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

With no annotations, the description carries the full burden. It discloses the return content (columns, numeric flags, row count, provenance banner) but does not explicitly state it is read-only or safe to call repeatedly. The phrase 'learn the schema' implies a non-mutating operation, but it's not explicit.

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

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

Two sentences with no fluff: the first lists the returned data, the second gives usage guidance. The key information is front-loaded, making it easy to scan.

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

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

For a no-parameter introspection tool, it lists the essential returned elements (columns, numeric flags, row count, provenance banner). It doesn't specify the output format, but for a schema-learning call, this is sufficient. The lack of an output schema is mitigated by the explicit list of what to expect.

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

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

There are zero parameters, so the baseline is 4. The description need not explain any parameters, and it doesn't. The schema coverage is trivially 100%.

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 tool returns columns, numeric flags, row count, and provenance banner, and identifies itself as the schema-learning entry point. This clearly distinguishes it from sibling tools like dataset_row (fetch a row) or dataset_stats (compute 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?

'Call this first to learn the schema' gives explicit ordering guidance, positioning it as the initial introspection step. It doesn't mention alternatives or when not to use it, but the directive is clear and actionable.

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 of behavioral disclosure. It mentions that rows are returned in the order given, but does not disclose whether the operation is read-only, how it handles missing values, error behavior, or output format. This is a significant gap for a tool with zero annotation support.

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 core behavior and ordering nuance. No unnecessary words or repetition. It is efficient while conveying the essential function.

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 filtering tool, the description covers the main semantics (column, values, ordering) and the intended use case. However, without annotations or an output schema, it lacks details about read-only nature, potential errors, and return structure, making it slightly incomplete for a fully informed agent 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 description explains the semantics of both parameters: 'column' as the field to filter on and 'values' as the list of values to match. This adds meaning beyond the bare schema, which only defines types and constraints. However, it does not mention the array constraints (minItems=2, maxItems=10) or any format details, so compensation for the 0% schema coverage is only 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 clearly states it returns rows from the dataset filtered by column values, and adds the ordering behavior. The phrase 'for X vs Y questions' implies a comparison use case, which helps distinguish it from siblings like dataset_search or dataset_row, but it does not name alternatives 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?

It provides a specific usage context ('for X vs Y questions'), indicating when this tool is appropriate (comparing values). However, it does not explicitly state when not to use it or name alternative tools, leaving some inference required.

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 burden of behavioral disclosure. It implicitly communicates a read-only, informational nature by describing provenance metadata and a citation purpose. It doesn't mention side effects or access requirements, but none are expected for a metadata tool.

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

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

The description is two concise sentences that front-load the key elements (source, date, licence, citation) and then state the intended usage. Every word earns its place with no 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?

Given the absence of parameters and output schema, the description fully covers what the tool provides and why it should be used. An agent can correctly invoke it without additional context.

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

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

The tool has zero parameters, so the schema is trivially complete (100% coverage). The description adds no parameter details because none exist, meeting the baseline for parameterless 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 states the tool's purpose: providing the source, computation date, licence, and citation for the Endpointo dataset. It uses a specific verb-resource structure ('Read this to attribute a figure correctly') and is distinct from siblings that focus on data operations like columns, search, or 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 description explicitly says 'Read this to attribute a figure correctly,' giving a clear use case and context. It doesn't list exclusions or alternative tools, but since no sibling covers provenance, this is sufficient.

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

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

There are no annotations, so the description must disclose behavior itself. It does reveal that matching is case-insensitive and exact, which is useful, but it does not mention what happens with multiple matches, no matches, or what fields are returned.

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 sentence with no filler, and the exact-match behavior is front-loaded. A more direct verb structure would improve clarity, but it earns no waste.

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-string-parameter lookup the description is mostly adequate, but with no output schema or annotations it omits the return shape and edge-case behavior. It also does not point to dataset_columns for discovering valid column names.

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%, but the sentence directly defines both parameters: 'column' is the field to check and 'value' is the exact value to match. It does not document where valid column names come from, but the basic semantics are clear.

