MCP Data Wrangler

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose focused on specific statistical calculations or data structure analysis. The tools are well-differentiated by their mathematical functions (mean, median, std, var, etc.) and orientation (column vs. horizontal), with no ambiguity about which tool to use for each operation.
Naming Consistency5/5
All tools follow a consistent 'data_' prefix with descriptive suffixes that clearly indicate their function. The naming pattern is uniform throughout (snake_case, descriptive terms), making it easy to understand what each tool does from its name alone.
Tool Count4/5
Sixteen tools is slightly high but reasonable for a comprehensive data analysis toolkit. The server covers extensive statistical operations, which justifies the count, though some tools like data_product might be less commonly used compared to core statistics.
Completeness5/5
The toolset provides complete coverage for data wrangling and statistical analysis, including descriptive statistics (mean, median, std, var), data structure inspection (schema, shape), and specialized calculations (quantiles, horizontal operations). There are no obvious gaps for this domain.
Average 2.5/5 across 16 of 16 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description fails to indicate whether this is a read operation, what format the output takes, whether it validates or just describes schemas, or any behavioral characteristics like error conditions or performance considerations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just 5 words, this is under-specification rather than effective conciseness. The description fails to provide essential information about the tool's purpose and behavior, making it inefficient for agent understanding despite its brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is completely inadequate. It doesn't explain what the tool returns, how to interpret results, or what value it provides compared to sibling tools. The agent would have no idea what to expect from invoking this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage for the single parameter, the schema already documents that the tool takes a file path. The description adds no additional parameter semantics beyond what's in the schema, so the baseline score of 3 is appropriate when the schema does the documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Data schema of the input data' is a tautology that essentially restates the tool name 'data_schema' without specifying what action the tool performs. It doesn't indicate whether this retrieves, analyzes, validates, or generates schemas, nor does it distinguish this tool from its many siblings that also operate on data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance about when to use this tool versus alternatives. With 14 sibling tools all operating on data, there's no indication of what specific problem this tool solves or when it should be selected over tools like 'data_shape' or 'describe_data' which might provide related information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description gives no indication of what 'data shape' means operationally, whether this is a read-only analysis, what format the output takes, or any computational characteristics. It fails to provide meaningful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise at 5 words, this is under-specification rather than effective brevity. The description fails to convey essential information about the tool's purpose and behavior, making it inefficient despite its short length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a vague description, this is inadequate for a data analysis tool. The description doesn't explain what 'data shape' means, what information it returns, or how it differs from similar tools like data_schema. The context is insufficient for an agent to understand when and how to use this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents the single parameter. The description adds no additional semantic context about the parameter beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Data shape of the input data' is a tautology that essentially restates the tool name 'data_shape' without specifying what action it performs. It doesn't provide a clear verb (e.g., 'analyze', 'calculate', 'return') or distinguish this tool from its many siblings that also operate on data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With 14 sibling tools including data_schema and describe_data that might serve similar purposes, there's no indication of what differentiates this tool or when it's appropriate to select it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It fails to do so—it does not explain what 'product' means operationally, whether it modifies data, requires specific permissions, handles errors, or what the output looks like. This leaves critical behavioral aspects undefined.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single phrase—and front-loaded with the core purpose. There is no wasted language, making it efficient in structure, though this brevity contributes to its lack of clarity and completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a data processing tool with no annotations, no output schema, and vague purpose, the description is incomplete. It does not explain what 'product' computes, how results are returned, or any prerequisites, failing to provide adequate context for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with one parameter clearly documented as 'Path to the input data file'. The description adds no additional meaning about parameters beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Product values for each column' is vague and tautological—it essentially restates the tool name 'data_product' without specifying what operation 'product' refers to (e.g., multiplication, aggregation, or something else). It does not clearly distinguish this tool from siblings like data_max or data_mean, which also operate on columns but with different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like data_max, data_mean, and data_quantile available, there is no indication of what 'product' calculates or in what scenarios it is preferred over other statistical tools, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden but offers no behavioral information. It doesn't indicate whether this is a read-only operation, what permissions are needed, how results are returned, or any side effects. For a statistical calculation tool with file input, this lack of transparency is critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (4 words) and front-loaded, with no wasted words. However, it's arguably under-specified rather than optimally concise, as it lacks essential context for tool selection and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of statistical operations, file input, and no annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., format of variance values), error conditions, or how it interacts with the data file, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional meaning about 'input_data_file_path' or 'ddof' beyond what the schema provides, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Variance values for each column' is a tautology that essentially restates the tool name 'data_var' without specifying the action verb or distinguishing it from sibling tools. It doesn't clarify whether this calculates, retrieves, or displays variance, nor how it differs from similar statistical tools like 'data_std' (standard deviation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With multiple sibling statistical tools (mean, median, std, etc.), the description offers no context about appropriate use cases, prerequisites, or comparisons to help an agent choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool estimates size but doesn't reveal how it works (e.g., sampling vs. full scan), performance implications, accuracy, or output format. For a tool with zero annotation coverage, this leaves critical behavioral traits unspecified, though it doesn't contradict anything.