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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 transparently lists what the tool returns and implies a read-only schema inspection, but it does not explicitly state whether there are side effects, how the output is structured, or whether there are performance/cost implications. No contradiction, but deeper behavioral disclosure is missing.

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 concrete output fields are front-loaded, and the usage directive 'Call this first' is appended after the substance. Every sentence contributes 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?

For a zero-parameter metadata tool, the description is nearly complete: it names the dataset, the returned fields, and the intended first-use position. It could add a note about whether the schema is static or dynamic, and the sibling overlap is not disambiguated, but the core need for selecting and invoking the tool is satisfied.

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 an empty input schema, so there is no parameter ambiguity to resolve. The baseline of 4 applies because no parameter documentation 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 specific dataset (RentRollDesk) and enumerates the exact returned information: column names, numeric flags, row count, and provenance banner. It stops short of explicitly contrasting itself with dataset_stats and dataset_provenance, which may overlap on row count and provenance, so it does not fully distinguish from 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?

It gives clear usage context with 'Call this first to learn the schema,' which implies this is the entry-point tool for understanding the dataset. However, it does not state when to choose a sibling tool instead or mention any conditions under which this tool should not be used.

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, and the description does not state whether the operation is read-only, whether it returns full rows, or whether there are any limits beyond the schema constraints. The description implies a query operation but leaves important behavioral details unstated.

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 and avoids extraneous detail, fitting the intended purpose into a single sentence. The phrasing is slightly awkward but remains understandable and focused.

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 detail about return shape, ordering, or error behavior, the description is incomplete for an agent that needs to know what result to expect. It explains the selection logic but not the full contract of 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 provides no parameter descriptions, and the description only loosely maps 'column' and 'values' by saying rows match 'any of the given values'. It does not clarify whether values are exact strings, how column names are specified, or how ordering by the given values is handled.

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 indicates that the tool retrieves rows matching given values in a specified column and frames this as supporting 'X vs Y' comparisons. The title reinforces the side-by-side comparison intent, distinguishing it from simple row retrieval or 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 phrase 'for X vs Y questions' gives some usage context, and the sibling tool names suggest alternatives. However, it does not explicitly state when to prefer this tool over dataset_search or dataset_row, nor does it describe matching semantics such as exact match or case sensitivity.

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 states what information is returned (source, date, licence, citation), which is transparent about the output. However, it does not mention any potential side effects, permissions, or limitations (e.g., whether the data might be stale or require network access). Being a read-only metadata tool, the risk is low, but the description could be more 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?

The description is two sentences long, each earning its place. The first sentence enumerates exactly what the tool returns, and the second gives a practical usage tip. There is 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?

Given the simplicity of the tool (no parameters, no output schema), the description fully conveys what the tool does and when to use it. It mentions the specific dataset and the metadata fields returned, which is sufficient for an agent to invoke it correctly and understand the result. No additional context is needed.

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 the schema coverage is trivially 100%. According to the baseline rule for high schema coverage, a score of 3 is appropriate. The description does not add any parameter-related information because there are none to document.

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 RentRollDesk dataset. It is specific about the resource and the kind of metadata returned, and it is distinct from sibling tools (columns, compare, row, search, stats, top) which all focus on data content rather than provenance.

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 explicit guidance on when to use the tool: 'Read this to attribute a figure correctly.' This conveys the intended use case clearly. It does not explicitly contrast with sibling tools, but the purpose is sufficiently distinct that no further disambiguation is necessary.

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 burden. It does disclose a meaningful behavioral trait: matching is exact but case-insensitive. However, it does not clarify whether all matching rows are returned, ordering, behavior with duplicate matches, or what happens if the column does not exist.

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 that states the tool's purpose and key behavioral constraint without wasted words. It is appropriately sized for a simple two-parameter lookup 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 simple exact-match lookup, the description is mostly sufficient: it names the dataset, the two parameters' relationship, and the matching behavior. It lacks an explicit output description and edge-case behaviors, but 'The rows...' implies the return value, which is enough for an agent to invoke 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?

Schema description coverage is 0%, so the description must compensate. It clarifies that the 'column' parameter is compared against the 'value' parameter, and adds the exact-match/case-insensitive semantics. Still, it gives no guidance on valid column names, value formatting, or how both parameters interact beyond the comparison.

