One Million Tools

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the output charset (UTF-8, implying binary output is not supported), but says nothing about error behavior on malformed input, tolerance of missing padding, or URL-safe variants — significant gaps for a decode utility.

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, tightly focused clause with the transformation and output encoding front-loaded. No filler, no restating of the tool name, and nothing that could be trimmed.

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 pure function with no annotations and no output schema, the description covers the core transformation and the return type but omits error semantics and input format tolerance. It is adequate for a trivial utility but leaves real invocation questions unanswered.

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% and the single 'input' parameter has no documentation in either place. The description implicitly identifies the input as a Base64 string, which is the essential semantic, but adds nothing about format variants, padding, or whitespace handling.

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 arrow notation states a specific transformation with an explicit source type (Base64 string) and destination encoding (UTF-8 text), which is enough to distinguish it from the sibling base64_encode. It is clear but minimal — no mention of the sibling by name and no framing verb such as 'Decode'.

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 versus base64_encode or url_decode, and no indication of what happens with invalid, URL-safe, padded, or whitespace-laden input. The agent must infer all selection conditions from the description 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?

No annotations are provided, so the description carries the full burden. It discloses the core transformation and input encoding (UTF-8) and output type (Base64 string), but says nothing about padding, error handling, or character-set 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?

A single fragment with zero wasted words. The essential input/output contract is front-loaded and immediately readable.

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 encoding utility with no output schema, the description is nearly complete: it states the input type and the return format. Minor omissions like padding behavior and maximum input size are not fatal here.

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% and the single parameter has no schema-level description. The description adds meaningful input semantics by specifying that the input is UTF-8 text, partially compensating, but does not address size limits or invalid input handling.

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 transformation: UTF-8 text to a Base64 string. This clearly separates it from base64_decode by implication, but does not explicitly name or contrast with the sibling tool.

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 when-to-use, when-not-to-use, or alternative guidance is given. The arrow notation implies input and output types, but an agent must infer that base64_decode is the inverse.

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 imply a pure, side-effect-free transformation, but never explicitly says the operation is deterministic and non-mutating, nor how malformed HEX is 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?

One short sentence with the input front-loaded and the output enumerations following immediately; nothing is wasted and nothing essential is buried.

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, no-output-schema utility, the description is nearly complete—it names all four output forms—though it omits edge-case behavior for invalid or shorthand HEX input.

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 100% and the schema already documents the single 'hex' parameter including the optional leading '#'. The description adds no format or validation details beyond what the schema provides, so baseline 3 applies.

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 (convert) and resource (HEX color) plus the exact output targets (RGB/HSL/HSV/CSS). No sibling in the list performs color conversion, so the tool is trivially distinguishable.

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 makes the trigger obvious by implication (you have a HEX value and need another representation), but it states no explicit when-not conditions or alternatives, and mentions nothing about rejecting invalid inputs.

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 behavioral burden, and it discloses almost nothing: not whether the call is read-only (implied by "explain"), not the accepted cron dialects (5-field vs 6-field with seconds, @daily macros), and not how invalid input is handled. For a low-risk lookup tool this is tolerable but thin.

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 short sentence with the purpose front-loaded and zero filler. It is arguably too terse rather than too verbose, but nothing in it is wasted.

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 utility with no annotations and no output schema, the description covers the core contract (cron in, English out) but omits accepted expression formats and error behavior. Acceptable minimum viability, not complete.

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% and the single "expression" parameter is an untyped-in-practice string with no documented format. The description does not compensate by stating which cron syntax variants are accepted, which is the main thing an agent needs to supply a valid value.

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 gives a specific verb ("Explain") and resource ("a cron expression") plus the output form ("in plain English"). No sibling tool touches cron, so there is nothing to disambiguate against, but the purpose is unambiguous on its own.

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 only implied: an agent would infer this is for turning a cron string into readable text. There is no statement of when to prefer it, what input forms are accepted, or what happens with an invalid expression, but with no competing sibling the implied usage is adequate.

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 behavioral disclosure burden. It does not state that this is a pure, side-effect-free transformation, does not describe the returned CSS format, and does not mention how zero/default values or invalid inputs 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?

The description is a single, front-loaded sentence with no filler. Every word contributes to identifying the operation and its inputs.

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 four-parameter transformation tool with no output schema and no annotations, the description is too thin. It does not explain the output format, the ordering or abbreviation meaning of the corner parameters, or the default behavior when inputs are omitted.

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%, and the four abbreviated parameters (bl, br, tl, tr) are undocumented in the schema. The description adds the unit ('px') and the general concept ('corner radii'), but never maps the abbreviations to bottom-left, bottom-right, top-left, and top-right, nor mentions the default of 0.

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: 'Build a CSS border-radius value'. It also identifies the input domain ('four corner radii in px'), which makes the tool distinguishable from most siblings. It does not explicitly differentiate itself from nearby siblings like css_box_shadow_build, but the resource is clear.

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 no when-to-use or when-not-to-use guidance and names no alternatives. It only states the mechanical purpose, leaving the agent to infer that this is the tool for constructing a border-radius value when other CSS formatting tools exist.

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, yet it says nothing about the returned representation (presumably a CSS string), layer ordering, or how the per-layer 'opacity' and 'color' inputs are combined into the emitted shadow. For a generator whose output is the whole point, this is a meaningful gap.

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 and the essential concept front-loaded. It is efficiently sized, though the brevity contributes to the missing detail noted in other dimensions.

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 tool taking a nested object array with seven sub-properties, no annotations, and no output schema, the description omits layer field meanings, default behavior, and output format. An agent cannot confidently construct a valid multi-layer call from this text 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?

Schema description coverage is 0% and the single parameter is a nested array of objects with seven undocumented sub-fields (offsetX, offsetY, blur, spread, color, inset, opacity). The phrase 'one or more layers' acknowledges the array but conveys nothing about what a layer contains, leaving the 0% coverage uncompensated.

