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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 behavioral burden, but for a zero-parameter metadata reader the burden is light. It discloses the full shape of the response (columns, numeric flags, row count, provenance banner), making the read-only nature and output content evident without stating it in safety terms.

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 dense sentences with no filler, and the list of returned fields is front-loaded. The only structural nit is that the critical 'call this first' instruction is placed at the end rather than the start.

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 explain the return values, and it does so explicitly (columns, numeric flags, row count, provenance banner). Nothing an agent needs in order to call a zero-arg introspection tool is missing, though it could note the response format or the relationship to dataset_provenance.

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 baseline is 4 and there is nothing parameter-specific for the description to clarify or compensate for.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific resource (the PotterySuppliesHQ dataset) and enumerates exactly what is returned: column list, numeric flags, row count, and provenance banner. It is clear what the tool provides, though it does not explicitly distinguish itself from the sibling dataset_provenance, which the mention of a 'provenance banner' partly overlaps with.

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/prerequisite instruction, which is genuine when-to-use guidance. It stops short of naming alternatives (e.g., dataset_provenance or dataset_stats) or saying when not to call 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses ordering behavior ('in the order given') and any-of matching, but omits permission requirements, result limits, pagination, error behavior for missing values, and output structure — significant gaps for a tool with zero annotation coverage.

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, front-loaded sentence with no wasted words. It is dense but readable, though the em dash construction is slightly cryptic.

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 or annotations exist, so the description should clarify return values and call behavior more fully. It omits what a returned row looks like, how many rows are returned, and what happens when a value is not found — leaving an agent without enough context to call and interpret results confidently.

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 0% schema description coverage, the description must compensate. It adds important semantics beyond the schema: the 'any of' matching logic against a column and the fact that output order follows the given values. However, it does not explain the column name expectation or the 2–10 item array constraint, leaving the compensation incomplete.

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 retrieval action: returning rows of the PotterySuppliesHQ dataset whose column matches any of the given values. It is clear and distinct from a generic dataset_search or dataset_row, though it does not explicitly name or contrast those siblings.

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 a clear contextual use case for when to select this tool. It does not provide explicit when-not-to-use guidance or name alternative tools, but the usage context is strong.

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. It does disclose the return content (source, date, licence, citation), making the read-only informational nature self-evident. It omits any statement about freshness guarantees or where the metadata originates, which would be the only 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 sentences, front-loaded with the content list and closed with the reason to call it. No filler. Slightly under-budgeted rather than verbose.

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 parameters, the description must convey what comes back, and it lists the four key returned attributes. That is sufficient for an agent to call and consume it correctly, though a note on whether the metadata is static or versioned would fully close the loop.

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 per the baseline there is nothing for the description to disambiguate. Its mention of returnable fields is a bonus, not a requirement.

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 exact resource (the PotterySuppliesHQ dataset) and enumerates the specific fields it exposes: source, computation date, licence, and citation. This clearly separates it from sibling tools like dataset_stats, dataset_columns, and dataset_search, which cover data rather than 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 when-to-use condition tied to citation/attribution. It stops short of naming alternatives or stating when NOT to call it, but the usage context is unambiguous for a zero-parameter info 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, the description carries the full behavioral burden. It does disclose matching behavior (exact, case-insensitive), which is valuable, but says nothing about whether multiple rows can match, permissions, dataset scope, or result format. For a lookup tool with zero structured behavioral coverage 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?

A single sentence with zero filler, front-loading the resource and the matching rule. Nothing redundant is 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 minimal two-parameter read tool with no annotations and no output schema, the description covers the core semantics but leaves the return shape (single row vs many) and sibling routing unstated. Adequate but with clear gaps.

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

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

Schema description coverage is 0%, so the description must compensate. It does add key semantics — the match is on a column equalling a value, and comparison is case-insensitive — which the schema alone does not convey. However, it does not explain valid column names, error behavior for unknown columns, or the exact-vs-partial distinction per parameter.

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 concrete verb+resource (rows of the PotterySuppliesHQ dataset) plus matching semantics ('a column equals a value exactly'), and the title reinforces 'look up by exact key'. It is clear what it does, but it never distinguishes itself from the sibling dataset_search, which likely covers non-exact matching.

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 when-to-use guidance and no alternative is named; the reader has to infer from the word 'exactly' that dataset_search is the tool for fuzzy/substring lookups. Nothing states prerequisites such as needing a valid column name.

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 usefully discloses case-insensitive matching and the 50-row cap, but says nothing about result ordering, pagination, or whether matching is substring-based. Low-risk read search, so partial coverage is acceptable.

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 fragment that front-loads the operation and packs matching semantics and the limit without waste. It is appropriately sized for a simple search tool.

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-output-schema search tool this covers the core matching semantics and cap, which is enough to call it correctly. It leaves gaps around result ordering and what a returned row contains, so it is adequate rather than 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 only 50%: query is documented in the schema, but limit has no schema description. The description compensates partially by stating the 'up to 50' cap, but adds no syntax or format details beyond that.

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 precise operation: it returns rows of the named dataset whose cells contain the query, matched case-insensitively. This is a specific verb+resource and lets an agent distinguish it from dataset_row (single row) or dataset_top. It stops short of explicitly naming siblings, so a 5 isn't warranted.

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 when-to-use guidance, no prerequisites, and no mention of alternatives among the many dataset_* siblings. The reader must infer that this is the free-text search tool versus dataset_row or dataset_top.

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 behavioral burden. It does add real value by disclosing data-handling rules ('grouping commas and currency are handled; non-numeric rows are excluded and counted'), which tells the agent results are parsed and filtered. It is silent on error behavior for an unknown or non-numeric column, null handling, and output 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 sentence that front-loads the returned statistics and then appends the data-handling caveats. Nothing is padded, though the parenthetical would read more cleanly as a separate 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?

