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

With no annotations, the description carries the full burden of behavioral disclosure, but it only mentions that the tool runs SPSS and requires installation. It does not explain what the tool returns, whether it modifies any files, how errors are surfaced, or how missing/invalid data is handled, leaving significant behavioral uncertainty.

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 very short and front-loaded with the main action. Every sentence provides useful information: what the tool runs, the optional post-hoc enhancement, and a required environmental prerequisite.

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?

Although an output schema exists, the overall definition is incomplete for an agent trying to call the tool correctly: required parameter meanings are absent, usage guidance versus sibling tools is missing, and post-hoc value formats are underspecified. The description covers only a small fraction of what an agent would need.

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, but it only adds examples for the post_hoc parameter (TUKEY, BONFERRONI, LSD). It does not define file_path, dependent, or factor, leaving critical required parameters undocumented beyond their names.

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 statistical procedure ('one-way ANOVA (ONEWAY)') and clearly distinguishes it from the many sibling analysis tools, including repeated measures, MANOVA, and t-test. It also states the optional post-hoc capability, making the purpose unambiguous.

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

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

The description implies when one-way ANOVA is appropriate by naming the procedure, but it does not explicitly contrast it with alternatives like t-test, repeated measures ANOVA, or regression, nor does it state conditions such as group count or assumptions. The only clear prerequisite is that IBM SPSS Statistics must be installed.

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 responsibility for behavioral disclosure. It mentions the implementation (GGRAPH + OMS) but does not reveal side effects such as file overwriting, output management changes, or error handling. No details on missing data or validation behavior are provided.

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, well-structured sentence that is front-loaded with the action ('Export') and includes all essential information without fluff. Every word contributes value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 10 parameters and many sibling chart types, the description provides a minimal viable definition but lacks guidance on file output, data_file usage, or interaction with other settings. The existence of an output schema covers return values, but the description remains somewhat thin for the complexity involved.

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 clarifies x and y ('y against a time/ordinal x variable'), leaving dpi, title, width, height, data_file, and other parameters unexplained. The mention of formats partially covers image_format but not the rest.

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

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

The description clearly states the tool exports a publication-ready area chart, specifies output formats (PNG/TIFF/EMF), and defines the relationship between y and a time/ordinal x variable. This distinguishes it from sibling chart tools like line or scatter plots.

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 provides clear usage context by specifying that the chart plots y against a time/ordinal x variable, implying when an area chart is appropriate. However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of a 5.

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 fully. It mentions output formats (PNG/TIFF/EMF), publication-ready, and the underlying method (GGRAPH + OMS IMAGE), but doesn't disclose side effects, file saving behavior, or requirements. This adds some behavioral context but not comprehensive.

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?

Single sentence that front-loads the tool's purpose and key options. No redundant or unnecessary phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 11 parameters and no annotations, the description only covers a subset of parameters and lacks usage caveats. The output schema covers return values, but the description's thinness forces the agent to infer several parameter meanings (e.g., width_px, height_px, data_file) and chart behavior (e.g., how missing values are handled). This is a moderate gap for an agent to select and invoke the tool accurately.

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 explains category, value, and stat via the main purpose sentence, and image_format explicitly mentions PNG/TIFF/EMF. However, other 8 params (dpi, title, x_label, y_label, width_px, height_px, data_file) are left to inference from their names, providing only partial compensation.

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 uses a specific verb 'Export' and clearly identifies the resource: a publication-ready bar chart of a categorical variable against the mean/sum of a continuous variable. This clearly differentiates it from sibling chart tools like spss_chart_histogram or spss_chart_line by specifying the bar chart type and aggregation.

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?

Description provides clear context about the tool's purpose (bar chart for categorical vs continuous) but does not explicitly state when to use it over alternatives like spss_chart_bar_error or spss_chart_histogram. No exclusions or alternative tool mentions are included.

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 must carry behavioral transparency, but it only reveals that it exports via GGRAPH + OMS. It does not disclose side effects like file creation or overwriting, prerequisites, required data context, or what happens on failure. This is a significant gap for an export tool.

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

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

The description is a single, front-loaded sentence that immediately states the key action, resource, and output formats. No redundant filler or excessive detail; it earns its place efficiently.

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?

Despite the tool's moderate complexity (11 parameters, 2 required), the description gives no guidance on data source, parameter semantics, or output handling. The presence of an output schema is not leveraged in the description, leaving the agent with insufficient information for reliable invocation.

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

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

The description hints that 'category' and 'value' are used for means and that image_format may be one of the listed formats, but it does not explain critical parameters like ci, dpi, width_px, height_px, labels, title, or data_file. With 0% schema description coverage, the description fails to compensate for the missing parameter context across 11 parameters.

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

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

The description uses specific verb 'Export' and clearly identifies the resource: a bar chart of category means with confidence-interval error bars. This distinguishes it from sibling tools like spss_chart_bar and spss_chart_errorbar, and the format list (PNG/TIFF/EMF) adds further specificity.

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

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

The phrase 'category means with confidence-interval error bars' conveys a clear use case that differs from simpler bar charts or standalone error bar charts. However, it does not explicitly mention when to choose this over alternatives or provide any exclusions, so it falls short of full 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 provided, the description must carry the burden of behavioral disclosure. It notes that the tool exports a file (implying non-mutating behavior) and mentions GGRAPH + OMS as the underlying mechanism, but it does not disclose potential side effects like file overwriting, output location, error conditions, or limitations. Key behavioral aspects remain unclear.

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 extremely concise, consisting of two short sentences that immediately convey the core purpose and required inputs. There is no fluff or redundant detail—every word earns its place. This is a model of efficient writing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 10 parameters and no annotations, the description is far from complete. It does not explain critical parameters like data_file, dpi, width/height, or image_format, and does not differentiate usage from sibling chart tools sufficiently. Although an output schema exists, the description still leaves too much unspecified for an agent to invoke the tool correctly without further schema inspection.

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 references a 'continuous variable' and 'categorical grouping variable,' which loosely map to the 'variable' and 'category' parameters, but it provides no guidance on the other eight parameters such as dpi, dimensions, image_format, or data_file. The description adds only minimal meaning beyond the raw schema.

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

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

The description clearly states the tool's function: exporting a publication-ready box-and-whisker plot in PNG/TIFF/EMF formats. It specifically identifies the chart type, distinguishing it from sibling chart tools like scatter, bar, and line. The verb 'Export' plus the specific resource makes the purpose unmistakable.

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

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

The description implies usage for creating boxplots by specifying required input (continuous variable and optional categorical group), but it does not explicitly state when to use this tool versus alternatives. No exclusions or comparisons to other chart tools are mentioned, leaving the usage context implicit rather than explicit.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the implementation via GGRAPH + OMS and the supported output formats (PNG/TIFF/EMF), but does not explain side effects, file handling (e.g., overwriting), prerequisites (like active dataset or required variables), or how the output is returned. This is a significant gap for a tool with 11 parameters.

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, well-structured sentence that front-loads the essential purpose (export chart) and includes key specifics (formats, statistical content, implementation). There is no wasted wording, and it is appropriately sized for the tool's core function.

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?

Despite having an output schema, the description lacks crucial context for an 11-parameter tool: it does not explain the role of data_file, dimensions, format details, or how the chart is generated and returned. The absence of parameter descriptions and behavior notes makes it incomplete for reliable invocation, particularly in edge cases or customizations.

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 hints at a few parameters: 'mean' relates to value, 'category' relates to category, and 'confidence-interval' relates to ci. It does not explain the meaning of dpi, image_format, width_px, height_px, title, labels, or data_file. Thus, the description fails to compensate for the lack of schema descriptions, leaving many parameters opaque to the agent.

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

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

The description clearly states the tool's function: exporting a publication-ready error bar chart with mean and confidence-interval whiskers per category. The verb 'Export' is specific, the resource is well-defined, and the scope (mean with CI whiskers) distinguishes it from general bar charts or histograms. Though it doesn't explicitly name sibling alternatives, the unique chart type and formats make the purpose unambiguous.

