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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 burden. It describes the output contents well and implies a read-only metadata retrieval, though it does not explicitly state the absence of side effects.

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 concise, one sentence listing outputs and the usage cue. Every word adds value.

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 fully explains what the tool returns and when to use it, despite lacking an output schema. It gives enough context for an agent 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?

There are no parameters, so there is nothing to document. The description correctly covers all input aspects by indicating none are needed.

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

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

The description clearly specifies the returned data (columns, numeric flags, row count, provenance banner) but lacks an explicit verb like 'get' or 'list'. It is still unambiguous and distinct from 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?

Explicitly instructs 'Call this first to learn the schema', providing clear when-to-use guidance. It does not mention alternatives but the instruction suffices for a metadata 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?

No annotations are provided, so the description carries the full burden. It discloses the core selection logic (matching values in order) but omits details like return format (full rows? specific fields?), pagination, empty-result behavior, or error conditions. It's not contradictory, but it's incomplete for a tool with no output schema.

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

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

The description is a single, compact sentence with no filler. The core behavior is front-loaded, and the use-case hint is appended efficiently. Every word contributes.

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 row-selection tool, the description gives the essential filter logic and ordering, but lacks any mention of output structure (since no output schema exists) or edge cases like missing columns or no matching values. It's adequate for a basic call but not fully complete for an agent to anticipate results.

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 explain parameters. It does convey that 'column' is the field to match and 'values' are the target values, and mentions ordering. However, it doesn't clarify constraints like minimum 2 values, max 10, or value formats. It adds meaning but not comprehensive compensation for the 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 clearly states it returns rows of the dataset filtered by a column matching any given values, in a specific order, and hints at its use case ('X vs Y' questions). This is specific and distinguishes it from siblings like dataset_row (single row) or dataset_search (search), though it doesn't explicitly name alternatives.

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 clear context for when to use the tool ('for X vs Y questions') which implies a comparative side-by-side scenario. It doesn't explicitly state when not to use it or name sibling tools, but the context is enough to guide selection.

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 provided, so description carries the burden. It discloses the kind of data returned (source, date, licence, citation) and implies no side effects since it is informational. Adequate for a read-only provenance 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 concise sentences that cover content and purpose without any redundancy or fluff.

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 provenance lookup, the description lists all the information returned and the context for using it. No output schema exists, but nothing essential 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 schema has no parameters, so description need not explain them. Baseline 3 applies since there is nothing to add about 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?

Clearly states the tool provides provenance information: source, date, licence, and citation. This distinguishes it from sibling tools like dataset_columns or dataset_stats which have different purposes.

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 says to read this to attribute a figure correctly, giving a practical use case. Does not explicitly contrast with alternatives, but the purpose is clear enough.

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?

The description discloses case-insensitivity and exact matching, which are behavioral traits. However, with no annotations provided, it fails to mention the return format, whether multiple rows can be returned, or error behavior. It also ambiguously uses both 'rows' and 'a row', creating inconsistency.

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 with minimal waste. The primary structural issue is the singular/plural inconsistency between the title ('a row') and the description ('rows'), which could confuse an agent. It is efficient but could be more precise.

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 tool with no output schema, the description covers the core matching logic. It is adequate but lacks critical details such as the return structure, whether multiple matches are possible, and error conditions. Given the simplicity, it is minimally viable but not thorough.

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 clarifies that the column parameter is the column to match against and the value is the value to compare, which adds meaning beyond the schema's property names. However, it does not provide additional details like column name formats, value type constraints, or handling of missing values.

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 identifies the specific resource (Tickmarko dataset) and the operation (retrieve rows matching an exact, case-insensitive column value). It is clear, but does not explicitly contrast with the sibling dataset_search, which might handle partial or fuzzy matches. The title adds clarity by stating 'Look a row up by an exact key'.