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 ('build') and resource ('CSS box-shadow value') with the input shape ('one or more layers'). It is clearly distinguishable from siblings like css_gradient_build and css_border_radius_build by the resource name, though it largely restates the tool name rather than adding new framing.

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 statement of when to use this versus alternatives, no prerequisites, and no mention of related tools such as css_format or css_contrast_check. The agent must infer usage entirely from the name.

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. It usefully discloses the evaluation standard (WCAG) and that a pass/fail verdict (AA/AAA) is produced, implying a pure, side-effect-free computation. However it omits the accepted color input formats and any assumptions about transparency/alpha, which are the main behavioral unknowns for this 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?

A single front-loaded sentence stating operation, standard, and result. Nothing is wasted and nothing is buried.

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?

No output schema exists, but the description does summarize the return (ratio plus AA/AAA verdict), which covers the essentials. The unresolved gap is input color-format handling, which an agent needs before it can call this reliably.

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 only implicitly names the two inputs via 'between two colors'; the schema's 'foreground'/'background' names are self-evident, but no accepted format (hex, rgb(), named, alpha) is documented anywhere, leaving real ambiguity.

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 gives a specific verb and resource ('Compute the WCAG contrast ratio between two colors') plus the output artifact ('AA/AAA verdict'), which is more than a tautology. It does not name or contrast itself against any sibling (e.g. color_convert), so it stops short of full sibling 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?

Usage is implied rather than stated: an agent can infer this is the accessibility-contrast tool as opposed to the general color_convert sibling. There is no explicit when-to-use/when-not guidance and no mention of 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 full behavioral burden, and it discloses almost nothing. It implies a non-mutating formatting operation but says nothing about error handling for invalid CSS, whether comments/whitespace are preserved, or how the result is 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?

One short, front-loaded sentence with no wasted words, but its brevity reflects under-specification rather than efficient density. It neither buries anything nor says enough to be considered well-structured.

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 three parameters, no annotations, and no output schema, the description is the only place an agent can learn behavior, and it provides a single fragment. Too much essential context (usage, error behavior, parameter meaning) 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?

Schema description coverage is 0% and the description mentions no parameters at all. The three parameters (input, indent, printWidth) get no semantic explanation beyond the enum values and defaults that the schema already exposes, so the description adds zero value here.

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 verb ('pretty-print') and resource ('CSS'), which is enough for an agent to distinguish it from the format siblings html_format, js_format, json_format, and yaml_format. It is clear but offers no explicit sibling differentiation, so it falls short of a 5.

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 the other formatters, no mention of prerequisites (e.g., the input must be valid CSS), and no exclusion conditions. The agent is left to infer usage entirely from the tool name.

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 does disclose the output type (a CSS string) and implicitly a pure, side-effect-free transform, which is the main behavioral fact for this tool, but it omits the default angle behavior, the minimum two-stop requirement, and whether output is validated/sanitized.

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 front-loaded sentence with zero filler; the resource and required inputs appear immediately. Nothing is repeated from the schema and nothing extraneous is included.

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 pure function with no output schema, the description conveys the resource, the inputs, and the return type, which is nearly everything needed. The remaining gap is the stop-item semantics (color format and position scale), which the schema only partially covers.

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 only 50%: angle is documented in the schema ("Degrees; 0 = top-to-bottom") but stops and its color/position fields are not. The description names both parameters but adds no format detail (color syntax, position units, stop ordering) beyond what the schema already structures, so it only partially compensates for the 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?

States a specific verb ("Build"), a specific resource ("CSS linear-gradient string"), and the inputs it consumes ("color stops + angle"). Among siblings it is the only gradient builder, so it is unambiguously distinguishable from css_box_shadow_build, css_border_radius_build, and css_format.

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 says what the tool produces but gives no when-to-use context, no prerequisites, and no alternatives (e.g., when to hand-write CSS vs. use css_format). The agent must infer that this is the tool to reach for whenever a linear gradient string is needed.

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 full behavioral burden. It never states what happens on no matches, how invalid regex is handled, whether it mutates input, or what the output looks like. Only the predicate mode list is conveyed, which is thin for a mutation-adjacent transform 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?

A single front-loaded sentence that states the core action first and packs the supported operators inline. No filler, though the parenthetical list leans on the enum rather than adding new information.

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 5 undocumented parameters, zero annotations, and no output schema, the definition is far too sparse. An agent lacks the input format, return shape, and error behavior needed to invoke it confidently.

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% across 5 params, yet the description only echoes the mode enum already present in the schema. It gives no meaning for input (CSV text vs file path), column (name vs index), value, or caseSensitive behavior.

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 (Filter) and resource (CSV rows) with the scoping mechanism (column predicate) and enumerates supported modes. An agent can distinguish it from csv_sort or csv_statistics, though 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?

The description never says when to choose csv_filter over csv_parse, csv_sort, or csv_statistics, nor any preconditions (e.g. header handling, required input format). Usage is only implied by the verb.

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 behavioral burden. It implies a read-only parse and mentions delimiter detection, but says nothing about size limits, how malformed rows are handled, or whether output is truncated to previewRows only.

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 front-loaded sentence with no filler, stating resource, transformation, and output up front. It is efficient, though arguably terse for a tool with three (0% documented) parameters.

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 annotations, the description should explain the return structure more fully than 'fields + preview rows'. It orients the agent adequately but leaves output format, limits, and error behavior unspecified.

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% for 3 parameters, so the description must compensate. It touches two of them (delimiter detection, preview rows) but never explains the required input string, the enum values, or the 1-500 previewRows bound.

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 (parse), resource (CSV text), and output shape (fields + preview rows), plus the delimiter-detection feature. This clearly separates it from siblings like csv_to_json, csv_filter, and csv_sort, though it never names an alternative.