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

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

The description implies usage when an error bar chart is needed, but it does not explicitly state when to use this tool versus alternatives like spss_chart_bar_error or spss_chart_bar. There are no exclusion criteria or mentions of other chart tools, so the guidance is merely implied rather than clearly articulated.

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 bears the full burden of behavioral disclosure. It states that the tool exports a file and returns the path, which is essential. However, it does not mention prerequisites such as an active dataset or the data_file parameter's role, nor any resource implications, though the core export behavior is disclosed.

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 very concise (two sentences) and front-loaded with the main action. Each clause adds value, and although the technical detail 'GGRAPH + OMS IMAGE' is somewhat niche, it is not filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 9 parameters and no annotations, the description is too sparse. It does not explain the meaning of optional parameters or how data_file relates to the active dataset. The output schema covers return value structure, but the agent still lacks sufficient context to correctly use all parameters or choose this over the density variant.

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 explains the continuous variable requirement and the image format enum (PNG/TIFF/EMF), but the remaining 7 parameters (dpi, title, labels, dimensions, data_file) are left to an underscriptive schema. This partial coverage is insufficient for fully understanding how to invoke the tool.

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

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

The description clearly states that the tool exports a publication-ready histogram and returns a file path, which is a specific verb-resource pairing. However, it does not explicitly differentiate from the sibling spss_chart_histogram_density tool, so it is clear but lacks explicit 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 description provides a basic usage requirement ('Provide the continuous variable') but gives no guidance on when to use this tool versus alternatives. No exclusions or alternative tool mentions are provided, leaving the agent without enough context to choose among many chart siblings.

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 transparency burden. It discloses the technical mechanism ('via GGRAPH + OMS') and the output nature ('Export'), but it omits important behavior such as what OMS does (e.g., suppressing table output), file handling details (naming, overwriting), or behavior when the variable is not continuous. The description is not misleading but is under-specified for a tool with no annotation safety net.

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

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

The description is a single, front-loaded sentence that states the action, output, and key input requirement. Every word adds value, and there is no irrelevant filler. It is appropriately concise for a focused chart-export 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?

With 9 parameters, 0% schema coverage, and no annotations, the description must carry a heavy load but only covers the core purpose and one input. It does not explain the data_file parameter, default behaviors, or how the output is returned (although an output schema exists, that doesn't cover input semantics). The description is too sparse to enable correct use in varied contexts.

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 adds meaning for the variable parameter ('Provide a continuous variable'). Parameters like data_file, dpi, width_px, height_px, image_format, and labels are left entirely to their names, which may be self-explanatory for some but not all (e.g., data_file). The description fails to compensate for the lack of schema descriptions.

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

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

The description clearly states a specific action ('Export a publication-ready histogram overlaid with a normal density curve') and distinct output formats (PNG/TIFF/EMF), which differentiates it from sibling tools like spss_chart_histogram that likely produce a simpler histogram without the density overlay. The verb 'Export' and resource specification are precise.

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 instruction 'Provide a continuous variable' gives a clear prerequisite for use. The description implies when to use it (when a histogram with normal density is needed) but does not explicitly name alternatives or exclusions, such as 'use spss_chart_histogram if you don't need the density curve.' Overall, the context is clear but lacks explicit when-not-to-use 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool runs the KM procedure and exports specified image formats, but it does not mention side effects, prerequisites like an active dataset, or output behavior such as file paths or return values. This is a significant gap for a tool with no annotation coverage.

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

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

The description is a single concise sentence that immediately states the tool's purpose and the essential inputs. There is no redundant or extraneous text, and the structure front-loads the primary action.

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 10 parameters, 0% schema coverage, and no annotations, the description is too sparse to be fully actionable. It covers only three parameters and does not address formatting dimensions, event coding semantics, or data_file defaults, even though the output schema exists.

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 explains time, status, and group, leaving seven other parameters (dpi, event, title, width_px, data_file, height_px, image_format) without added meaning. This fails to provide sufficient guidance for an agent invoking the tool.

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 uses a specific verb ('Export') and clearly identifies the resource as a 'publication-ready Kaplan-Meier survival curve' with output formats (PNG/TIFF/EMF). This distinguishes it from sibling chart tools and from spss_kaplan_meier, which likely performs the analysis rather than the chart export.

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 provides clear context that time and status variables are required and group is optional, giving preconditions for use. However, it does not explicitly mention alternatives or when not to use this tool, such as using spss_kaplan_meier for statistical results or other chart types for different visualizations.

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 must disclose behavior. It mentions the export mechanism (GGRAPH + OMS IMAGE) and that a file path is returned, but does not clarify side effects like file overwriting, required active SPSS dataset, or variable type constraints. This is moderate transparency but leaves significant gaps.

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

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

Two succinct sentences with no filler. The core purpose, required inputs, and return value are front-loaded. Perfectly concise for the information provided.

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?

Provided complexity is moderate (10 params, no annotations, output schema exists). The description covers the main action and return value, but omits behaviors like file saving details, prerequisites, and parameter formats. It is minimally viable but not fully complete for a charting tool with many options.

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%. The description adds meaning for x and y (time/ordinal and the variable), but the other 8 parameters (dpi, title, labels, width, height, data_file, image_format) are not explained. Given 10 parameters, the description compensates poorly for the schema's lack of descriptions.

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

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

The description clearly states the tool exports a publication-ready line chart, names the specific technology (GGRAPH + OMS IMAGE), and lists output formats (PNG/TIFF/EMF). This distinguishes it from sibling chart tools (e.g., spss_chart_bar, spss_chart_scatter) and the verb 'Export' is specific.

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

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

The description implies usage by specifying to provide x (time/ordinal) and y variables, and the tool name makes the line chart context obvious. However, it does not explicitly exclude other chart types or reference sibling tools as alternatives, so it falls short of full alternative 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the PPLOT and OMS pipeline, but it does not clarify whether the chart is returned directly, saved to a file, or how the data_file parameter affects execution. It also lacks any mention of side effects, prerequisites, or output format details beyond the image types.

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

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

The description is a single concise sentence that front-loads the action and chart type. It avoids unnecessary words, though the acronyms PPLOT and OMS could be slightly more transparent to a general agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The core purpose is well-covered, but the description lacks guidance on when to use the tool, parameter semantics for several fields, and behavioral details like return values or file handling. Given the 7-parameter schema and output schema existence, more contextual completeness would be expected.

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

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

With 0% schema coverage, the description must compensate for parameter documentation. It does clarify that 'variable' is a continuous variable and lists image formats (matching image_format), but it leaves dpi, title, dimensions, and data_file unexplained. This is only partial compensation for the missing schema descriptions.

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

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

The description clearly states the tool exports a specific chart type (normal Q-Q plot) with supported formats (PNG/TIFF/EMF) for a continuous variable, using a specified method (PPLOT via OMS). This distinguishes it from sibling chart tools and statistical procedures.

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

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

The description implies usage for continuous variables to create normal Q-Q plots, but it does not explicitly state when to use this tool versus alternatives such as spss_normality_outliers or other chart types. No exclusions or alternative recommendations are provided.

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 of behavioral disclosure. It reveals the export mechanism (GGRAPH + OMS IMAGE), the output formats (PNG/TIFF/EMF), and the return of a file path—useful behavioral details beyond the schema. It doesn't mention whether the tool overwrites files or how data is sourced (e.g., active dataset vs. data_file parameter), which is a slight gap, but the core outcome is well disclosed.

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, well-structured sentence that front-loads the purpose and then gives a quick usage note. It avoids fluff and every clause earns its place. However, it is almost too terse for a tool with 10 parameters, but for conciseness alone it's efficient and 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?