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 guidance is given on when to use this tool versus alternatives like dataset_search or dataset_compare. The description implies exact-match use but does not state exclusions or mention when a sibling would be more appropriate, leaving the agent to infer the 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?

The description discloses some behavioral traits such as case-insensitivity and the limit cap of 50 rows, which are useful. However, with no annotations present, it does not mention potential side effects, permissions, or error behavior, leaving some transparency gaps.

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 conveys the core functionality and key constraints without extraneous details. It is well-structured and easy to parse.

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 simple schema (two parameters, no output schema), the description covers the main purpose and critical behavior. It could mention return format or error handling, but these are not essential for understanding basic usage.

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 clarifies the meaning of both parameters: 'query' is matched case-insensitively in any cell, and 'limit' sets a maximum of 50 results. This adds semantic value beyond the sparse schema descriptions, though the limit parameter lacks an explicit description in 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 clearly states the tool's function: it returns rows from the Tickmarko dataset based on a case-insensitive substring match across all cells. This is specific and unambiguous, distinguishing it as a full-text search operation.

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 guidance is provided regarding when to use this tool instead of the sibling tools (e.g., dataset_row, dataset_stats, dataset_top). There is no mention of use cases, limitations, or alternative scenarios.

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 discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which are useful behavioral details. However, it does not mention return format or error conditions, leaving some gaps.

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

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

The description is a single, efficient sentence that front-loads the core purpose and adds handling details in a parenthetical. No wasted words, and the most important information appears first.

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 tool with one parameter and no output schema, the description is fairly complete. It lists the statistics and clarifies data handling, but does not specify the exact output structure or error handling for invalid columns, which are minor gaps. Overall, it provides enough context for a correct invocation.

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

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

The schema provides only a string parameter with no description (0% coverage). The description adds meaning by indicating the column should be numeric, which is beyond the schema. However, it does not explicitly state that the parameter is the column name or provide examples, so it partially compensates but is not comprehensive.

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 computes count, min, max, mean, median, and sum for a numeric column of the Tickmarko dataset, clearly distinguishing it from sibling tools that handle columns, rows, search, and comparison. The verb 'compute' is implied but the list of statistics is specific and 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 description does not explicitly state when to use this tool over alternatives like dataset_search or dataset_top. It provides some context about handling commas and currency, which may guide usage, but there is no explicit when-to-use or when-not-to-use guidance, leaving the agent to infer the appropriate selection.

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 only states the core ranking action and mentions the ascending flag implicitly, but does not describe the output format, tie-breaking behavior, pagination limits, or that it is a read-only operation. The short phrase leaves many behavioral aspects ambiguous.

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

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

The description is a single sentence, highly concise and front-loaded with the core purpose. There is no wasted text. However, it is so brief that it omits important details, so while it is efficient, it borders on under-specification. The structure is acceptable but could benefit from a second sentence on output or usage.

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 ranking tool with no output schema and no annotations, the description is too sparse. An agent cannot infer what the response looks like (full rows, column values, count), whether ties are included, or any edge-case behavior. The description covers the basic action but leaves critical context unaddressed.

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 (33% - only 'ascending' has a description). The description adds a useful semantic hint that 'column' must be numeric, which is not in the schema. However, it does not explain 'limit' semantics (beyond schema min/max) or clarify how 'ascending' interacts with the ranking beyond the schema's own description. It partially compensates for the coverage gap but not sufficiently.

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 ranks rows by a numeric column and returns the highest or lowest, with the illustrative phrase 'which is the most/least X'. This distinguishes it from siblings like dataset_stats (aggregates), dataset_search (matching), and dataset_row (single record). The verb 'rank' and resource 'rows' are explicit.

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 use for top/bottom ranking queries ('most/least X') but provides no explicit when-to-use guidance or exclusions. It doesn't reference sibling tools or state when to prefer dataset_stats or dataset_search instead. The context is clear enough for a simple ranking task, but it lacks explicit routing.

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