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 statement of when to use this tool versus the many csv_* and json_to_csv siblings, nor any precondition such as malformed-input handling. The purpose is implied by the name alone, leaving the agent to infer routing.

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 behavioral burden. It says nothing about header handling, whether sorting is stable, what happens with non-numeric values in numeric mode, how the delimiter is inferred, or what the tool returns — significant gaps for a data-transformation 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?

A single front-loaded sentence with no filler; the core action and the mode options come first. It is efficient, though its terseness contributes to the gaps in other dimensions.

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, no output schema, and 0% parameter description coverage for a 5-parameter mutation-style tool, the description is too thin. An agent cannot determine delimiter behavior, direction defaults, or header handling from the definition 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?

Schema description coverage is 0% across 5 parameters. The description only loosely covers 'column' and 'mode' (alpha/numeric) and says nothing about 'delimiter', 'direction' (asc/desc), or the 'input' format, leaving half the parameters undocumented in both schema and prose.

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 (sort) and resource (CSV rows) with the key constraint 'by one column' and hints at the mode dimension (alpha or numeric). It clearly distinguishes from unrelated siblings like csv_filter or csv_statistics, though it doesn't explicitly name a sibling to route against.

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 statement of when to use this over alternatives such as csv_filter or csv_to_json, and no prerequisites or conditions. The only usage signal is implicit: if you need sorted CSV rows.

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 burden, but it omits critical details: whether input is CSV text or a file path, where output files are written, whether the operation is read-only or destructive, and what is returned. It only conveys the high-level transformation.

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 redundant or filler content. Every word contributes to stating the core 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?

Given four parameters, no annotations, no output schema, and only 50% schema description coverage, the description is insufficient. It does not explain the expected input format, output destination, side effects, or how the two split modes operate in practice.

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 implicitly links 'N chunks' to the chunks parameter and 'files of N rows each' to rowsPerChunk, adding some meaning beyond the schema's brief descriptions. However, it does not explain the input parameter (CSV string vs. path) or the mode parameter's two enum values, and schema coverage is only 50%.

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 ('Split') and resource ('CSV'), and clarifies the two splitting modes (by N chunks or by N rows). It does not differentiate this tool from sibling CSV utilities (e.g., csv_parse, csv_filter), so it falls short of a 5.

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 no guidance on when to choose this tool over alternatives like csv_parse or csv_filter, nor does it explain when to use byChunks versus byRows. It only restates the operation.

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 behavioral burden and falls short: it does not say whether 'input' is a file path or raw CSV text, whether it touches the filesystem, or how many columns/rows are supported. It does at least imply a read-only computation, but key operational behavior is undisclosed.

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?

One compact sentence that front-loads the verb and resource and lists the returned statistics without any wasted words.

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, the description helpfully names the metrics returned, but it leaves the required 'input' parameter's format undefined and omits any note about which columns are numeric-only or how non-numeric columns are handled. Adequate but with a real gap for a single required param.

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 single parameter 'input' has 0% schema description coverage, so the description must compensate. 'over a CSV' hints the argument is CSV-related but never clarifies whether it expects a path, raw CSV content, or a URL, leaving a critical ambiguity for a required parameter.

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 (compute) and resource (per-column summary statistics over a CSV) and enumerates the exact statistics produced (count, min, max, mean, median). This implicitly separates it from csv_filter, csv_sort and csv_split, though it never names an alternative 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?

There is no guidance on when to use this tool versus the other csv_* siblings, nor any stated prerequisites or exclusions. The usage context must be inferred entirely from the name and description.

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 it does disclose the return shape (a JSON array of objects), which implies header-row parsing. It says nothing about delimiter handling, quoting, malformed-input errors, or what happens with an empty or headerless input, which are the real behavioral risks for a parser.

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?

One short, front-loaded sentence with no filler. It is efficient, though the brevity leaves no room for the usage or behavioral detail an agent would want.

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 single-parameter, low-complexity conversion tool with no output schema or annotations, the description covers the basic contract. It omits header assumptions, delimiter behavior, and error handling, which are exactly the gaps structured fields do not fill here.

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% for the single 'input' parameter, but the description's phrase 'CSV text' effectively tells the agent what to pass. It adds no format, size, or encoding detail beyond that 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?

States a specific verb and resource ('Parse CSV text into a JSON array of objects'), so the transformation is unambiguous. However, it does not distinguish itself from the sibling 'csv_parse' or acknowledge its inverse 'json_to_csv', so an agent cannot tell which parsing tool to pick from the text alone.

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 on when to use this versus csv_parse, csv_filter, or json_to_csv, and no preconditions such as whether the CSV must have a header row. The agent must infer applicability entirely from the tool name.

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 behavioral burden. It says what is removed but not what the operation returns (redacted text vs. a report of findings), what the replacement marker is, whether the input is mutated, or whether any secret classes are missed or require configuration.

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 tight sentence with the verb and target front-loaded and zero filler. Nothing in it is redundant or padded.

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 tool with no annotations, no output schema, and an undocumented parameter, the description should clarify input format and result shape. It leaves the agent guessing about what comes back after redaction and how secrets are masked.

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 single 'input' parameter has 0% schema description coverage, so the schema explains nothing. The description's phrase 'logs / config / env text' hints that input is raw text content rather than a file path, but it never states the expected format, size limits, or whether structured formats are 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?

States a specific verb ('Redact') and resource ('secrets (API keys, tokens, passwords) from logs / config / env text'), so the agent immediately knows what transformation is performed. No sibling tool in the list performs redaction, so it is implicitly distinct, though the description never explicitly contrasts itself with 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. The agent must infer from the name alone that this is the right tool when handling text that may contain secrets.

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 the two key behavioral facts an agent needs: the output is a hex digest string and the input is treated as UTF-8 text (deterministic, no side effects implied). It does not state that the operation is stateless/read-only or mention the default algorithm, but for a pure local computation the remaining gaps are minor.