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 title and description indicate a row lookup by exact, case-insensitive column-value match, which is specific enough to understand the operation and distinguish it from a general search sibling like dataset_search. However, the phrasing is somewhat awkward and does not explicitly state whether all matching rows or a single row are returned.

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 'exact key' and 'equals a value exactly' imply this tool is for exact-match lookups rather than fuzzy or general searching, but no alternatives are named and no when-not-to-use guidance is given.

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 behavioral burden. It discloses that matching is case-insensitive, that query text is matched across cell contents, and that results are capped at 50 rows. It implicitly presents the operation as a read-only search, though it does not mention ordering, empty results, or response format.

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 entire description is one sentence with no filler. It front-loads the operative behavior and key constraints, packing the predicate, case-insensitivity, and row cap into a compact, scannable form.

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 output schema, the description covers what the tool returns (matching rows) and the main operational constraints. The only gaps are the default limit when the parameter is omitted and result ordering, which are minor for correctly invoking the 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?

The schema already documents query as 'text to look for in any cell', while limit has only type and bounds. The description adds that matching is case-insensitive and containment-based across all cells, and that 'up to 50' clarifies the limit parameter's purpose beyond its numeric maximum.

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 selection predicate: rows whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from siblings like dataset_row (single-row retrieval) and dataset_stats (aggregate statistics). The title reinforces the search intent.

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 this is for content-based search across all dataset cells, and the sibling names make adjacent operations visible. However, it never explicitly says when to prefer this tool over alternatives, nor does it mention when not to use it or what prerequisites apply.

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. It discloses handling of grouping commas, currency, and exclusion/counting of non-numeric rows, which is useful. However, it does not state whether the operation is read-only, what the output format is, or how errors are handled.

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 list of statistics and immediately defines the scope. Every word earns its place, with 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 simple one-parameter tool, it covers key behaviors including data cleaning and the computed statistics. However, without an output schema, it does not specify the exact response structure (e.g., whether the excluded row count is included in the output), leaving 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?

Schema description coverage is 0%, so the description must compensate. It clarifies the column must be numeric and that formatting is handled, adding meaning beyond the schema's type and minLength. However, it lacks examples or specifics about column name conventions.

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

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

The description clearly states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Endpointo dataset. This specific verb and resource set it apart from sibling tools like dataset_row (row retrieval) and dataset_search (search).

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 usage for obtaining numeric column statistics but does not explicitly contrast with alternatives like dataset_top or dataset_compare. It provides context about handling grouping commas and currency, but no when-not guidance or mention of other tools.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions highest/lowest rows and numeric column but does not state the default ordering (though the schema does), limit behavior, handling of non-numeric columns, ties, or what the response includes (e.g., full rows vs. column values). These gaps leave the agent uncertain about side effects and edge cases.

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 filler. It efficiently communicates the core action and purpose, making it easy for an agent to scan and understand quickly.

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

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

Given three parameters, no output schema, and no annotations, the description is insufficiently complete. It fails to describe the return format, pagination or limit behavior, error conditions (e.g., non-numeric column), or how to interpret results. The agent cannot fully anticipate the tool's behavior from the description alone.

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 description adds meaning for 'column' (must be numeric) and for 'ascending' (highest vs. lowest), which complements the schema. However, it does not elaborate on 'limit' semantics (e.g., default value, max) beyond the schema's min/max. With schema coverage at only 33%, the description partially compensates but does not fully clarify all parameters.

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

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

The description clearly states the tool's function: it ranks rows of the Endpointo dataset by a numeric column and returns the highest or lowest rows. The phrasing "which is the most/least X" makes the purpose specific and distinguishable from sibling tools like dataset_stats (which likely aggregates) and dataset_search (which likely filters).

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 guidance on when to use this tool versus alternatives. The description implies a ranking use case but does not mention when to prefer it over dataset_row, dataset_search, or dataset_stats. No exclusions or alternative names are given, leaving the agent to infer applicability.

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