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

No annotations exist, so the description carries the full burden, and it does disclose the exact contents of the response (columns, numeric flags, row count, provenance banner). It says nothing about whether this is a pure read, whether the dataset must already be loaded, or any auth/cost implications, which is a real gap for a zero-annotation 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?

Two short sentences, zero filler, and the payload description is front-loaded before the usage directive. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must describe the return value, and it does so by enumerating the four things returned. Only the read-only/no-side-effect guarantee and any precondition on dataset load state are missing, which are minor for a metadata inspection call.

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 takes no parameters, so there is no parameter meaning to add; the baseline for a zero-parameter tool applies. The description correctly spends no words on 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 names the specific resource (columns, numeric flags, row count, provenance banner) so an agent knows exactly what comes back, which separates it from dataset_stats and dataset_provenance. The verb is only implied rather than stated, but the enumerated payload is unusually concrete.

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?

'Call this first to learn the schema' gives an explicit ordering instruction that positions it ahead of the other dataset_* tools. It does not name a situation where you would skip it or route to a named alternative, so it stops short of full when/when-not 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 full behavioral burden, and it only discloses that results preserve the given value order. It says nothing about whether this is a safe read, how missing values are handled, result limits, or whether the comparison returns aligned side-by-side output as the title promises.

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 compact sentence that front-loads the dataset scope and ends with the use-case hint; nothing is padded. It loses a point only for being a sentence fragment that reads awkwardly.

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, non-nested tool with no output schema, the description covers the basic input contract and ordering behavior but leaves return shape and edge-case handling unstated. There are no annotations to fill those gaps, so the definition is only minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate: it clarifies that 'column' is the field to match and 'values' is the set matched against it, and that the array order is meaningful. However, it omits the constraints visible only in the schema (2–10 values, non-empty strings) and the exact matching semantics (exact vs. partial).

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 the resource (rows of the Injection Molding Cost Checker dataset) and the filtering rule (column matches any of the given values), but it is written as a noun phrase with the verb only implied by the title 'Compare rows side by side'. It hints at differentiation from dataset_search via 'in the order given' and 'X vs Y questions', but never states the operation plainly enough to be unmistakable.

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 trailing clause 'for "X vs Y" questions' gives an implied usage context that helps distinguish it from a free-text dataset_search, but no alternative tool is named and no conditions for when-not-to-use it are given. The agent must infer the routing itself.

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 exist, so the description carries the full burden here, and it does disclose the shape of the returned content (source, date, licence, citation) and that the tool is a lookup rather than a mutation. It stops short of describing structure or format of the response, which is the remaining 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?

Two short sentences, front-loaded with the resource and its contents, then the usage rationale. No filler and nothing repeated from 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 zero-parameter, read-only metadata lookup with no output schema, the description supplies exactly what an agent needs: what it returns and when to use it. Nothing further is required to invoke it correctly.

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 takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a no-argument lookup.

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 the specific resource (provenance of the 'Injection Molding Cost Checker' dataset) and enumerates exactly what it yields: source, computation date, licence and citation. That is clearly distinguishable from siblings like dataset_stats or dataset_columns, which deal with the data itself rather than its attribution metadata.

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?

'Read this to attribute a figure correctly' gives a concrete trigger condition for calling the tool. It does not name an alternative or state exclusions, but no sibling covers provenance, so the routing risk is low.

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, and it does disclose one genuine trait: matching is exact and case-insensitive. However, it is silent on how many rows are returned, whether results are capped or paginated, permissions, and behavior on no match — notable gaps for a dataset query 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?

A single front-loaded sentence with no wasted words. It is grammatically a noun phrase rather than a sentence, but the predicate information (what is returned and under what filter) arrives immediately.

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?

No output schema, no annotations, and two undocumented parameters leave real gaps: result limits, multiple-match behavior, and the relationship to dataset_search are all unstated. For a lookup tool in a family of nine dataset_* siblings, this is under-specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must supply meaning, and it does explain the equality semantics of both parameters ('a column equals a value'). It does not clarify that 'column' must be an existing column name (e.g. discoverable via dataset_columns) or that case-insensitivity applies to the value comparison.