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 front-loaded sentence with no filler; the verb, the resource, the algorithm set, and the output format all land in one pass.

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?

No output schema exists, but the description itself explains the return value (hex digest), and both parameters are covered, which is enough for a two-parameter pure function. The unstated default algorithm and the unaddressed sha256_hash overlap are the only real omissions.

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 — and it largely does, naming all three algorithm values and the UTF-8 text encoding of the input, covering both parameters. The only omissions are the SHA-256 default and the absence of a parameter/field mapping (e.g. that 'algorithm' is optional).

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 and resource ('Compute a ... hex digest of UTF-8 text') plus the exact output form, which clearly separates it from siblings like base64_encode or uuid_generate. It does not, however, acknowledge the overlapping sibling sha256_hash, so the agent gets no help choosing between the two.

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 when-to-use guidance at all. With a sibling named sha256_hash sitting in the same toolkit, the single most useful sentence here would have been 'use this instead of sha256_hash when you need SHA-1 or SHA-512', and it is absent.

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 behavioral burden. It says only that the tool pretty-prints HTML, without disclosing whether it returns a string, modifies in place, preserves original content, or has any side effects. This is a significant gap for a transformation 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 short sentence with no wasted words, but it is under-specified for a tool with three undocumented parameters. Brevity here reflects missing information rather than efficient communication.

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 zero annotation coverage, zero schema description coverage, no output schema, and three parameters, the description is drastically incomplete. An agent would have to guess what input expects, what indent and printWidth do, and what the tool returns.

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%, and the description adds no meaning for any of the three parameters (input, indent, printWidth). The schema itself only defines types, an enum for indent, and a numeric range for printWidth, but no explanatory text. The description does nothing to compensate.

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-resource pair: pretty-print HTML. It is clear what the tool does, and no sibling tool performs HTML formatting. However, it does not explicitly distinguish itself from adjacent format tools like css_format or js_format.

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 or prerequisites. The description only states the core action and offers no context such as when formatting is appropriate or what input shapes are acceptable.

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 names Prettier (useful, implies opinionated deterministic formatting) but says nothing about side effects, whether syntax errors throw, idempotency, or that it is a pure transform rather than a file-writing operation.

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 front-loaded sentence with no filler or repetition. It is efficient, though arguably too terse given the six undocumented parameters.

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 6-parameter tool with no annotations and no output schema, the description omits parameter behavior, error/parse-failure handling, and return format. An agent knows what the tool is for but not how to drive 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?

Schema description coverage is 0% across 6 parameters, and the description mentions none of them. Parameter names like semi, singleQuote, printWidth, indent and parser are largely self-explanatory from their enums/defaults/bounds, which earns minimal credit, but the description adds zero meaning over 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?

States a specific verb+resource ('Pretty-print JavaScript / TypeScript') and names the engine (Prettier), which cleanly separates it from json_format, css_format, html_format and other siblings by input language. It stops short of explicitly contrasting with those siblings, but the resource 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 when-to-use or when-not-to-use guidance and no alternative named, even though the sibling list contains several other format tools whose boundaries (JS vs JSON vs TS) an agent might want confirmed. Usage is only implied by the tool name.

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 behavioral burden. It does disclose the return semantics (added/removed/changed keys), which is genuine value, but it omits whether the diff is deep/recursive or top-level only, and what happens on invalid JSON input.

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 front-loaded sentence with zero filler. It is appropriately tight for a simple utility, though it is terse enough that it leaves the gaps noted in other dimensions.

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 pure stateless diff utility with no output schema and no annotations, the definition is minimally adequate but thin. The output structure (flat key list vs. nested paths, change representation) is never described, and input format expectations are unstated.

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% and the two parameters ('left', 'right') are undocumented in both schema and description. The phrase 'two JSON documents' loosely maps them to the two operands, but it does not clarify whether they are raw JSON strings, file paths, or how nested structures are compared, leaving a real gap at full required-parameter coverage.

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 verb ('Diff'), the resource ('two JSON documents'), and the output shape ('added/removed/changed keys'). This implicitly separates it from the sibling text_diff, since key-level diffing is a different operation than textual line diffing, though 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?

There is no guidance on when to use this tool versus text_diff, nor any prerequisites such as whether inputs must be valid JSON or how malformed input is handled. The agent must infer usage entirely from the one-line purpose statement.

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 the indentation options but says nothing about behavior on malformed JSON input, whether input is validated, or that the operation is a side-effect-free transformation.

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 front-loaded sentence with no filler; every clause earns its place by stating the operation and the one configurable option.

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 transformation with no output schema and no nested objects, the description covers purpose and the indentation choice adequately. Only the error behavior on invalid JSON is left unstated, which is 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 coverage is 50% (only `input` is documented). The description compensates for the undocumented `indent` parameter by explaining that the values mean 2 spaces, 4 spaces, or a tab character, adding real meaning beyond the bare enum 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?

States a specific verb (pretty-print) and resource (JSON) plus the configurable indentation, so the agent knows this reformats rather than validates or minifies. It does not explicitly name siblings like json_minify/json_validate, but the verb alone cleanly separates it.

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 by the verb: use this to reformat raw JSON text. There is no explicit when/when-not guidance or naming of the adjacent tools (json_minify, json_validate, json_diff) that an agent might confuse it with.

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 the transformation but says nothing about behavior on malformed JSON (error vs. passthrough), whether the input is validated, or that output is a pure string transform with no 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?

A single tight sentence with the action front-loaded and zero filler. It is efficient, though its brevity contributes to the documentation gaps noted elsewhere.

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 trivial one-parameter transform with no output schema, the description is minimally adequate, but it omits the input format expectation and error behavior on invalid JSON, both of which an agent would benefit from knowing.