The tool has 10 parameters, no annotations, and schema descriptions cover none of the parameters. The one-sentence description leaves critical gaps, particularly the data_file parameter (how to supply data if no active dataset exists) and the meaning of visualization options like dpi, labels, and dimensions. Even though an output schema exists, the lack of guidance on data source and optional formatting makes the description incomplete for effective use.

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 for parameter meanings. It only clarifies x and y variables and optionally implies the image format list (PNG/TIFF/EMF). Many parameters remain unexplained: dpi, title, x_label, y_label, width_px, height_px, data_file, and image_format defaults. The description provides minimal semantic value beyond the schema's type/default information, leaving users guessing about data_file and formatting options.

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

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

The description clearly states the tool exports a scatter plot in PNG/TIFF/EMF formats using GGRAPH + OMS IMAGE. The verb 'Export' and specific resource 'scatter plot' distinguish it from sibling chart tools like histogram or bar. It also mentions the key inputs (x and y variables), making the purpose unambiguous.

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

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

The description gives direct instruction to provide x and y variables, which is the core usage. It also notes that the chart file path is returned for direct submission, providing context for when this tool is appropriate (e.g., preparing publication figures). However, it doesn't explicitly contrast with alternative chart tools or state when not to use it, though the name and sibling list make this somewhat implicit.

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 transparently conveys a read-only introspection behavior by stating it checks status and reports capabilities, path, versions, and configuration. It does not detail error behavior or connection prerequisites, but the non-mutating nature and informational return are clear.

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

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

Two sentences with no filler: the first states what is checked and what is reported, the second gives the recommended invocation timing. It is front-loaded with the primary action and all details are relevant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter status tool with an output schema present, the description is complete: it names the exact information returned and when to call it. Return-value details are already covered by the output schema, so no additional explanation is needed.

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

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

The tool has zero parameters, so per the baseline rule this scores 4. The description does not need to add parameter meaning since there is nothing to configure, and the input schema is vacuously fully covered.

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 uses a specific verb and resource: 'Check the SPSS MCP server status' and enumerates exactly what is reported (capabilities, SPSS vs file-only mode, SPSS path, library versions, configuration). This clearly distinguishes it from sibling tools like spss_list_supported_methods or spss_get_method_support, which inspect methods rather than overall server status.

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

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

The description gives explicit when-to-use guidance: 'Call this first to understand what tools are available.' It does not mention explicit exclusions or alternatives, but for a status-check tool the sequencing guidance is clear and sufficient context.

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

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

There are no annotations, so the description carries the full burden. It discloses only that SPSS must be installed and that multiple linkage/distance options are supported; it does not say whether the operation reads the input file only, creates output artifacts, or has side effects on the file.

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?

Three short sentences with the main action first and no filler. The SPSS prerequisite is useful, though 'with dendrogram' partly duplicates the dendrogram parameter's default behavior.

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 statistical tool with no schema descriptions and no annotations, this is too thin. The output schema covers returns, but missing parameter semantics, selection guidance, and behavior mean an agent cannot confidently invoke or configure it beyond guessing from property names.

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 adds a general hint that method relates to linkage and measure relates to distance, but it never explains file_path, variables, id_variable, or dendrogram semantics, and the enum values are left entirely to their names.

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 an exact statistical procedure ('hierarchical cluster analysis') and mentions the dendrogram output, so an agent can identify what the tool does. It does not explicitly contrast with the sibling clustering tools spss_twostep_cluster and spss_quick_cluster, but the method name itself is specific enough to be 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?

No guidance about when to choose hierarchical clustering over the sibling twostep_cluster or quick_cluster tools, and no exclusions or preconditions beyond the SPSS installation note. The intended use is only implied by the tool's name and the verb 'Run'.

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 of behavioral disclosure. It discloses the computation method (SUM/MEAN), optional reverse coding, and minimum valid item count, which are useful behavioral details. However, it does not disclose side effects such as whether the file is modified in place or whether a new variable is added, nor does it mention prerequisites about variable existence.

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

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

Two sentences with no wasted words. The core function and key options are front-loaded, followed by the environmental requirement. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters and no annotations, the description gives a solid high-level overview but omits operational details like how reverse coding determines values, how min_valid interacts with missing data, and whether the result is written to the file or returned. The presence of an output schema mitigates return-value ambiguity, but the reverse coding and file-mutation behavior remain underspecified.

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 meaningfully maps to parameters: 'SUM or MEAN' covers the method enum, 'multiple item variables' covers items, 'optional reverse coding' covers reverse_items/reverse_min/reverse_max, and 'minimum valid item count' covers min_valid. It leaves file_path and new_variable implicit, but these are self-evident from their names.

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 ('Compute') and resource ('scale score') with the SUM/MEAN methods and optional reverse coding and minimum valid item count. It clearly distinguishes this tool from generic spss_compute and analysis tools like spss_reliability_alpha by focusing on scale scoring from item variables.

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

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

The description implies the use case (computing scale scores from multiple item variables) but does not explicitly state when to prefer this over siblings such as spss_compute or spss_recode, nor does it mention exclusions or alternatives. The only contextual guidance is the IBM SPSS Statistics requirement, which is a prerequisite rather than a usage comparison.

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, but it only reports that SPSS must be installed. It does not disclose whether the operation is read-only, whether it writes outputs or modifies the dataset, or what conditions cause it to fail. 'Compute' weakly implies analysis, but side effects and limitations are left unspecified.

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 pack the core purpose and a key prerequisite. The key action is front-loaded, and there is no filler or repetition of schema fields.

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?

Although an output schema exists and reduces the need to explain return values, the tool still lacks usage context and parameter semantics. For a statistical analysis tool with many siblings and four parameters, this sparse description does not give the agent enough to invoke it correctly without additional probing.

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 adds meaning to the method parameter by naming Pearson and Spearman. The required file_path and variables, plus two_tailed, are not explained; the agent must infer their semantics from parameter names alone. The description does not compensate for the schema gap.

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

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

The description states a specific command ('Run SPSS CORRELATIONS') and a concrete outcome ('compute Pearson or Spearman correlation matrix'), which precisely identifies the tool's role among the many SPSS siblings. The method choices reinforce the scope without ambiguity.

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/when-not guidance or alternatives are given. The only contextual note is the installation prerequisite, which is an environment constraint rather than guidance for choosing this over, say, spss_regression or spss_crosstabs. The intended use must be inferred from the tool's name and 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?

With no annotations available, the description carries the burden of behavioral disclosure. It provides useful context by naming the external installation requirement and describing supported analyses. It does not disclose side effects, failure behavior, or whether the operation writes files or modifies the input dataset, which leaves some behavioral ambiguity.

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

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

The description is two sentences with no filler. The action and domain are front-loaded, followed by capability and prerequisite details that are relevant for 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?

The tool has 9 parameters, 5 required, with zero schema descriptions, and the description does not explain how to populate core fields beyond the procedure name. An agent cannot confidently determine status_event_value semantics, categorical variable treatment, or method choice from this definition. The presence of an output schema reduces return-value ambiguity but does not fill the invocation gaps.

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

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

Schema description coverage is 0%, so the description must compensate, but it only alludes to stratification and time-dependent covariates. It does not clarify required parameters such as status_event_value, the method enum values (ENTER/FSTEP/BSTEP), categorical handling, or save_survival. The parameter names are somewhat self-explanatory, which prevents a score of 1.

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

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

The description identifies a specific procedure ('Cox proportional hazards regression') with a clear verb ('Run') and domain ('survival analysis'). It advertises distinctive capabilities like time-dependent covariates, stratification, and diagnostics, which separates it from most siblings by analytic method. It does not explicitly contrast with spss_kaplan_meier, but the named technique is unambiguous.

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

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

The phrase 'for survival analysis' establishes the appropriate data context, and the IBM SPSS prerequisite tells the agent when the tool cannot be used. However, it does not explicitly state when to prefer this tool over survival-adjacent siblings like spss_kaplan_meier or over regression alternatives such as spss_logistic_regression. The usage guidance is implied rather than explicit.

