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

With no annotations, the description itself communicates that this is a non-mutating introspection call by describing exactly what information it returns. It does not discuss errors, latency, or response format, but the zero-parameter read-only nature is sufficiently implied.

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

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

A single sentence packs the output contents and the primary usage instruction with no filler. The most important operational guidance ('Call this first') remains direct and unambiguous.

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 schema-introspection tool with no output schema, the description covers what an agent needs: what the call returns and when to call it. No missing prerequisites or side effects are needed 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 has no parameters, so the description cannot add parameter-level meaning; baseline 4 applies. It still clarifies what the returned schema information will contain, which is the relevant semantic context.

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 (Funnelvo dataset) and enumerates the returned content: columns, numeric flags, row count, and provenance banner. It also states a clear invocation role ('Call this first to learn the schema'), which visibly separates it from the sibling analysis 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?

'Call this first' is an explicit usage directive for agents starting to understand the dataset. It gives a clear context for use, though it does not spell out when not to use it or name alternatives.

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 disclosure burden. It conveys the core behavior—filtering rows and preserving order—but does not clarify exact-match semantics, case sensitivity, error behavior, or explicitly state that this is a read-only operation. The read-only nature is only implied by the wording.

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 tight sentence with no wasted words. The front-loaded behavior is followed by a short purpose clause that adds orienting context without bloating the definition.

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 retrieval tool with no output schema, the description provides enough to invoke it correctly: the filter column, the values to match, the ordering, and the comparison use case. It does not discuss matching nuances or alternatives, but those are not essential for 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?

With 0% schema description coverage, the description compensates by explaining that 'column' is the filter field and 'values' are the values to match, with output order following the provided values. It does not restate the min/max constraints, but those are already visible 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 states a specific verb and resource: it returns rows of the Funnelvo dataset filtered by a column matching any of the given values, preserving the given order. The 'X vs Y' framing makes the intended comparison purpose clear and helps distinguish it from related dataset tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: use this tool when comparing rows for 'X vs Y' questions based on a specific column and a list of values. It does not explicitly name alternatives or when-not-to-use cases, but the intended scenario is evident.

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 clearly enumerates the content the tool returns (source, date, licence, citation) and frames the operation as a read/reference action, implying no side effects. This is adequate transparency for a simple 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 short sentences with no filler. The essential information is front-loaded: the dataset name and the specific provenance fields, followed by a practical use case. Every word 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 zero-parameter tool with no output schema, the description is complete. It names the dataset, lists exactly what information will be returned, and explains why an agent would call it. Nothing necessary for correct invocation 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 has zero parameters and schema coverage is 100%, so the baseline is 4. There is no parameter semantics to add, and the description appropriately focuses on the tool's output and usage rather than nonexistent 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 clearly states that the tool provides the source, computation date, licence, and citation for the Funnelvo dataset. It uses the directive 'Read this to attribute a figure correctly,' making the purpose explicit. This distinguishes it well from sibling tools that focus on columns, rows, stats, or comparisons.

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 'Read this to attribute a figure correctly' explicitly indicates when to use the tool: whenever attribution or citation is needed. While it does not name sibling alternatives or state when not to use it, the context of provenance versus data operations 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?

With no annotations, the description carries the burden. It discloses that matching is case-insensitive, which adds behavioral nuance beyond the schema. However, it does not state whether one or many rows are returned, what happens on no match, or what the response contains.

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. It front-loads the resource and states the filtering rule efficiently, though it lacks an explicit verb like 'returns' or 'fetches'.

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 core lookup semantics are clear, but the title says 'a row' while the description says 'rows,' creating ambiguity about cardinality. With no output schema and no annotations, the description should clarify return behavior and error cases; it does not.

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 loosely maps column and value to the equality condition, but does not define whether column must be a key column, whether value supports patterns, or what constraints exist beyond minLength. For two simple params, the mapping is adequate but not detailed.

