site

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

No annotations are provided, so the description carries the disclosure burden. It does disclose the contents of the response (columns, numeric flags, row count, provenance banner) and implies a read-only role via 'learn the schema,' but it does not explicitly state that the tool is non-mutating, mention output format, or address auth/rate-limit considerations.

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 that front-loads the specific outputs and ends with actionable guidance. There is no filler or redundant restatement of the title.

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 sufficient: it enumerates the return contents and positions the tool as the first call for schema discovery. An agent can invoke it immediately without missing essential 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 and the schema is an empty object, so the baseline is 4. The description adds useful context about what the agent will learn, but there are no parameters to elaborate on.

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 names the tool's outputs ('columns, which of them are numeric, the row count and the provenance banner') and states the intended use ('Call this first to learn the schema'). This makes the tool's purpose specific to schema/shape discovery and clearly distinguishable from siblings like dataset_search or dataset_compare.

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 positional guidance: 'Call this first to learn the schema,' implying this is the entry-point tool before others are used. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it falls short of full exclusion 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 transparency burden. It discloses two key behaviors: rows match if the column equals any of the given values, and output order follows the order of the values. Exact-match semantics and return format are implied but not fully elaborated.

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 communicates the core behavior, order guarantee, and typical use case without repetition or filler. Every clause 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, no-output-schema tool, the description covers what is needed to select and invoke it. It could add return-format details, but 'rows of the dataset' is an adequate gloss for this simple retrieval operation.

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 the parameters. It clarifies that 'column' is the dataset column to match and 'values' are the values to compare, including that output order mirrors the order of 'values'. This goes beyond the bare 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?

States a specific verb+resource: it returns rows of the Markupbird dataset filtered by column values, preserving the given order. This clearly distinguishes it from siblings like dataset_row (single row) or dataset_search (likely text 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?

Explicitly frames the tool for "X vs Y" comparison questions, giving the agent a clear use case. It does not name alternatives or state when not to use it, but the context is strong enough to route 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. It correctly frames the operation as a read ('Read this'), lists the concrete fields returned, and implies no side effects. It does not mention rate limits or authorization, but nothing suggests risk or mutation.

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 deliver the resource, the field list, and the intended use case without filler. The most important information is front-loaded, and nothing repeats the title or schema.

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-input read with no output schema, the description is complete: it says what the agent will get and when to ask for it. The list of fields compensates for the absence of an output schema.

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 no parameters and the schema coverage is complete, so the baseline is 4. The description adds useful context by specifying what the returned provenance record contains, even though there are no parameters 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 names a specific resource (the Markupbird dataset) and enumerates the exact provenance fields (source, computed date, licence, citation). This clearly distinguishes it from sibling tools that handle columns, rows, stats, search, or comparison.

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 tells the agent when to use it: 'Read this to attribute a figure correctly.' It does not list exclusions or point to sibling alternatives, but for a zero-parameter provenance getter the stated use case is sufficient 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?

With no annotations, the description carries full behavioral disclosure responsibility. It does disclose case-insensitive exact matching, which is a useful behavioral trait, but it stays silent on return cardinality (one row vs. all matches), ordering, pagination, or error behavior.

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 no filler, and the key matching semantics are front-loaded. The grammar is slightly clunky, but it is appropriately brief for such a simple operation.

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 lookup with no output schema, the description omits what the returned rows look like, whether all matches are returned, and how to discover valid column names (a sibling dataset_columns exists but is never referenced). This leaves an agent guessing about expected results and prerequisites.

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. The sentence only implicitly assigns roles: 'column' is the field to match and 'value' is the sought value. It adds no detail about valid column names, value formatting, or matching rules beyond the case-insensitive qualifier.

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 clearly identify a row lookup by exact column-value match, and the 'exactly (case-insensitive)' qualifier distinguishes it from a fuzzy search. The phrasing is slightly awkward ('The rows ... where a column equals a value exactly'), but the intent 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?

No guidance is given on when to prefer this tool over dataset_search or other siblings, nor are exclusions or prerequisites stated. The exact-match description implies a use case, but does not explicitly frame it against 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals key behavior: case-insensitive matching, matching across any cell, and a maximum of 50 rows. It omits pagination/ordering details, but these are minor for a simple read-oriented search.

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 no filler; the core behavior, case-insensitivity, and row limit are all front-loaded. 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 simple search tool with two parameters and no output schema, the description is largely complete: it states the search scope, matching semantics, and result cap. It could mention ordering or the default limit, but an agent has enough to invoke the tool 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?

Schema description coverage is 50%: query is described but limit is not. The tool description adds the case-insensitive and any-cell semantics for the query and the 'up to 50' cap, which gives context to the limit parameter. However, it does not clarify default behavior when limit is omitted.

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 operation: returning rows of the dataset whose cells contain the query, with explicit case-insensitivity and a 50-row cap. It is distinct from sibling tools like dataset_row or dataset_top, though it does not name them as 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 description implies this is the go-to tool for free-text search across all cells, and the sibling names suggest other specialized operations, but there is no explicit statement of when to use this tool versus alternatives or any 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?

With no annotations, the description carries the burden, and it does well by disclosing preprocessing behavior ('grouping commas and currency are handled') and data cleaning ('non-numeric rows are excluded and counted'). This adds genuine behavioral context beyond the schema. It doesn't mention null handling or output shape, so it stops short of a 5.

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: it front-loads the operations, then the dataset, then the edge-case handling. Every element earns its place with no filler or redundant phrasing.

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 stats tool, the description supplies the necessary context: what the tool operates on, what it returns conceptually, and how it treats non-numeric data. The phrase 'excluded and counted' is slightly ambiguous, but the overall picture is sufficient for a simple 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 does clarify that the single parameter refers to a numeric column of the Markupbird dataset and that stats apply to that column. However, it omits details like accepted column naming conventions, case sensitivity, or error conditions if the column doesn't exist, so it's adequate but not complete.

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 six named summary statistics (count, min, max, mean, median, sum) on a numeric column of a specific dataset, which is a specific verb+resource operation. It doesn't explicitly differentiate from siblings like dataset_top or dataset_search, but the operation is distinctive enough to identify.

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 tool's use case is implied: call it when you need summary statistics for a numeric column. However, it gives no explicit guidance about when not to use it or how it compares to alternatives like dataset_top or dataset_search, leaving selection to inference.

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 describing behavior; it does disclose high/low ordering and the requirement for a numeric column. It does not mention tie handling, null values, the effect of limit, or what the returned rows look like, so transparency is only partial.

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 with no filler, and the core behavior is front-loaded before the illustrative phrase. Every element 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?

There is no output schema and no annotations, so the description should fully explain what is returned, but it omits the number of rows returned, default limit behavior, and response shape. The tool is simple, but the missing return-value context makes it incomplete for an agent invoking it without prior knowledge.

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 documentation covers only 'ascending' (33% coverage), so the description must compensate. It adds meaning by explaining that the tool ranks by a numeric column and can return either highest or lowest values, but it does not clarify the 'limit' parameter aside from what the schema already states.

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 operation as selecting the highest or lowest rows of the Markupbird dataset by a numeric column, which is clear enough to distinguish from sibling tools like dataset_search or dataset_stats. It does not explicitly name siblings, but the ranking intent 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 phrase 'which is the most/least X' implies when the tool should be used, giving the agent a concrete question it answers. However, it does not explicitly state when to avoid this tool or prefer alternatives such as dataset_stats or dataset_search.

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