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

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

With no annotations provided, the description carries the burden of explaining behavior. It discloses the operation, optional statistics, and the software installation requirement. It does not state whether the command modifies the data file, writes any files, or requires a running SPSS instance, so some side-effect/execution behavior remains unspecified.

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 compact sentences front-load the core purpose and then list optional features and a prerequisite. Every sentence 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?

The description is sufficient for selecting the tool, and the presence of an output schema means return values do not need detailing. However, for a 6-parameter tool with no annotations and 0% schema coverage, important execution details are missing (expected file format, defaults, whether data is modified). It is minimally adequate but 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?

The schema has 0% description coverage, so the description must compensate. It clarifies that row_variable and column_variable are categorical and that the boolean flags correspond to chi-square and row/column percentages. It does not explain file_path (e.g., expected .sav format) or confirm the default-true behavior, leaving only partial compensation for the missing schema descriptions.

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 action ('Run SPSS CROSSTABS') and object ('contingency table between two categorical variables'), which separates it from univariate procedures like spss_frequencies. It also mentions optional chi-square and percentages, making the tool's scope easy to grasp. However, it never references a sibling tool by name, so differentiation relies on inference rather than explicit 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?

The description provides a clear condition for use—when a contingency table between two categorical variables is needed—and adds a prerequisite (IBM SPSS Statistics installed). It does not explicitly state when to prefer an alternative tool such as spss_frequencies or spss_custom_tables, nor does it give when-not-to-use guidance. This is enough for common cases but leaves the 'vs alternatives' question unanswered.

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 disclosure burden. It does reveal the returned statistics (N, mean, std deviation, min, max) and the system requirement, which is useful. But it does not disclose whether the operation modifies files, how it handles invalid variables, or any error behavior, leaving gaps for a read-only-looking analysis command.

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 three concise sentences with no filler. It front-loads the action and scope, then gives output and environment context. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The presence of an output schema helps explain return structure, and the description lists key statistics, but the bare input schema and lack of parameter guidance make the definition incomplete. Key details about file_path and statistics options are missing, and no differentiation from siblings is offered.

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

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

Schema coverage is 0%, so the description must compensate. It adds some meaning by specifying that variables are numeric and that statistics are optional, but it does not explain file_path at all, nor does it provide any hint about what values the statistics parameter accepts. An agent would likely struggle to correctly construct the parameters.

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

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

The description clearly states the operation: 'Run SPSS DESCRIPTIVES for numeric variables' and lists the statistics produced. It is specific about the procedure and scope, but does not distinguish it from similar siblings like spss_frequencies or spss_crosstabs, so it loses a point.

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 a scope condition ('for numeric variables') and a prerequisite ('Requires IBM SPSS Statistics to be installed'), which implies appropriate usage. However, it does not explicitly say when to choose this tool over alternatives such as spss_frequencies, nor does it state any exclusions.

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

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

With no annotations provided, the description adds some behavioral context by noting the IBM SPSS requirement and supported options (stepwise selection, cross-validation). However, it does not disclose potential side effects such as creating new variables or modifying the input file, or any permissions or error behaviors.

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

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

Two sentences that are lean and front-loaded: the first states the core purpose, the second adds key capabilities and a prerequisite. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex statistical tool with 8 parameters and an external dependency, the description covers the essential identity and requirement but omits details like data format expectations, grouping variable constraints, or output behavior. The presence of an output schema lessens the need to describe return values, but assumptions and preconditions are 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%, so the description must compensate by explaining parameter semantics, but it does not. It only hints at stepwise selection, which maps vaguely to the method enum, but leaves parameters like priors, group_values, and save_class unexplained. The description adds minimal value over the raw schema.

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

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

The description clearly specifies the tool's function: 'Run discriminant analysis to classify cases into groups.' This is a specific verb+resource statement that distinguishes it from sibling tools like regression or factor analysis. Mentioning stepwise selection and cross-validation further clarifies scope.

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

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

The description implies use when discriminant analysis is needed for classification, but it does not explicitly contrast with alternatives like logistic regression or MANOVA, nor does it provide when-not-to-use guidance. The context is clear enough for a basic selection but lacks explicit exclusions.

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

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

With no annotations provided, the description carries the burden of disclosing behavioral traits. It mentions a key prerequisite (IBM SPSS Statistics installed) and the core outputs (eigenvalues, variance explained, rotated factor matrix). However, it does not disclose side effects, read-only behavior, error handling, or output format details, leaving significant gaps.

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

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

The description is three sentences, front-loaded with the core action, and each sentence adds distinct information: what it runs, what it produces, and a critical prerequisite. No unnecessary words or repetition.

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 complex statistical tool with five parameters and an output schema, the description is quite sparse. It fails to explain parameter meanings, extraction methods, rotation choices, or how to interpret results. While the output schema may cover return structure, the description alone is insufficient for an agent to use this tool 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%, so the description must compensate. It implicitly references the method parameter by naming PC and PA, and rotation via 'rotated factor matrix,' but it does not explain the required parameters file_path and variables, nor n_factors. This leaves most parameters under-specified, providing only minimal semantic 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 clearly states the tool runs SPSS FACTOR analysis, specifically naming principal components and principal axis factoring, and lists key outputs. This unambiguously identifies the tool's function and distinguishes it from siblings like regression or cluster analysis.

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

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

The description implies usage for factor analysis but provides no explicit guidance on when to choose this tool over alternatives such as PCA or other dimension reduction methods. It neither mentions exclusions nor suggests alternative tools, so usage context is only implicit.

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 to indicate side effects or safety, the description carries the behavioral disclosure burden. It clearly states that computation happens locally and does not require SPSS, which is meaningful behavior beyond the schema. It does not explicitly state that the file is read-only, but 'summary' plus local computation strongly implies no mutation.

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

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

The description is compact and front-loaded with the core purpose and output contents. It loses a point because 'Does not require SPSS to be installed' repeats the parenthetical 'no SPSS needed,' adding minor redundancy rather than 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 the output schema covers return structure, the description adequately covers the core purpose, input type, and the key behavioral differentiator (no SPSS required). It doesn't discuss file access requirements or edge cases, but for a summary tool with one parameter this is a workable completeness level.

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

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

Schema coverage is 0%, so the description must compensate for the bare string parameter. It adds meaning by indicating that file_path should reference an SPSS .sav file and that processing is local. It doesn't specify path formatting or file-size caveats, but for a single obvious parameter this is reasonably sufficient.

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 opens with a specific verb and resource ('Get a summary of an SPSS .sav file') and enumerates the exact contents: case count, variable count, variable list, and basic descriptive statistics. It distinguishes itself from SPSS-dependent siblings by emphasizing that the summary is computed locally without SPSS.

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 repeated 'no SPSS needed' note gives clear context for when this tool is appropriate: when a quick local summary is desired without requiring an SPSS installation. It does not explicitly name alternatives or state when NOT to use it, but the local-computation framing provides usable selection guidance among the many SPSS tools.

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

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

No annotations are provided, so the description carries the behavioral disclosure burden. It communicates the return value and the IBM SPSS installation requirement, but it does not explicitly state whether the operation is read-only or whether it can modify the dataset.

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

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

The description is two focused sentences with no filler. It front-loads the command and outcome, then adds a necessary prerequisite. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema covers return-value structure, but with no annotations and sparse parameter descriptions, the description leaves gaps around accepted statistics values, file_path semantics, and variable requirements. It is adequate for a simple frequency call but incomplete for nuanced usage.

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

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

The schema contains no descriptions for any parameter, so the description must compensate. It clarifies that variables are one or more and that statistics are optional, but it does not define which statistic values are accepted, the file_path format, or variable type constraints.