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

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

The title and description together specify a clear action — look up rows by an exact key — and a resource (RentRollDesk dataset rows). The description adds the matching rule (exact, case-insensitive) which helps distinguish it from fuzzy search, but it does not name sibling tools explicitly.

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

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

The description does not state when to use this tool instead of dataset_search or other siblings. There is no mention of when exact lookup is preferred over search, stats, or compare tools, leaving the agent to infer usage from the name alone.

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 disclose key behaviors: case-insensitive matching, cell-level substring search, and a 50-row cap. However, it does not disclose the output format, default limit, behavior when more than 50 rows match, or what happens if no rows match, which are meaningful gaps for a no-annotation tool.

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

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

The description is a single, front-loaded sentence with no filler words. It communicates the core search behavior, case-insensitivity, and row cap in minimal space, so every part 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 two-parameter search tool, the description gives enough to invoke it with valid parameters, and the schema fills in constraints. However, with no output schema, no annotations, and no usage guidance, the description falls short of fully preparing the agent for the result shape and when to prefer this tool over siblings.

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 exactly 50%: the query parameter is described in the schema, while limit is not. The description adds value by clarifying case-insensitivity for query and stating the 'up to 50' cap, which relates to limit. Still, it does not add meaning for the limit default or whether limit is optional beyond the schema's min/max constraints.

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 action as returning rows of the RentRollDesk dataset that match the query in any cell, which distinguishes it from column, stats, and row-specific siblings. However, it lacks an explicit verb like 'returns' and does not explicitly contrast it with dataset_row or dataset_top, so sibling differentiation is implied rather than stated.

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 offers no guidance on when to use this tool versus siblings such as dataset_row, dataset_stats, or dataset_columns. It does not state exclusions or provide alternative tool names, leaving the agent to infer the appropriate context from the name alone.

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 behavioral transparency burden. It discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded but counted—useful quirks beyond the tool's name. It does not cover edge cases like empty columns or invalid column names, but it provides meaningful context.

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 the key output statistics front-loaded and behavioral caveats in a parenthetical. Every word contributes value; no 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 one-parameter read-only tool, the description adequately covers purpose, output values, and key behavioral nuances. The lack of an output schema is mitigated by listing the statistics returned. It falls slightly short only by omitting error/edge-case handling and explicit sibling differentiation.

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 only defines 'column' as a non-empty string with 0% description coverage. The description adds that the column must be numeric and belong to the RentRollDesk dataset, which is minimal but helpful. It does not provide examples, formatting expectations, or error behavior for invalid 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 clearly states the tool computes named statistics (count, min, max, mean, median, sum) for a numeric column of the RentRollDesk dataset. It specifies the resource and scope, and the specificity differentiates it from siblings like dataset_search or dataset_top.

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 is implied: call this when you need summary statistics for a numeric column in RentRollDesk. However, there is no explicit guidance on when not to use it or how it compares to sibling tools like dataset_compare or dataset_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?

No annotations are provided, so the description must carry the behavioral disclosure burden. It only restates the ordering behavior already implied by the title and the 'ascending' schema description, and it does not address null handling, tie ordering, limit behavior, or what the returned rows look like.

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 with the core concept front-loaded and no filler. It is appropriately compact, although its brevity comes at the cost of important behavioral and parameter detail.

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 is too sparse to fully support correct invocation. It does not state what the tool returns, how limit is applied, what happens with non-numeric columns, or where valid column names can be found, leaving meaningful gaps for an agent.

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 at 33%, and the description only partially compensates. It usefully clarifies that 'column' must be numeric, but it adds nothing about 'limit' semantics or the default direction beyond what the schema already says for 'ascending'.

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 operation: return the highest or lowest rows of the RentRollDesk dataset ordered by a numeric column, framed as answering 'which is the most/least X'. This is clear and distinguishable from sibling tools like dataset_stats or dataset_search, though it does not explicitly name those 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?

The 'most/least X' phrasing implies when the tool is appropriate, but the description gives no explicit guidance about when to use it versus sibling tools such as dataset_stats, dataset_row, or dataset_search. The intended context is inferable but not stated.

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