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?

One parameter ('input') with 0% schema description coverage, so the description must compensate. It says nothing about what 'input' should contain (a JSON string? a file path?), leaving the sole parameter undocumented in both schema and description.

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 and resource: 'Compress JSON by stripping whitespace.' This is clearly distinguishable from the sibling json_format, which does the opposite, even though the description does not name that sibling 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?

No when-to-use guidance, no mention of the natural alternative (json_format for pretty-printing), and no conditions or prerequisites. The agent must infer the use case from the verb 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?

No annotations are provided, so the description carries the full behavioral burden. It states the basic operation but omits output format details (headers, delimiter, quoting), handling of nested or non-flat objects, error behavior, and explicit indication that the conversion is pure/read-only.

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 front-loaded sentence with no filler or redundancy. It is appropriately sized for a simple converter 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 converter with 100% schema coverage, the description covers the core operation. However, with no output schema and no annotations, it leaves output formatting and edge-case behavior such as nested objects and errors unstated, making it minimally adequate rather than fully complete.

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 100%, so the schema already documents the single input parameter. The description says 'array of JSON objects' but adds no syntax, format, or constraint detail beyond what the schema states as 'JSON array of flat objects'.

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 and resource: convert an array of JSON objects to CSV. It is clear and unambiguous, but does not differentiate from siblings such as csv_to_json or json_to_yaml, so it lacks explicit sibling routing.

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 when-to-use guidance, alternatives, prerequisites, or exclusions are provided. The only implied usage is the obvious conversion direction, which is already conveyed by the tool name.

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 behavioral burden, yet it discloses nothing beyond the name: no behavior on malformed/invalid JSON, no statement about output formatting or key ordering, and no error semantics. For a pure conversion function these gaps are small but real.

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 front-loaded sentence with zero waste — appropriately sized for a trivial utility. It is terse enough that no sentence needs cutting, though it could carry one more clause of useful detail at no structural cost.

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, no-output-schema converter with no annotations, the description is minimally adequate: the operation and direction are unambiguous. It omits error behavior and input-format expectations, which an agent would need when handling edge cases.

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 is one parameter ('input') with 0% schema description coverage, so the description must compensate. 'Convert JSON to YAML' does imply that the input is a JSON string, which adds some meaning, but it gives no detail on accepted forms (object, array, scalar) or whether the input is a string vs. file path.

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 and resource (convert JSON to YAML), and the direction of conversion inherently distinguishes it from the yaml_to_json sibling. It stops short of explicitly naming or contrasting that sibling, so it is clear but not maximally differentiated.

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 by the conversion direction — an agent can infer 'use this when you have JSON and need YAML' — but there is no explicit when-to-use, no prerequisites, and no mention of alternatives such as json_format or yaml_format.

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. It does disclose the two return shapes (parsed value on success, line/column error on failure), which is genuinely useful given there is no output schema. However, it says nothing about side-effect freedom, idempotency, input size limits, or handling of duplicates/trailing content.

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 front-loaded sentence with no filler; the primary action leads and the outcome contract follows. Nothing to trim.

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, no-output-schema utility, the description covers what the tool does and what it returns, including the error shape with line/column detail. Only minor gaps remain around input constraints and side effects.

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% and the single 'input' property has no description, but the tool context makes clear it is the JSON text to validate. One self-evident string parameter means the baseline expectation is modest; the description neither adds format guidance (e.g., raw string vs file) nor misleads.

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 and resource ('Validate JSON syntax') and even previews the outcome (parsed value or line/column error). It is clearly distinct from siblings like json_format and json_minify, though it never names an alternative to reinforce that distinction.

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 use case (checking whether JSON is well-formed) is implied by the verb 'validate' but never spelled out as when-to-use versus json_format or json_to_yaml. No prerequisites, no exclusions. Adequate but leaves routing to the agent's 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 present, the description carries the full burden, and it discloses the single most safety-relevant trait: no signature verification, meaning the payload is unauthenticated data. It omits error behavior on malformed tokens and any statement about whether the operation is a pure read, which are smaller gaps for a stateless decode function.

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 short sentences, zero waste, with the positive capability stated first and the critical caveat immediately after. Nothing needs trimming or reordering.

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 single-parameter, no-output-schema tool this covers what is returned (decoded header + payload) and the key trust caveat. A brief note on the shape of the result or behavior on invalid input would make it fully self-sufficient without 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?

Schema description coverage is 0% for the single 'token' parameter, so the description must compensate. It implies the input is a JWT (by naming header and payload) but adds no format detail, no example, and no note on whether the token may include the 'Bearer ' prefix.

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 (decode) and resource (JWT header + payload), and scopes the operation precisely to the two decoded segments. This is immediately distinguishable from the neighboring base64_decode/url_decode tools, which operate on opaque strings rather than JWTs.

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 sentence 'Does not verify the signature' gives the agent an explicit condition under which the output must not be trusted, which functions as an implicit when-not-to-use signal. It stops short of naming an alternative (e.g., a verify/validate tool) or stating a positive use case such as debugging or inspecting claims.

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 says the output is 'normalized' but never states whether the original input is mutated or a new string is returned, whether the operation is idempotent, or what the caller gets back. For a transformation tool with zero annotation coverage this is a meaningful gap.

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 front-loaded sentence with no filler; the specifics are packed into the parenthetical. It is efficient, though the extreme brevity contributes to the gaps elsewhere.

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 4-parameter tool with 0% schema description coverage, no annotations and no output schema, the description leaves too much unstated: return behavior, enum options, defaults, and edge-case handling. The simple nature of the tool limits the damage, but an agent still lacks key information.

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 should compensate, and it does partially: the three named normalizations map onto listMarker, trimTrailing and collapseBlankLines. However it never explains the accepted marker values (the enum in the schema), the defaults, or what the required 'input' parameter expects.