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

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

The description names the exact SPSS command ('FREQUENCIES') and the resource ('one or more variables'), and clearly states the output as frequency tables with counts, percentages, and optional statistics. This distinguishes it from siblings like descriptives, crosstabs, and regression.

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

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

The description implies the tool is for generating frequency distributions, which is a clear context. However, it does not explicitly state when to prefer this over descriptives or crosstabs, nor does it provide exclusions such as continuous variables or missing data handling.

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 must carry the burden of explaining behavior, but it only states that IBM SPSS Statistics must be installed and lists supported distributions. It does not disclose execution side effects, failure behavior, or what happens to the output/data.

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

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

The description is two short sentences with the primary action front-loaded and no filler. Every sentence adds relevant information about the model family or runtime prerequisite.

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 complex model-fitting tool with 8 parameters, no annotations, and no parameter descriptions, this is incomplete. The output schema covers return values, but the description still omits essential call context such as variable role requirements and categorical handling.

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, but it only hints at distribution and link-function concepts. Core parameters like file_path, dependent, predictors, scale, and save_predicted remain 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 uses a specific verb and resource: 'Run generalized linear model (GENLIN)' and clarifies flexible distribution/link support. It is clear, but it doesn't explicitly differentiate this from siblings like spss_logistic_regression or spss_genlinmixed.

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 choose GENLIN over alternatives such as spss_regression or spss_logistic_regression, and no exclusion criteria are given. The only contextual note is the SPSS installation requirement, which is a prerequisite, not a usage rule.

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 only states that the tool 'runs' a model and requires SPSS to be installed; it does not disclose whether the operation modifies data, writes files, or produces specific outputs. The behavioral traits of execution are left largely unspecified.

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 short and front-loaded, with the model type in the first sentence and the key prerequisite in the final clause. It is concise and well-ordered, though it uses its brevity to omit substantive guidance.

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 complex modeling tool with 7 parameters and multiple sibling model types, the description is materially incomplete. It does not clarify parameter roles, when to select this over spss_genlin or spss_mixed, or what running the model implies operationally. Although an output schema exists, the missing parameter and usage context makes the definition insufficient for correct invocation.

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

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

Schema description coverage is 0%, and the description mentions no parameters by name or semantic role. It never explains file_path, dependent, fixed_effects, random_effects, link, subject, or distribution, so it does not compensate for the lack of schema-level documentation at all.

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 uses the specific verb 'Run' with the resource 'generalized linear mixed model combining GLM with random effects.' It explicitly identifies the model type and distinguishes it from sibling tools like spss_genlin and spss_mixed by noting it combines GLM with random effects, making the purpose clear and 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?

The phrase 'Supports non-normal outcomes with hierarchical structure' implies when the tool is appropriate, but it does not explicitly state when to use this tool over spss_genlin, spss_mixed, or other modeling siblings, nor does it mention any exclusions. Usage context is implied rather than directly guided.

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 of behavioral disclosure. It strongly implies a read-only introspection action ('Get the JSON schema', 'before execution'), but it does not explicitly state that the method is not executed, what happens for invalid tool names, or whether the method must already exist in the registry.

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

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

The description is two concise sentences with no redundancy. The action and resource are front-loaded, and the usage context follows directly. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter schema-retrieval tool with an output schema, the description covers the purpose and usage adequately. However, it omits parameter semantics and does not mention how to discover valid method names, leaving an obvious gap for an agent preparing to call 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%, and the description does not explicitly explain that tool_name is the SPSS method name or how valid values can be discovered. An agent can infer the meaning from context, but the description should have pointed to spss_list_supported_methods for valid tool names.

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 ('Get') and resource ('JSON schema for a registry-backed SPSS method'), making the tool's purpose clear. It is implicitly distinct from siblings like spss_get_method_support, but it does not explicitly differentiate itself by naming alternatives.

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

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

The second sentence provides explicit usage context: 'useful for structured orchestration and parameter inspection before execution.' This tells an agent when to use it, though it does not mention when not to use it or name alternative tools.

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

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

With no annotations, the description carries the transparency burden. It makes the read-only nature clear ('Get support metadata') and specifies what information is returned, so an agent can infer this is a non-mutating lookup. It does not cover error behavior or auth requirements, which is the only substantial 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?

The description is a single, front-loaded sentence with no filler. Every phrase earns its place by specifying the target and the returned metadata categories.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with an output schema, the description is nearly complete: it identifies the input's semantic domain and the shape of the returned metadata. It could be more explicit about how to discover supported method names and what happens for non-registry-backed methods, but neither is essential for basic invocation.

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

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

The only parameter, tool_name, has 0% schema description coverage. The description adds the meaning 'registry-backed SPSS method name,' which helps, but it does not tell the agent where to find valid values (e.g., from spss_list_supported_methods) or define the expected identifier format. It partially compensates for the bare schema.

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

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

The description states a specific action ('Get support metadata') on a specific resource ('a registry-backed SPSS method') and enumerates the payload categories (command family, support tier, coverage assertions, documentation tags). This clearly distinguishes it from siblings like spss_get_method_schema or spss_list_supported_methods.

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 only defines what the tool does; it gives no conditions for when to prefer it over alternatives such as spss_get_method_schema or spss_list_supported_methods, and no prerequisites like the need to obtain a valid method name from the registry. Usage context is left entirely to inference.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the installation prerequisite and supported analysis features, but does not disclose side effects such as whether 'save_predicted' modifies data files, whether SPSS is launched in batch mode, or what operational constraints apply.

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 three short, purposeful sentences: action first, capabilities second, environment prerequisite third. It is front-loaded, avoids redundancy with the tool name, and contains 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?

For an 8-parameter statistical tool with no annotations and no schema descriptions, the description is too sparse to enable confident invocation. It leaves unclear how to specify variables, what file formats are expected, which parameters are optional or mutually exclusive, and how this GLM tool relates to nearby siblings like spss_anova.

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 loosely hints at factors, emmeans, and posthoc parameters without explaining their formats or relationships. It adds no meaning for file_path, dependent, covariates, posthoc_method, or save_predicted, so parameter semantics remain largely underdocumented.

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 action ('Run univariate general linear model') and a clear scope ('factorial designs'), while mentioning distinctive capabilities like estimated marginal means, contrasts, and post-hoc tests. It does not explicitly differentiate from close siblings such as spss_anova or spss_manova, but the univariate/factorial framing provides adequate orientation.

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

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

The description implies when to use the tool: for univariate GLMs with factorial designs and optional post-hoc/emmeans analyses. However, it gives no explicit when-not-to-use guidance or alternatives among the many SPSS analysis siblings, leaving routing largely 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, the description carries the full burden of transparency. It discloses that the tool creates a .sav file, where it saves by default, and that it bypasses the SPSS engine. However, it omits important behavioral traits such as whether existing files are overwritten, data type handling, or encoding limitations. This is adequate but not deeply transparent.

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 three sentences, front-loaded with the core purpose, and every sentence adds value: what it does, why it's faster, and where output goes. No redundant or filler text. It is succinct and 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?

Given the tool's moderate complexity (5 params, no annotations), the description provides the essential context for the primary use case but leaves out guidance on several parameters (encoding, delimiter, column_labels) and potential edge cases like overwriting or unsupported features. It is sufficient for a straightforward conversion task but lacks depth for full autonomous use without schema 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%, so the description must compensate for parameter understanding. It only mentions 'output_path' as a custom destination; it does not explain 'encoding', 'delimiter', or 'column_labels', even though these are important for correctly importing a CSV. The parameter names are somewhat self-explanatory, but the description adds minimal value 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?

The description uses a specific verb and resource: 'Convert a CSV file to SPSS .sav format'. It clearly differentiates from sibling analysis tools by naming the conversion goal and the technical approach (pandas + pyreadstat), and contrasts with SPSS syntax execution via spss_run_syntax. This leaves no ambiguity about what the tool 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?