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?

States a specific operation and resource: return rows of the Injection Molding Cost Checker dataset where a column equals a value. The exact-match, case-insensitive qualifier makes it distinguishable in spirit from a generic search, but it never names the sibling it is not (dataset_search), so the agent must infer the split.

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 when-to-use guidance, prerequisites, or alternatives. The word 'exactly' hints that this is for exact-key lookups rather than substring/pattern search, but the agent is left to infer that dataset_search is the alternative for fuzzy matching. No guidance on what happens when no row matches or when the column is invalid.

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

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

With no annotations provided, the description carries the full behavioral burden. It usefully discloses case-insensitive matching and a 50-row result cap, but says nothing about return format, ordering, behavior on zero matches, or how rows are represented. Read-only nature is only implied by '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?

A single front-loaded sentence with no filler. It leads with the resource (rows of the dataset) and folds the matching semantics and cap into the same clause.

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 with no annotations and no output schema, the description conveys what is matched and how many results max come back, but leaves the shape of a returned row unspecified. Adequate, not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (only 'query' has a description; 'limit' relies on min/max constraints). The description compensates partially by stating the effective result ceiling ('up to 50'), but the distinct meaning of the limit parameter vs. the return cap is not clarified.

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 (search), a precise resource (rows of the Injection Molding Cost Checker dataset), and a match rule (cells contain the query, case-insensitive). It clearly distinguishes a text-search tool from sibling operations like dataset_row or dataset_top, though it doesn't explicitly 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 description implies the use case (find rows containing text) but gives no explicit when-to-use guidance and names no alternatives among the many siblings (dataset_rows, dataset_top, dataset_compare, etc.). The agent must infer that a text search is the right call.

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, and it does disclose genuinely useful data-handling behavior: group separators and currency symbols are normalized, and non-numeric rows are excluded but still counted. It stops short of stating whether the call is read-only, what the response looks like, or how errors (missing/non-numeric column) surface.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with the returned statistics and drops edge-case handling into a parenthetical, with no filler. Slightly dense but every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema and no annotations, the description usefully enumerates the exact metrics returned, which substitutes for the missing return contract, and it covers null/format handling. Missing only error behavior and dataset/connection context, which are minor at this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the schema only gives a string type with minLength 1, so the description's constraint that the column must be numeric is real added meaning. However it offers no column naming convention, example values, or pointer to dataset_columns for discovering valid names, leaving the parameter under-specified.

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 operation (compute count/min/max/mean/median/sum) against a specific resource (a numeric column of the Injection Molding Cost Checker dataset), so an agent immediately knows it is an aggregation tool. It is implicitly distinct from listing siblings like dataset_rows or dataset_columns, but it never explicitly contrasts itself with dataset_top, the nearest aggregation-ish 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?

There is no when-to-use or when-not-to-use guidance and no named alternative. The agent must infer from the stat list that this is the tool for numeric summaries, and nothing tells it what to do when the column is non-numeric or which sibling to consult for valid column names.

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, and it discloses little beyond the ranking direction. It does not say how many rows are returned by default, how ties are broken, that the result set is capped, or anything about the return shape.

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 compact sentence with the operation front-loaded and no filler. It is efficient, though the trailing quoted example is only marginally informative.

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 3-parameter tool with no annotations, no output schema, and low schema description coverage, the definition should explain the default result size, the limit cap, and ordering behavior. None of that is present, so an agent cannot predict the response.

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% (only 'ascending' is documented), so the description must compensate. It usefully establishes that the column must be numeric and that the tool can return highest or lowest, but it says nothing about 'limit' or its maximum of 50, leaving a real 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?