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 ('Normalize') and resource ('Markdown'), and the parenthetical enumerates the transformations (list markers, trailing whitespace, blank lines), which distinguishes it from siblings like markdown_preview or markdown_to_html that convert rather than clean up. It doesn't explicitly name those siblings, keeping it below a 5.

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 indication of when to reach for this tool versus markdown_preview or markdown_to_html, no prerequisites, and no when-not-to-use guidance. The agent must infer the intended use case from the one-line purpose.

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 supplied, so the description carries the full burden, and it discloses almost nothing beyond the purpose sentence. It does reveal two behavioral traits — the output is a fragment (not a full document) and counts are computed — but says nothing about safety, side effects, or how gfm/breaks alter rendering.

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 front-loaded sentence with no filler or redundancy; the verb and output type come first. It is arguably under-specified rather than concise, but as a structure judgment it wastes no words.

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, no output schema, and 0% parameter description coverage on a 3-parameter tool, the description should compensate for all of that and does not. An agent cannot tell from this text how gfm or breaks change the result, nor why to pick this over the sibling Markdown tools.

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 input, gfm, and breaks, and it explains none of them. 'Render Markdown' weakly implies the input is Markdown source, but the two boolean flags controlling GFM and line-break behavior are entirely undocumented in both schema and description.

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 gives a specific verb and resource ('Render Markdown to an HTML preview fragment') plus the added detail that word/char counts are included, so the core function is unambiguous. However, it never distinguishes itself from the closely named siblings markdown_to_html and markdown_format, which an agent could easily confuse it with.

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 statement of when to choose this over markdown_to_html or markdown_format, nor any precondition or exclusion. The agent must infer the 'preview' use case purely from the word 'preview' in the description.

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 behavioral burden and delivers almost nothing. It does not say whether output is a full HTML document or a fragment, whether the HTML is sanitized, what happens with embedded raw HTML, or what errors look like. Only the one-way direction of the transform is conveyed.

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 single sentence is front-loaded and free of padding, but its brevity comes from omission rather than economy. It is efficient yet too thin to actually guide invocation.

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, no annotations, and zero parameter documentation, the description needed to compensate and does not. Key facts an agent needs, such as output shape and the semantics of the gfm/breaks defaults, are missing entirely.

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% across all three parameters, and the description adds no parameter meaning whatsoever. The `gfm` and `breaks` flags in particular are non-obvious and are left completely unexplained, so the agent cannot reason about their effect on output.

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 clear verb+resource pair ('Convert Markdown to HTML'), but it is essentially a restatement of the tool name and gives no differentiation from nearby siblings such as markdown_format, markdown_preview, or html_format. An agent can infer the transformation, yet cannot tell from this text why it would choose this tool over markdown_preview.

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 when-to-use guidance, no mention of prerequisites, and no routing to alternatives. With markdown_format and markdown_preview as siblings, the absence of any distinction leaves selection entirely to the agent's guesswork.

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 it discloses almost nothing beyond the obvious: no indication of output format (HTML snippet, JSON, string), whether inputs are validated or escaped, or whether any external fetch occurs (e.g., for url/imageUrl). For a 10-parameter generator this is a significant gap.

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 front-loaded sentence with no filler, which is appropriately compact. It is arguably too terse for a 10-parameter tool, 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?

Given 10 parameters, 0% schema coverage, no annotations and no output schema, the description should do more work. It never says what form the generated tags take or how the many optional inputs influence the output, so an agent cannot invoke it confidently.

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% across 10 parameters, so the description is the only place semantics could be added. It names only title and description explicitly and gestures at OG/Twitter tags generally, leaving url, type, author, locale, imageUrl, siteName, canonical and twitterHandle entirely unexplained.

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 (generate) and resource (title, description, Open Graph and Twitter Card tags), so an agent knows exactly what the tool produces. It does not, however, differentiate itself from the sibling og_preview_build, which covers overlapping Open Graph territory.

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 such as og_preview_build, nor any stated prerequisites or context. The sentence simply describes what it emits, leaving the agent to infer the use case.

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 disclosure burden. It hints at 'truncation' but never explains the truncation rules, whether url/imageUrl empty defaults are tolerated, whether anything is fetched over the network, or what the resulting bundle looks like. For a generation tool with zero structured metadata, this leaves behavior largely opaque.

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?

One compact sentence with the resource front-loaded and no filler. It is appropriately sized, though the ambiguous 'domain truncation' parenthetical is the least informative part of it.

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 5 undocumented parameters, no annotations, and no output schema, the description would need to explain the shape of the produced bundle and the truncation behavior. It does neither, so an agent cannot confidently predict the result of calling it.

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% across 5 parameters, so the description must compensate and it barely does. It names title and description (the two required params) but says nothing about url, siteName, imageUrl, their empty-string defaults, or what happens when they are 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 verb 'Build' plus the resource 'Open Graph preview metadata bundle' is a concrete verb+resource pair, and the parenthetical names the fields involved. However, the phrase 'domain truncation' is cryptic and the description never distinguishes this tool from the sibling meta_tags_generate, which an agent could easily confuse it with.

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 when-to-use guidance and no mention of alternatives. Given that meta_tags_generate sits in the sibling list and produces overlapping output, the absence of any routing hint is a real gap.

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. It states that matches and capture groups are returned, but says nothing about invalid-pattern handling, default flag behavior, or what happens when 'replacement' is supplied (does it return a replaced string instead of matches?).

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 efficient sentence with the core action front-loaded and no filler. It is well-sized, though its brevity comes at the cost of omitting behaviorally important detail rather than through disciplined editing.

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 four-parameter tool with no annotations and no output schema, the description is too thin: it does not explain the replacement mode, error behavior, or flag semantics. An agent cannot confidently invoke it beyond the trivial pattern+input case.