The description implies the primary use case: converting CSV to SPSS .sav without requiring SPSS installation. It explicitly compares to 'going through SPSS syntax' and notes it is 'much faster', providing an alternative. However, it does not state when not to use this tool or mention any prerequisites beyond the CSV file, so it is slightly short of a perfect score.

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 an important prerequisite (IBM SPSS Statistics must be installed) and describes the produced results. It does not discuss side effects or failure behavior, but for an analysis command the read-only nature is reasonably implied.

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

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

Three short sentences with no filler. The action and outputs are front-loaded, the prerequisite is placed at the end, and nothing repeats the schema's structural 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?

The output schema covers return values, but the description still leaves gaps around parameter semantics, especially status_event_value and optional strata/percentiles. For a well-known statistical method, the description is minimally adequate, though it could be more useful with explicit guidance on event/censoring coding and comparison methods.

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%, yet the description adds almost no parameter-level meaning. It does not explain status_event_value, strata, percentiles, or compare_method beyond what the names and enum already suggest, so it fails to compensate for the schema's lack of descriptions.

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

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

The description names the exact statistical procedure ('Kaplan-Meier survival analysis') with a clear verb ('Run') and states the main outputs: survival curves and group comparison. This distinguishes it from sibling tools like spss_cox_regression and other analysis procedures.

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

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

The description gives clear context by specifying survival analysis with a log-rank test and group comparison. It does not explicitly mention when to choose it over alternatives like cox_regression, but the statistical context is clear enough for an informed agent.

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

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

With no annotations, the description carries the burden of explaining behavior. It clearly says the tool lists .sav files in a directory and is for dataset discovery. However, it does not mention how the 'recursive' parameter affects behavior, what happens with invalid directories, or whether only .sav files are returned. Basic behavior is clear, but edge-case behavior is undeclared.

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

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

Two sentences, front-loaded with the core action and followed by a practical usage note. No filler or redundant restatement. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple and has an output schema, which reduces the burden on the description. Still, the description omits any guidance on the recursive parameter and does not clarify whether the tool filters strictly to .sav files. It is adequate for basic use but not fully complete for nuanced calls.

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 implies the 'directory' parameter through 'in a directory,' but does not explain what values are valid or how the 'recursive' parameter changes results. The description adds minimal semantic value beyond the schema's bare parameter names.

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: 'List SPSS .sav files in a directory.' The purpose is unambiguous, and the distinction from siblings like 'read_data' or 'file_summary' is reasonably clear because this tool is about discovering files, not reading or analyzing 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?

Provides explicit usage context: 'Useful for discovering available datasets when the user hasn't specified a file path.' This tells an agent when to reach for this tool. It does not explicitly name alternative tools or exclusion conditions, so it stops short of a 5.

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 does reveal that the operation lists registry-backed methods and that results have schemas, templates, and coverage assertions, but it never explicitly states that it is read-only or side-effect-free, nor does it describe the result format (though an output schema exists).

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

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

Two sentences with no filler: the first states the core function, the second gives directed usage. The most important information is front-loaded, and every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (no parameters) and has an output schema, so the description need not detail return values. It provides enough context to invoke correctly. The only slight gap is the undefined term 'cold methods', which could confuse an agent but does not block a correct call.

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

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

The tool has zero parameters, so the description need not add parameter-level semantics. The 100% schema coverage of an empty object confirms no parameters are expected, earning the baseline score of 4 for parameterless tools.

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 uses the specific verb 'List' with a clear resource: 'registry-backed SPSS methods available for structured execution.' It further distinguishes the tool from statistical/analysis siblings by stating it discovers methods that have schemas, templates, and coverage assertions.

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?

'Use this to discover cold methods that have schemas, templates, and coverage assertions' explicitly states when to use the tool. However, it does not mention when not to use it or point to any alternative, and 'cold methods' is unexplained jargon.

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 of behavioral disclosure. It clearly signals a read-oriented listing operation, describes the file source, the optional search filter, and the no-SPSS-installation requirement. It does not explicitly state that the file is never modified, but 'List' strongly implies read-only behavior.

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

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

Two sentences with no filler. The primary purpose is front-loaded, followed by the optional search behavior and the environment note. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and an output schema, the description covers the essential calling context: the file source, optional filter, and runtime dependency. It could be slightly more complete if it explicitly stated that the operation is read-only and clarified the search scope, but overall it is largely sufficient.

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

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

The schema has 0% property-description coverage, so the description must compensate. It adds meaning for file_path as an SPSS .sav file and for search as an optional filter term. However, it leaves ambiguity about whether search matches variable names, labels, or both, and does not elaborate on the required file_path beyond the schema structure.

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 uses a specific verb, 'List', names the exact resource ('variable names and their labels'), and explicitly mentions the source as an SPSS .sav file. It also distinguishes itself from sibling tools by clarifying it lists variables, not files or methods.

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

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

The description implies when to use it—when you need variable names/labels with optional search filtering—and adds a useful runtime note that SPSS need not be installed. However, it does not explicitly contrast this with sibling tools like spss_read_metadata or spss_file_summary, nor does it state exclusions or when-not-to-use conditions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully notes that IBM SPSS Statistics must be installed, but it does not state whether the tool writes or modifies files, whether it has side effects, what happens when SPSS is unavailable, or what errors or outputs to expect. This is a significant gap for an execution 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 concise and front-loaded, with each sentence serving a purpose: what it does, what it supports, and what it requires. It could include more parameter guidance without becoming bloated, but as written it is tight and free of filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given eight parameters, no annotations, and no schema-level descriptions, the description leaves key usage details unexplained. While an output schema exists, the tool still lacks guidance on parameter semantics, side effects, and relationship to sibling regression tools, making it incomplete for an agent to invoke 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%, so the description must compensate for parameter meaning. It hints at 'stepwise selection' and 'categorical predictors,' which map to method and categorical, but it provides no explanation of required params like file_path, dependent, and predictors, or optional params like contrast, print_options, and save_predicted. The compensation is insufficient.

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

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

The description clearly states the tool's purpose with a specific verb ('Run') and resource ('binary or multinomial logistic regression'). It also distinguishes the tool from siblings like spss_regression and spss_ordinal_regression by naming the exact model family and scope.

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

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

The description implies use for logistic regression tasks, which gives basic context. However, it does not explicitly state when to choose this tool over sibling alternatives, nor does it provide exclusions or conditions where another regression tool would be more appropriate.

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 discloses that the tool tests multivariate effects, provides univariate follow-ups, and requires IBM SPSS Statistics. However, it does not state whether it modifies data, how missing values are handled, or other behavioral constraints beyond the prerequisite.

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 three short sentences, front-loaded with the core action followed by output behavior and prerequisite. There is no fluff or redundancy; each sentence adds necessary context.

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 complex tool with 8 parameters and no annotations, the description is too sparse. While an output schema exists (covering return values), it omits usage guidance, parameter semantics, and assumptions (e.g., data requirements, missing data handling), leaving the agent under-informed for correct invocation.

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

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

The schema has 0% parameter description coverage, so the description must compensate. It only hints at the role of dependents ('multiple dependent variables') and indirectly factors, but does not explain method, factor_ranges, covariates, print_multivariate, print_univariate, or other parameters. This is insufficient for an 8-parameter tool.

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

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

The description clearly states the action ('Run') and the resource ('multivariate analysis of variance (MANOVA) for multiple dependent variables'), which precisely identifies what the tool does. It also mentions univariate follow-ups, making it distinct from univariate ANOVA tools like spss_anova.

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

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

The description implies when to use the tool by specifying it is for 'multiple dependent variables', but it does not explicitly mention alternatives or exclusionary conditions. It only notes a prerequisite (SPSS installed), which is useful but not full usage 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 provided, the description bears full responsibility for behavioral disclosure. It explains the three-step process, the regression-based paths, and the Sobel test, and explicitly states a limitation (no PROCESS macro). This adds useful context beyond the name, though it does not mention assumptions (e.g., normality, variable types) or whether the tool modifies files.