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 that the tool retrieves rows from the Funnelvo dataset based on an exact column-value match, and the title adds the 'look up by key' framing. It does not explicitly differentiate from siblings like dataset_search, but the meaning is specific enough for an agent to grasp the core function.

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 on when to use this tool versus alternatives such as dataset_search or dataset_compare. There are no exclusions, prerequisites, or hints about which tool suits fuzzy vs. exact lookups.

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 load. It usefully discloses case-insensitive matching, 'any cell' coverage, and the 50-row cap. But it omits ordering, the behavior when limit is omitted, and what happens when no rows match.

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

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

The description is one concise sentence that front-loads the core behavior, then adds the two most important constraints: case-insensitivity and the row cap. No filler or redundant restatement of the tool name.

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 search tool, the description covers the target dataset, the matching rule, and the result cap. It is sufficiently complete for an agent to invoke it correctly, though a small amount of detail about default limit or ordering would make it fully airtight.

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 describes 'query' but not 'limit'. The description adds meaningful context by clarifying case-insensitive cell-content matching and the up-to-50 cap, but it does not explain the default limit or pagination behavior beyond what the schema already constrains.

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

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

The description clearly identifies the tool as returning rows of the Funnelvo dataset that contain the query in any cell, which is a specific and searchable operation. It does not explicitly contrast it with sibling tools like dataset_top or dataset_row, so it falls short of full differentiation.

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 intended use is implied: call this tool when you need to find rows by cell text. However, the description provides no explicit guidance on when to prefer it over the sibling dataset tools or what it should not be used for.

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 add value: it discloses that grouping commas and currency symbols are handled during parsing, and that non-numeric rows are excluded and counted. This is useful beyond what the schema provides. It does not cover edge cases like empty columns or nonexistent column names, but the disclosed parsing behavior is the main practical risk.

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, well-structured sentence: the statistics are front-loaded, and the parsing caveats are compactly placed in a parenthetical. Every element earns its place with no filler or 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?

For a one-parameter tool, the description is nearly complete: it enumerates the six computed outputs, which effectively conveys the return content despite the lack of an output schema. Minor gaps are how to discover valid column names and behavior when all rows are non-numeric or the column is missing, but these are minor at this complexity level.

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 compensate for the single parameter. It clarifies that the column should be numeric, but it does not explicitly state that 'column' is a column name or how to obtain a valid value (e.g., via dataset_columns). The description adds some meaning over the bare string schema but leaves the parameter semantics partly to inference.

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 resource ('numeric column of the Funnelvo dataset') and the exact computation performed (count, min, max, mean, median, sum). The enumerated statistics clearly distinguish it from siblings like dataset_search and dataset_row, although no sibling is named 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?

Usage context is implied: the tool is for computing summary statistics on a numeric column. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives such as dataset_top or dataset_columns for discovering valid columns.

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 burden of behavioral disclosure. It does reveal that the tool sorts by a numeric column and can return either end of the ordering, but it does not mention default limit, tie handling, nulls, or the response shape. Useful but incomplete.

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

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

The description is one crisp sentence with a clarifying example, and the key behavior is front-loaded. Slight redundancy with the title means it is not a 5, but there is no wasted prose.

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 three-parameter tool the core idea is present, and the schema covers limit bounds and ascending semantics. Still, without an output schema or annotations, the description omits usage boundaries and expected return ordering/defaults, leaving moderate 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?

The description adds important meaning for 'column' by clarifying it must be numeric, and 'highest/lowest' aligns with the ascending flag. However, it does not explain limit behavior beyond schema bounds, and schema description coverage is only 33%, so the compensation is 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 a specific operation — ranking rows by a numeric column and returning the highest or lowest — and grounds it with a concrete question ('which is the most/least X'). It is clear on its own, though it does not explicitly distinguish itself from siblings like dataset_stats or dataset_row.

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 statement of when to use this tool versus alternatives such as dataset_stats, dataset_search, or dataset_row. The intended use is only implied by the 'most/least X' example, leaving an agent to infer when top-N ranking is the right choice.

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