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 75%, and the description adds essentially nothing about the four parameters. 'pattern', 'flags' and 'replacement' are documented only in the schema, and 'input' is undocumented in both places, so the description fails to compensate 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?

States a specific verb and resource ('Test a regular expression against input text') and even names the return shape ('matches with capture groups'). It is unambiguous, though it does no sibling differentiation work (no sibling is a regex tool, so this is a minor gap).

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 versus alternatives, and no mention of prerequisites. Crucially, it never explains the interaction between 'testing' and the optional 'replacement' parameter, which changes the tool's behavior entirely.

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, but this is a pure deterministic computation with no side effects, permissions, or state to disclose, which lowers the bar. It does state the input encoding (UTF-8) and the output form (hex digest), though it does not specify digest casing, length, or determinism explicitly.

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, front-loaded sentence with zero filler; the algorithm and output format lead and nothing is wasted.

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, side-effect-free computation with no output schema, the description covers the essentials: algorithm, input encoding, and output representation. The only real gap is the lack of routing guidance against the sibling hash_text.

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% for the single 'input' parameter, so the description must compensate. It does add genuine meaning by specifying the input is UTF-8 text (an encoding assumption the schema does not state), but nothing else about the parameter is clarified.

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 and resource ('Compute a SHA-256 hex digest') with the input encoding qualifier, which is far more precise than the sibling base64_encode or a generic hash tool. However, it never distinguishes itself from the sibling hash_text, which plausibly overlaps in scope, so an agent cannot tell from the text alone which one to pick.

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 when-to-use guidance, no exclusions, and no mention of the sibling hash_text, which is the most likely alternative for hashing. The only usage signal is inferable from the name itself.

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 disclose behavior on its own. It states the basic operation but omits all transformation behavior (lowercasing, accent stripping, separator choice, repeat collapsing, length limiting) and any error or idempotency behavior, which is a significant gap for a six-parameter utility.

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 single sentence is front-loaded, grammatical, and free of waste. However, it is under-specified for a tool with six parameters and no annotations, so it is concise without being appropriately sized for the task.

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, no output schema, 17% schema description coverage, and six parameters, the description should carry substantial explanatory weight. Instead it offers only one sentence and leaves parameter behavior and transformation rules completely undocumented.

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 only 17%, and the description does not mention any of the six parameters. Options like lowercase, separator, maxLength, stripAccents, and collapseRepeats are left entirely to schema names and defaults, so the description adds no meaning beyond the structured fields.

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 verb ('Generate') and resource ('URL slug') and states the input source ('from a string'). It clearly distinguishes the tool's purpose from siblings like url_encode or uuid_generate, though it does not explicitly compare itself to them.

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 no explicit when-to-use guidance, no exclusions, and no alternatives. It does not tell an agent when to choose slug_generate over url_encode or other text utilities.

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 behavioral burden, and it discloses nothing: not whether malformed SQL raises an error or is returned unchanged, whether comments/whitespace are preserved, or whether the operation is idempotent. For a transformation tool with four options this is a significant gap.

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 words are not concise so much as under-specified for a four-parameter tool. There is no wasted text, but there is also nothing front-loaded that an agent could act on.

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 four parameters (three enums), no annotations, no output schema, and no parameter documentation, the definition leaves the agent without enough context to choose a dialect or predict the result. The minimum needed to call it correctly 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?

Schema description coverage is 0% and the description says nothing about indent, dialect, or keywordCase. The enum values hint at valid choices, but there is no explanation of dialect effects on output or how keywordCase interacts with the input, so the description adds no meaning beyond 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?

"Pretty-print SQL" states a specific verb (pretty-print) and resource (SQL), which is unambiguous and easily separable from siblings like json_format or css_format. It stops short of describing scope (validation, comment handling, error behavior), so it is clear but thin.

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, no prerequisites, and no statement about whether it also validates or only reformats. The agent must infer everything from the name.

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 does disclose the return shape ('part-level changes plus a unified string'), which is useful since there is no output schema, but it omits any behavioral detail about mode defaults, input size limits, or how the two return representations relate.

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 front-loaded sentence with no filler; the verb, inputs, and return format all appear immediately. Nothing in it is redundant.

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, no output schema, and 0% schema coverage on three parameters, the description should do more. It explains the return shape only vaguely and leaves the mode parameter and its default completely unaddressed, so an agent cannot confidently invoke it without opening the 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?

Schema description coverage is 0% and the description only implies the two text inputs via 'two texts'; it never mentions the 'mode' parameter, its 'lines'/'words' enum values, or the 'words' default. The single non-obvious parameter, which materially changes output, is left entirely undocumented.

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 ('Diff') and resource ('two texts') and names the output shape, which distinguishes it from siblings like json_diff (which diffs JSON, not free text). It stops short of explicitly routing against any sibling, so it is clear but not maximally differentiated.

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 versus json_diff or when to choose 'lines' vs 'words' mode. The agent must infer that this is for plain text and that mode selection is meaningful, with no conditions or exclusions 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 full burden. It discloses the accepted input shapes and the output format, which is useful, but says nothing about the default unit/timezone behavior, invalid or out-of-range input handling, or that this is a pure side-effect-free computation. The core behavior is inferable, but several traits are left to the schema's defaults.

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 action, the input, and the output. Every clause earns its place with no 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?

With no output schema, the description correctly states the return type as an ISO 8601 string. For a simple pure converter this is nearly complete; the remaining gap is that it never addresses the zone parameter or default unit behavior, which matters for the accuracy of the returned string.

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 33% (only 'input' is documented), so the description must compensate. Saying 'seconds or ms' usefully explains the meaning of the unit enum values, but the zone parameter ('local' vs 'utc') is never mentioned and its default behavior is unexplained.

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 verb (Convert), a specific resource (Unix timestamp), the accepted input variants (seconds or ms), and the exact output format (ISO 8601 date string). No sibling tool does timestamp conversion, so no differentiation is needed, and an agent can tell instantly what this does.