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

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

The description is two sentences, front-loaded with the core purpose, and contains no redundant information. Every phrase adds value, from the specific method to the output details and the PROCESS macro exclusion.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's statistical complexity and the absence of annotations, the description covers the main workflow, outputs, and a key limitation. The output schema presumably handles return-value documentation. It omits assumptions and data prerequisites, but the description is still reasonably complete for an agent to understand the tool's role and select it appropriately.

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 input schema has no descriptions (0% coverage), so the description must compensate. It maps x, mediator, y to the X, M, Y roles in the mediation model, which adds meaning. However, file_path is not explained, and there is no mention of variable types, ordering, or data preparation requirements. The param names are self-explanatory, but the description only partially bridges the schema gap.

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

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

The description uses a specific verb ('Run') and clearly identifies the resource ('Baron & Kenny three-step mediation analysis (X -> M -> Y)'). It lists exact outputs (paths a/b/c/c', indirect effect a*b, Sobel test) and distinguishes itself from PROCESS macro, differentiating it from sibling tools like spss_regression and spss_moderation.

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

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

The description implies when to use this tool by specifying the mediation method and output. It also notes a key exclusion ('Does not bundle the PROCESS macro'), guiding users away from expecting that functionality. However, it does not explicitly state 'use this instead of regression when examining mediation' or mention any alternative tool for other mediation approaches.

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

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

With no annotations provided, the description carries the full burden of disclosing behavior. It mentions support for nested/crossed random effects and repeated measures structures, but does not describe side effects, output format, error conditions, or whether any data is modified. The only operational detail is the requirement that SPSS be installed.

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

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

The description is concise and front-loaded with the core action. It wastes no words and adds meaningful context about the type of model and prerequisite. It is appropriately sized for a tool of this complexity, though it could be structured slightly better by separating capabilities from requirements.

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?

This is a complex model-fitting tool with 9 parameters, no annotations, and zero schema coverage. The description does not explain how to specify random effects, fixed effects, subject/repeated terms, or covariance structures, making it difficult for an agent to invoke correctly without additional external knowledge. The output schema exists but cannot compensate for missing parameter and usage context.

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

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

Schema description coverage is 0% and the description does not mention any of the 9 parameters, including the required ones like file_path, dependent, and fixed_effects. The description must compensate for the lacking schema documentation but does not, leaving an agent with no semantic understanding of how to populate the 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 clearly states the specific action ('Run') and the resource ('linear mixed-effects model'), and further clarifies it as a multilevel model with random effects. This distinguishes it from many siblings that run other analysis types, such as spss_anova or spss_regression.

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 provides no explicit guidance on when to use this tool versus close alternatives like spss_genlinmixed, spss_repeated_measures_anova, or spss_glm_univariate. It implies use for multilevel/repeated measures data, but does not state when not to use it or what distinguishes it from these siblings.

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

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It adds value by explaining that variables are mean-centred and that the interaction term is reported. However, it does not mention potential side effects like data modification or requirements like variable types/numeric ranges, so it is not fully transparent.

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

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

The description is a single, front-loaded sentence that delivers the essential information without any wasted words. It is concise yet precise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return values are covered by that. The description sufficiently describes the statistical method and purpose for an agent to select the tool correctly, but it could include assumptions like continuous moderator not being explicitly stated.

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

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

Schema description coverage is 0%, so the description must compensate. It clarifies the roles of x, moderator, and y through the model formula, but does not explain file_path or specify the expected input types (e.g., variable names vs. column indices). This is partial compensation.

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 uses a specific verb ('Run'), names the resource ('mean-centred moderation regression'), and specifies the exact model formula (Y ~ X + W + X*W). It clearly states the output (interaction term) and differentiates from sibling tools like spss_regression and spss_mediation.

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 clearly indicates the tool is for testing moderation effects, which implies when to use it. However, it does not explicitly mention when not to use it or name alternative tools (e.g., if only main effects are needed, use spss_regression).

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool runs tests and requires SPSS, but it does not disclose whether executing a test modifies data, creates output artifacts, or has side effects in the SPSS session. This is a thin behavioral profile for a command-style tool.

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

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

The description is two sentences with no filler. The first sentence front-loads the action and test options, and the second adds a relevant prerequisite. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The existing output schema reduces the need to explain return values, but this is still a five-parameter tool with conditional parameter requirements and no annotations. The description leaves an agent unable to correctly shape the variables, grouping_variable, and group_values arguments for the selected test type.

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 does not compensate by explaining any of the five parameters. It does not clarify that Wilcoxon uses two variables, that Mann-Whitney and Kruskal-Wallis require grouping_variable and group_values, or what file_path and variables should contain. The enum list merely repeats the test_type schema values.

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

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

The description uses a specific verb ('Run') and a clear resource ('common nonparametric tests in SPSS'), and it names the three supported tests. It distinguishes the tool from parametric sibling tools by the word 'nonparametric,' though it does not explicitly contrast it with a sibling.

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 only usage-related information is the prerequisite that IBM SPSS Statistics must be installed. There is no guidance about when to choose this tool over t_test, anova, or other siblings, and no guidance about when each of the three nonparametric tests is appropriate.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It transparently states that the tool invokes SPSS EXAMINE and requires IBM SPSS Statistics to be installed, and it notes that plots are optional. It does not explicitly state whether the operation modifies data or is read-only, though the verb 'check' suggests an analytical rather than mutating action.

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

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

Two sentences convey the procedure, purpose, variable scope, optional behavior, and a critical prerequisite. There is no filler, and the most important information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main purpose, the underlying SPSS command, the prerequisite installation, and the optional plots. However, it lacks explicit guidance on file_path semantics and on how to choose this tool over closely related sibling statistical procedures, even though the modest parameter complexity and existing output schema reduce the overall 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?

The schema has 0% description coverage, so the description must compensate. It clarifies that variables should be numeric and that plots are diagnostic, but it leaves the required file_path parameter as just a string with no indication of the expected file format or SPSS dataset context. The variables array item type is also unspecified 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 clearly states a specific verb and resource: it runs SPSS EXAMINE to check normality and outliers for numeric variables. It also mentions optional diagnostic plots, which distinguishes it from the many sibling statistical and data-management tools.

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

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

The description implies the appropriate use case: checking normality and outliers for numeric variables. It does not explicitly contrast this with sibling tools such as spss_descriptives or spss_graph_boxplot, nor does it state when not to use it, though the SPSS prerequisite is mentioned.

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 bears the full burden. It mentions a runtime prerequisite and the parallel lines test capability, but it does not disclose side effects, whether data is modified, what happens with saved predictions, or what output behavior is expected beyond what the output schema might provide.

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 compact and front-loaded, with each sentence contributing meaningful information: what the tool does, what capabilities it has, and a key prerequisite. There is no filler or redundant restating of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core purpose, typical use case, and a key external dependency, while an output schema exists to document return values. However, given a 7-parameter tool with no annotations and near-zero schema description coverage, it lacks guidance on parameter semantics, data prerequisites, and alternative tool selection, making it adequate but not 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 0%, so the description must compensate. It adds context for the 'link' parameter ('multiple link functions') and 'test_parallel' ('parallel lines test'), but required parameters like file_path, dependent, and predictors are not explained, leaving the agent to infer their meaning from names alone.

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 action ('Run ordinal regression (PLUM)') and the target scenario ('ordered categorical outcomes'), making the tool's purpose clear. It distinguishes itself from siblings like spss_logistic_regression by naming PLUM and the ordered-outcome use case, though it does not explicitly compare against alternatives.

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

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

The phrase 'for ordered categorical outcomes' implies when this tool should be used, and the prerequisite 'Requires IBM SPSS Statistics to be installed' is a useful condition. However, it does not explicitly say when to prefer this over spss_regression, spss_logistic_regression, or spss_genlin, nor does it state when not to use it.