With no annotations and no output schema, the description does useful work by enumerating the six return values, which is required here. However, for a data-analysis tool it omits how invalid or missing columns are handled and how the 'excluded and counted' count is surfaced, leaving meaningful gaps.

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 is one parameter with 0% schema description coverage, so the description must compensate. It conveys that the argument is a numeric column name in the PotterySuppliesHQ dataset, which is more than the bare 'string, minLength 1' schema says, but it does not specify name format, case sensitivity, or how to discover valid column names (e.g., via dataset_columns).

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) on a specific resource (a numeric column of the PotterySuppliesHQ dataset), so an agent knows exactly what it produces. It does not name or contrast with any sibling such as dataset_top or dataset_compare, 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 'numeric column' implicitly scopes when the tool applies and rules out non-numeric columns, which is a useful implied constraint. There is no explicit statement of when to prefer this over dataset_top, dataset_row, or dataset_compare, nor any prerequisites, so guidance remains inferred 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, the description carries the full behavioral burden, yet it only tells us it ranks rows. It omits that this is a read-only operation, the 50-row cap implied by the schema, behavior on ties or non-numeric input, and the return shape. Significant disclosure gaps for a 3-parameter 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 the resource and the guiding question, with no filler. The trailing gloss is slightly informal but earns its place as a usage cue.

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 and no annotations mean the description must cover the operation fully, but it leaves return format, the limit cap, tie-breaking, and error behavior unspecified. Adequate to identify the tool, insufficient to call it confidently.

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 33% – only 'ascending' is documented in the schema. The description compensates by conveying that the column must be numeric and that ordering can be highest or lowest (mapping to 'ascending'), but the 'limit' parameter goes unexplained in both schema and description.

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 verb (rank/return top) and resource (rows of the PotterySuppliesHQ dataset) scoped to a numeric column, which is enough to distinguish it from dataset_stats or dataset_search. It does not explicitly name a sibling, but the operation is unambiguous.

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 parenthetical "which is the most/least X" implies the use case, so an agent can infer intent, but there is no explicit when-to-use or when-not-to-use guidance relative to dataset_stats, dataset_row, or dataset_search.

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 well: it discloses that nothing is bought, ordered or paid, that no quote is guaranteed, and that it is free. It also names what the return content covers (recipients, consent wording, confirmation method). It does not describe error/edge behavior, but the core behavioral profile is clearly conveyed.

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?

'Read first' is front-loaded and the two sentences earn their place by covering purpose and the key behavioral facts. The middle clause is somewhat run-on, but 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 zero-parameter, annotation-free tool with no output schema, the description covers both intent and what the response conveys (recipients, consent wording, confirmation). Little an agent needs in order to call it correctly is missing.

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 baseline of 4 applies; there is nothing for the description to compensate for.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description makes clear this is a read-only explainer that 'states plainly what submit_enquiry does' and returns the consent/receipt details, which separates it from submit_enquiry itself. The verb (read/states/returns) and resource (the enquiry process description) are identifiable, though the opening framing describes the enquiry rather than the tool directly.

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 first' gives an explicit ordering cue relative to submit_enquiry, implying this should be consulted before submitting. However, it never explicitly states when to use this versus the sibling enquiry_fields, nor any conditions or exclusions.

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 disclosure burden. It correctly implies a safe read by enumerating returned metadata and discloses the output fields (important since there is no output schema), but says nothing about permissions, whether options are always present, or error behavior for unknown enquiries.

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 almost no waste, and the returned-field enumeration is front-loaded. The list of fields is slightly dense but justified because no output schema exists to carry it.

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 tool with no output schema, the description supplies the return-value contract and links it to submit_enquiry, which is what an agent needs to act. The only real gap is sibling differentiation against enquiry_describe.

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 document beyond what the empty schema already conveys. Baseline 4 applies; no 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 names the exact resource it returns: every field of the PotterySuppliesHQ enquiry, and enumerates the shape (key, label, type, required, help text, options). It does not, however, distinguish itself from the sibling enquiry_describe, which an agent could easily confuse with it.

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?

It tells the agent what to do with the result ('Pass answers to submit_enquiry keyed by field key'), which implies the discovery-then-submit workflow. But it never states when to call this versus enquiry_describe, and there is no explicit exclusion or prerequisite 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 burden and does so: it discloses the validation pass, the returned summary/consent line/confirmation token, the two-phase commit, the requirement that the recipient click an emailed link before any provider sees the enquiry, and the exact data-sharing scope. These are non-obvious behavioral facts an agent must know to avoid submitting prematurely.

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 purpose and structured as Step 1/Step 2, with every clause doing work. Slightly penalized because the full consent sentence is reproduced verbatim from the schema's consent description, a minor redundancy.

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 exists, but the description enumerates what each phase returns (summary, consent line, confirmation token) and the downstream email-link flow, so an agent knows both the call protocol and the side effects. Nothing material is missing for a 3-parameter nested tool.

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%, so the baseline is 3; the description earns above baseline by explaining that answers are keyed by field key sourced from enquiry_fields and that confirmation is only used after summary approval. It largely restates what the schema already says about consent, hence a 4 rather than 5.

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 PotterySuppliesHQ) and explicitly negates the two most likely misreadings: 'NOT a purchase, NOT a guaranteed quote.' It also routes the agent to the sibling that supplies field keys (enquiry_fields), so it is distinguishable from enquiry_fields/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?

Gives an explicit two-step protocol with the exact condition selecting each call: step 1 with answers+consent to validate, step 2 only if the person agrees, with the same payload plus the token. Nothing about when to invoke this vs. alternatives is left to inference.

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