States a specific operation (rank/return top or bottom rows) on a specific resource (the Injection Molding Cost Checker dataset) constrained by a numeric column, and gives a plain-language framing ('which is the most/least X'). It is distinct from siblings like dataset_stats or dataset_row, though it never names them 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 'which is the most/least X' phrasing implies the intended question shape, so usage is inferable, but there is no explicit when-to-use statement and no routing to alternatives such as dataset_stats or dataset_search for different query types.

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 full disclosure. It usefully clarifies the operation is free, buys nothing, and guarantees no quote, plus names the returned content. It still omits whether it is strictly read-only and what the response actually looks like, leaving gaps a describe tool could fill.

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 definition is short and front-loads 'Read first' followed by the substantive content description. A minor amount of the text is spent re-explaining the sibling rather than this tool, but overall it is efficient.

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 and no parameters, so the description must convey what comes back; it lists recipients, consent wording, and confirmation method, which is a reasonably complete picture. The remaining gap is the tool's own distinct purpose relative to submit_enquiry and enquiry_fields.

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 takes zero parameters, so the schema has nothing to document and the baseline of 4 applies. No parameter meaning is needed here.

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 frames itself mainly around what the sibling submit_enquiry does ('States plainly what submit_enquiry does'), which muddles this tool's own role. It does reveal the resource it returns (recipients, consent wording, confirmation method), but the verb 'describe' stays vague and the emphasis on the sibling makes the boundary unclear.

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 leading 'Read first' implies usage order relative to submit_enquiry, giving implied sequencing guidance. However, it never explicitly states when to call this versus enquiry_fields or the dataset_* siblings, so the guidance is only suggested.

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 disclosure burden, and it does describe the shape of the returned data (six per-field attributes including help text and allowed options). It does not explicitly state that the tool is read-only or side-effect-free, but the enumeration of static field metadata strongly implies an inspection-only call.

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, no filler. The what-it-returns clause is front-loaded and the downstream usage note follows it, so the agent gets purpose before procedure.

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, so the description must explain the return value itself; it does so field-by-field and notes that options are only present 'where there are any.' Combined with the submit_enquiry keying instruction, an agent has everything needed to call this 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?

The input schema is empty with zero parameters, so there is nothing to document and the baseline of 4 applies. The description correctly describes this as a no-argument lookup and instead explains how the returned keys are consumed downstream.

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 a specific resource and enumerates exactly what it returns (key, label, type, required, help text, allowed options) for a named enquiry. It is clearly distinguishable from dataset_* siblings and from enquiry_describe.

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?

Explicitly routes the agent to the next step: 'Pass answers to submit_enquiry keyed by field key,' which names the sibling and the required keying convention. It lacks an explicit statement of when not to call it, but for a zero-arg discovery tool the workflow guidance is 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 and does so: it discloses the two-call validation flow, that a confirmation token is mandatory for the second call, that the person receives an email requiring a link click before any provider sees the enquiry, and it spells out exactly what consent means. These are the side effects and consent/authorization requirements an agent must know.

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?

Front-loaded with the core action and the key negation, then structured as Step 1 / Step 2, which is easy to follow. The verbatim consent sentence is already reproduced in the schema's consent description, making that clause mildly redundant and slightly bloating the text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations, no output schema and a nested answers object, the description is thorough: it explains step 1's return payload (summary, consent line, confirmation token) and the downstream email gate. It does not state what the second call returns, which is a minor remaining 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?

Schema description coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining how the parameters relate across calls: answers keyed by field key from enquiry_fields, consent=true on both calls, and the confirmation token being the value returned by step 1 rather than something the agent invents.

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?

States a specific verb (submits) and resource (an enquiry to Injection Molding Cost Checker), and explicitly disambiguates from what it is not: 'NOT a purchase, NOT a guaranteed quote'. It also ties itself to sibling tools by referencing field keys from enquiry_fields, so an agent can separate it from the dataset_* and enquiry_describe/enquiry_fields 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?

Gives explicit step-by-step usage: call once to validate and get a token, then call again 'only if the person agrees' with the token. It excludes a common misreading (not a purchase, not a guaranteed quote). It never names an alternative tool for the adjacent need, so it stops short of a 5.

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