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 exist, so the description carries the full behavioral burden. It discloses genuinely useful traits: output as a Markdown table, optional variable filtering, row-count limiting, and no SPSS installation requirement. However, it does not mention value-label behavior even though apply_value_labels defaults to true, and it never explicitly confirms the read-only safety profile.

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?

Three short sentences with the core function front-loaded. Every sentence adds information — no filler, no repetition of schema defaults. Appropriately sized for the tool's low-to-moderate complexity.

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 read tool the description covers the essentials, and the presence of an output schema handles return-value documentation. But with no annotations and 0% schema coverage, the undocumented apply_value_labels parameter and the lack of routing guidance against spss_read_metadata leave meaningful gaps. Adequate, but an agent could still misfire on value-label expectations.

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 translates two parameters into plain language ('filter to specific variables' → variables, 'limit row count' → max_rows) and implies file_path is the .sav path. However, apply_value_labels is documented nowhere in either the schema or description, leaving one of four parameters completely 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?

States a specific action with resource and output format: 'Read rows of data from an SPSS .sav file as a Markdown table.' The 'rows of data' phrasing clearly distinguishes it from the close sibling spss_read_metadata, which reads metadata rather than data. An agent can tell this is the data-reading tool among many analysis siblings without opening schemas.

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

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

The description implies when to use it — whenever actual data rows from a .sav file are needed — but never contrasts it with alternatives like spss_read_metadata, spss_list_variables, or spss_file_summary. The 'Does not require SPSS to be installed' note is useful context but not usage routing. No explicit when-not-to-use guidance is present.

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 well: 'Read' clearly indicates a non-mutating operation, 'Returns a detailed Markdown report' sets output expectations, and 'Does not require SPSS to be installed' adds an important environment requirement. It does not explicitly state 'does not modify the file', but the read verb makes that reasonably clear.

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

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

Two concise sentences with no filler. The core operation and output format are front-loaded, and the environment requirement is a useful final note.

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 read-only tool with an output schema, the description is largely complete: it names the input file type, the metadata returned, the output format, and the lack of SPSS dependency. The main missing piece is explicit routing among sibling metadata tools, but that is more of a usage-guideline gap than a contextual completeness 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?

The schema provides zero description for the file_path parameter, so the description must compensate. It adds that the file is an SPSS .sav file, which gives the parameter semantic context, but it does not explicitly explain path requirements, accessibility, or expected format beyond that.

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 action ('Read') with an unambiguous resource ('SPSS .sav file') and enumerates the metadata types returned. The description is clear and would distinguish it from spss_read_data, but it does not explicitly differentiate it from sibling metadata tools like spss_list_variables or spss_value_labels.

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?

Intended use can be inferred from the description: call this when you need file structure metadata like variable names and value labels. However, there is no explicit guidance about when to use this over sibling tools such as spss_file_summary or spss_list_variables, and no exclusions or when-not-to-use cases.

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

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

With no annotations, the description carries the transparency burden. It discloses the SPSS installation requirement and summarizes the returned statistics, but it does not mention side effects, failure modes, data mutation risks, or the effect of the diagnostics flag.

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?

Three short sentences, front-loaded with the action, and no filler. Each sentence contributes useful information: the operation, the inputs, the outputs, and the environment requirement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The core operation and required inputs are covered, and the output schema presumably handles return details. However, with five parameters and zero schema coverage, the description omits meaningful semantics around method and include_diagnostics, leaving an agent to guess valid values and behavior.

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

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

The schema has no parameter descriptions (0% coverage), so the description must compensate. It mentions dependent and predictors, but leaves file_path, method (e.g., ENTER vs stepwise), and include_diagnostics unexplained. The description only partially compensates for the schema gap.

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

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

The description uses a precise verb ('Run') with a specific resource ('SPSS linear regression') and the qualifier 'linear' clearly separates it from the many regression siblings like spss_logistic_regression or spss_ordinal_regression. It states exactly what the tool performs.

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

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

The description implies when to use it (when a linear regression is needed) and provides a key prerequisite (SPSS must be installed). However, it gives no explicit guidance about when not to use it or which alternative tool should be chosen instead, such as logistic regression for binary outcomes.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully states that the tool returns scale reliability and item statistics and that IBM SPSS Statistics must be installed. However, it does not explain what happens when SPSS is absent, whether any files are written, or other runtime behaviors.

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

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

The description is two sentences with no filler. The core action is front-loaded, and the prerequisite about IBM SPSS Statistics is useful supporting information. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although an output schema exists and the tool is narrowly scoped, the description omits essential parameter guidance and leaves model options and scale_name undefined. An agent would likely need additional inference or external knowledge to call this correctly with all optional parameters.

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 compensates for none of the four parameters. It does not explain file_path, variables, the model ALPHA/OMEGA distinction, or scale_name. The mention of Cronbach's alpha only hints at the default model but does not clarify the parameter semantics.

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

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

The description clearly identifies the tool's action ('Run SPSS RELIABILITY analysis'), the specific method (Cronbach's alpha), and the domain (psychometric workflows). It is distinct from the many sibling analysis tools and leaves no ambiguity about what the tool 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?

The phrase 'for psychometric workflows' provides clear context for when this tool is appropriate, and the specificity of Cronbach's alpha implies its use case. It does not explicitly name alternative tools or exclusions, but the niche scope makes the usage context reasonably clear.

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 must carry behavioral disclosure. It usefully states that IBM SPSS Statistics must be installed, an important external dependency, and 'Run' implies an analysis rather than a data mutation. However, it does not describe error behavior, variable type constraints, or what the returned output represents.

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

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

Two sentences, each earning its place: the first identifies the operation and the required input pattern, and the second gives the prerequisite. The essential information is front-loaded and there is 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?

For a complex statistical tool with no annotations and 0% schema description coverage, the description covers the core invocation (factor, levels, variables) but omits behavior around include_pairwise and any assumptions about the data/variables. An output schema exists, but the description alone is only minimally 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%, so this description compensates for the key required parameters by saying to provide the within-factor name, number of levels, and one variable per level. file_path is self-evident, but the optional include_pairwise parameter is not explained despite having a default 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 uses a specific verb ('Run') and names the exact analysis ('SPSS repeated-measures ANOVA'), clarifying it is the within-subject GLM. This distinguishes it from sibling tools such as spss_anova or spss_manova.

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

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

The phrase 'repeated-measures ANOVA (within-subject GLM)' gives a clear statistical context for when the tool applies. It does not name alternatives or state when not to use it, but the repeated-measures framing and 'Requires IBM SPSS Statistics' prerequisite provide enough contextual 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. It discloses key side effects (persisting .spv and .sps files by default), prerequisites (IBM SPSS Statistics installed), and output format (Markdown). This goes beyond the schema and adds important context.

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

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

The description is three sentences, front-loaded with the core action and output, followed by optional data_file behavior and then side effects/prerequisites. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex (7 params, no annotations) but has an output schema, so return values need not be explained. The description covers purpose, side effects, and prerequisites, but it misses key parameter behaviors like filtering and dry_run, leaving the description incomplete for fully reliable invocation.

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

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

Schema description coverage is 0%, so the description must compensate. It explains data_file (prepends GET FILE) and indirectly the save flags (persisting files), but fails to describe dry_run, select_if, and filter_variable. These parameters remain under-defined for a 7-parameter tool.

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

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

The description clearly states the tool's purpose: 'Execute arbitrary SPSS syntax commands and return the output as Markdown.' This distinguishes it from sibling tools that provide specific analyses, making the general-purpose nature explicit.

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

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

No guidance is provided about when to use this tool versus alternatives. Given the many sibling tools for specific procedures, the description should mention that this is for arbitrary syntax not covered by dedicated methods, but it does not.

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