substantial meaning: answers must be keyed by field keys from enquiry_fields, consent must be true and match the specified text, and confirmation is the token from Step 1. This directly links each parameter to the two-step workflow.

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 states the action ('submits an enquiry to GPA Grade Compare'), the target ('human providers'), and the key exclusions ('NOT a purchase, NOT a guaranteed quote'). The two-step nature is named in both title and description, distinguishing it from sibling tools like enquiry_describe and enquiry_fields.

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?

Provides an explicit step-by-step procedure: Step 1 validates and returns a summary/confirmation token; Step 2 is used only if the person agrees, with the same answers plus the token. It also states the prerequisite of using field keys from enquiry_fields and clarifies when the tool should not be used.

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