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 by the description — you call this when you have a numeric Unix timestamp and want a human-readable date — but there is no explicit when-to-use guidance, no mention of edge cases, and no alternative tool referenced. With no near-neighbor sibling, the lack of routing guidance is tolerable but not helpful.

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 behavioral burden, and it adds almost nothing beyond the verb. It does not say how malformed or partially-encoded input is handled, whether '+' is treated as space, or how the mode setting changes results — all of which matter for a decode operation.

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 front-loaded sentence with zero filler, appropriate for a simple utility. It is efficient rather than bloated; the weakness is under-specification, not verbosity.

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, no output schema, and 0% schema coverage, the description is the only documentation available and it omits the mode enum semantics and any error/edge-case behavior. For a two-parameter tool whose enum materially changes output, that leaves a genuine 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% across two parameters, so the description is the only source of parameter meaning. It implies that 'input' is the encoded text, but says nothing at all about 'mode', whose component/uri enum encodes a meaningful decoding difference (e.g., plus-sign and reserved-character handling) that an agent cannot guess.

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 and resource ('Decode percent-encoded URL text'), which cleanly distinguishes it from the inverse sibling url_encode and from unrelated decoders like base64_decode or jwt_decode. It stops short of explicitly naming the counterpart tool, but the name-plus-description pairing leaves no ambiguity about what it does.

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 reach for this tool versus url_encode, base64_decode, or jwt_decode, and no mention of the component-vs-uri distinction that determines correct behavior. The agent must infer all selection logic from the tool 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 behavioral burden. It states that percent-encoding occurs, but does not explain mode-specific behavior, such as which reserved characters are preserved or encoded in component versus full URI mode, nor how spaces or delimiters 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?

The single sentence is front-loaded, free of filler, and appropriately sized for a simple two-parameter utility. Every clause contributes to identifying the tool and its main mode distinction.

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 primitive with no annotations and no output schema, the description is still too thin to let an agent choose the correct mode confidently. Mode semantics are the key missing context, and the schema does not provide them either.

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 repeats the mode enum values as 'component or full URI mode' but does not explain what each mode changes, and it adds no semantics for the input parameter beyond the obvious name.

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 gives a specific verb and resource: 'Percent-encode a string'. It also identifies two operational modes, component and full URI, making the tool clearly distinguishable from url_decode and other text transformation 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 mentions two modes parenthetically but gives no guidance on when to choose component versus full URI mode. There is no when-to-use context beyond the implied encoding operation, and no alternative or 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 full burden, and it does add one genuine behavioral fact: the output is RFC 4122 version 4 (random, non-sequential). It does not disclose the return shape when count > 1 (array vs. single string) or that this is purely local/non-deterministic.

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 tight sentence with the key qualifier (RFC 4122 v4) front-loaded and zero filler. It is efficient, though borderline terse given the undisclosed count parameter.

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 low-complexity, zero-required-parameter tool this is nearly sufficient, but with no output schema the agent gets no clue about the return format when count > 1 or the shape of the generated value. One more clause on the count parameter would close the 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% and the description never mentions the count parameter, so the batching capability is undocumented in prose. The schema itself does encode min 1, max 64 and default 1, but the description adds nothing beyond it.

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 (Generate) and resource (RFC 4122 v4 UUIDs), pinning the exact variant/version so the agent knows this is a random v4 generator rather than a v1/v7 or hash-based ID tool. It is unambiguous against all siblings, none of which produce UUIDs.

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 purpose is self-evident for a simple generator, so usage is implied rather than stated. There is no explicit when-to-use or when-not-to-use guidance, and no routing to adjacent tools such as slug_generate or hash_text for 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 full behavioral burden, and it discloses almost nothing. It does not say whether input is a raw YAML string, what happens on malformed YAML, whether the operation is idempotent, or what the return value looks like. The low risk profile of a pure formatter keeps this from a 1, but the disclosure is minimal.

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 front-loaded sentence with zero waste, which is structurally fine. But it is under-specified rather than genuinely concise for a tool with four formatting options, so it cannot score higher.

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 4-parameter formatter with no annotations, no output schema, and 0% schema description coverage, the description omits the formatting controls, error behavior, and output shape entirely. An agent can call it with just the required field, but cannot use it well.

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% across 4 parameters, yet the description mentions none of them. The agent gets no help understanding indent (2 vs 4), sortKeys, or lineWidth behavior; only the parameter names, defaults, and the enum hint at their meaning.

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 and resource: "Pretty-print YAML." An agent immediately knows this is a formatting/normalization operation, not a conversion. It does not, however, distinguish itself from siblings like yaml_to_json or json_format, so it lands at 4 rather than 5.

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 when-to-use guidance, no exclusions, no mention of the adjacent tools (yaml_to_json, json_format, json_minify). The agent is left to infer from the name alone when this 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.

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

With no annotations, the description carries the full behavioral burden. It discloses the output format ('pretty-printed JSON'), which is useful, but omits error handling, input validation requirements, and the fact that the operation is a pure, side-effect-free transformation.

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, front-loaded sentence with zero waste. Every word earns its place and the core action is immediately clear.

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 conversion tool with no output schema, the description states both the input type (YAML) and output format (pretty-printed JSON), which is nearly sufficient. It could mention error behavior, but core functionality is covered.

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% and the single input parameter has no schema description. The description implies the input is YAML text, adding essential meaning, but does not name the parameter or specify format details, leaving it minimally compensated.

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 ('Convert') and resource ('YAML to pretty-printed JSON'), making the transformation direction unambiguous. An agent can instantly distinguish this from sibling tools like json_to_yaml and yaml_format.

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 no guidance on when to use this tool versus alternatives such as json_to_yaml or yaml_format. No prerequisites, exclusions, or context for selection are provided.

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