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without fluff. It's appropriately sized for a simple tool, though it could be more front-loaded with key details. There's no waste, but it borders on under-specification rather than optimal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what 'estimated' means (e.g., approximation method), what the output looks like, or any limitations. For a tool that performs estimation—a potentially nuanced operation—this lacks necessary context to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for both parameters (input_data_file_path and unit with enum). The description adds no meaning beyond the schema—it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate or enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Estimated size of the input data' states what the tool does (estimating size) but is vague about scope and method. It doesn't specify whether this estimates file size, memory footprint, or something else, nor does it distinguish from siblings like data_shape or data_schema that might provide related metadata. The purpose is understandable but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like data_shape (which might include size info) and data_schema (for structure), there's no indication of when estimation is preferred over exact measurements or other metadata tools. Usage is implied only by the tool name, with no explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool calculates mean values, implying a read-only operation, but doesn't disclose critical behaviors: whether it modifies the input file, requires specific data formats, handles errors (e.g., missing values), or returns results in a particular structure. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise ('Mean values for each column'), which is efficient and front-loaded with the core purpose. However, it's under-specified rather than optimally concise—it could benefit from slightly more detail (e.g., 'Calculates the arithmetic mean for numerical columns in a data file') without becoming verbose. Every word earns its place, but more value could be added.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a statistical calculation tool), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a dictionary of column means), error conditions, or dependencies on data types. For a tool with 1 parameter but significant behavioral implications, more context is needed to make it fully usable by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 100% description coverage ('Path to the input data file'), so the schema does the heavy lifting. The description adds no meaning beyond the schema—it doesn't explain what 'input data file' entails (e.g., CSV, JSON), constraints, or examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Mean values for each column' states what the tool does (calculates means) but is vague about the resource (data from a file) and lacks specificity. It distinguishes from siblings like data_max or data_min by indicating it calculates means rather than other statistics, but doesn't clarify the scope (e.g., numerical columns only) or how it handles non-numerical data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., requires a data file), exclusions (e.g., not for categorical data), or comparisons to siblings like data_mean_horizontal (for row-wise means) or describe_data (which might include means). Usage is implied from the name and context, but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool calculates medians but doesn't describe how it handles missing values, data types, errors, or the return format. For a statistical tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that gets straight to the point without unnecessary words. It's appropriately sized for a simple tool, though it could be more informative. There's no structural issue, but it's borderline under-specified rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a statistical calculation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of medians, a summary object) or handle edge cases. For a tool that likely processes data files, more context is needed to be fully useful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'input_data_file_path' documented in the schema as 'Path to the input data file'. The description doesn't add any meaning beyond this, such as file format requirements or path examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Median values for each column' states what the tool does (calculates medians) but is vague about the resource (data from a file path). It doesn't distinguish from siblings like data_mean or data_quantile, which also compute statistical measures on data columns. The purpose is understandable but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention siblings like data_mean (for averages) or data_quantile (for other percentiles), nor does it specify prerequisites or contexts for choosing median over other statistical tools. The description offers no usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'quantile values for each column' but doesn't explain what the tool returns (e.g., a list, dictionary, or structured output), any performance considerations, or error handling. For a tool with no annotations and no output schema, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a single phrase 'Quantile values for each column', which is front-loaded and wastes no words. However, it's arguably too brief, bordering on under-specified, but within the bounds of efficient communication for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a statistical tool with potential complexity, the description is incomplete. It doesn't clarify the return format, how results are structured, or any prerequisites (e.g., data format). For a tool with 3 parameters and siblings offering similar functions, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents parameters like input_data_file_path, interpolation, and quantile. The description adds no additional meaning beyond the schema, such as explaining how quantiles are applied per column or the impact of interpolation choices. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Quantile values for each column' states what the tool does but is vague about the specific action. It mentions 'quantile values' and 'each column' but doesn't specify the verb (e.g., 'calculate' or 'compute') or distinguish it from sibling tools like data_median or data_percentile (not listed but implied by context). It's better than a tautology but lacks precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like data_median (which is a specific quantile at 0.5) and data_mean, there's no indication of when quantile analysis is preferred over other statistical measures. No context or exclusions are mentioned, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 only states what is computed, without mentioning how the tool behaves (e.g., it reads a file, processes numeric data, returns a list or dict, potential errors for non-numeric columns, or performance considerations). For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's output. It's appropriately sized and front-loaded with the key information. However, it could be slightly more structured by including a brief example or context, but it avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (statistical computation with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return format (e.g., a dictionary mapping columns to std values), error handling, or data requirements. For a tool with siblings and no structured output info, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters ('input_data_file_path' and 'ddof'). The description adds no parameter semantics beyond what the schema provides (e.g., it doesn't explain file format expectations or the practical impact of 'ddof'). With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states what the tool calculates ('Standard deviation values for each column'), which provides a basic purpose. However, it's vague about the resource (what data is being processed) and doesn't distinguish it from sibling tools like 'data_var' (variance) or 'describe_data' (which might include std). It specifies 'for each column' which helps, but lacks the specific verb+resource clarity needed for higher scores.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'data_var' (variance), 'data_mean' (mean), and 'describe_data' (comprehensive stats), there's no indication of when standard deviation is preferred or what context it applies to. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool computes 'summary statistics' but doesn't specify what statistics are included (e.g., mean, median, percentiles, count), how missing data is handled, whether it's a read-only operation, or what the output format looks like. For a tool with no annotations, this leaves critical behavioral traits undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, stating the core purpose immediately. Every word earns its place, making it easy to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of statistical computation, no annotations, no output schema, and a crowded set of sibling tools, the description is incomplete. It fails to specify what summary statistics are computed, how they relate to the parameters, or what the output includes. This leaves significant gaps for an agent to understand the tool's behavior and choose it appropriately among alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all three parameters (input_data_file_path, interpolation, percentiles). The description adds no meaning beyond what the schema provides—it doesn't explain how parameters relate to the summary statistics or provide usage examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Summary statistics of the input data' states what the tool does in a general sense (verb: 'summary statistics', resource: 'input data'), but it's vague about what specific statistics are computed. It doesn't distinguish this tool from siblings like data_mean, data_median, or data_quantile, which also provide statistical summaries. The purpose is understandable but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many sibling tools (e.g., data_mean, data_median, data_quantile). It doesn't mention alternatives, prerequisites, or exclusions. Without any context, an agent must infer usage from the tool name and parameters alone, which is insufficient given the crowded namespace of statistical tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states what the tool does but doesn't describe how it behaves: e.g., whether it handles large files efficiently, requires specific file formats, returns results in a particular structure, or has any side effects. For a tool with no annotations, this leaves critical operational details unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single, clear sentence that states the core functionality without unnecessary words. It's front-loaded with the essential purpose, making it easy to parse. Every word earns its place, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a data analysis tool with many siblings), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a dictionary, list, or formatted string), how errors are handled, or prerequisites for use. This leaves significant gaps for an agent to understand the tool fully in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'input_data_file_path' clearly documented. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't specify accepted file types or path formats). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: counting non-null elements per column. It uses specific verbs ('Number of') and identifies the resource ('non-null elements for each column'). However, it doesn't explicitly differentiate from siblings like data_shape (which might give dimensions) or describe_data (which might provide summary statistics), leaving room for ambiguity in a crowded namespace.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for data analysis (e.g., data_mean, data_median, data_schema), there's no indication of whether this is for data quality checks, preprocessing, or other contexts. It lacks explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Maximum values for each column' but doesn't disclose behavioral traits like whether it handles missing data, requires numeric columns, returns errors for non-numeric data, or outputs format (e.g., list, dictionary). This leaves significant gaps for a tool that processes data files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description 'Maximum values for each column' is extremely concise and front-loaded, with no wasted words. It directly conveys the core purpose in a single phrase, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It doesn't explain what the tool returns (e.g., a dictionary of column max values), error conditions, or data processing behavior, which are crucial for a data analysis tool with many siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the single parameter 'input_data_file_path' is documented as 'Path to the input data file'), so the baseline is 3. The description adds no additional parameter semantics beyond what the schema provides, such as file format requirements or path examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Maximum values for each column' clearly states the tool's function (calculating maximum values) and resource (columns in data). It distinguishes from siblings like data_min (minimum values) and data_mean (average values), though not explicitly. However, it doesn't specify the verb (e.g., 'calculate' or 'compute'), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like data_min, data_mean, and describe_data (which might include max values), there's no indication of when this specific maximum calculation is preferred or what distinguishes it from similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While 'Maximum values' implies a read-only calculation, the description doesn't disclose important behavioral traits: what format the input file should be, whether the operation modifies data, what the output looks like, or any error conditions. For a tool with no annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, front-loaded with the core functionality. Every word earns its place by specifying the operation, scope, and orientation. There's zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (presumably maximum values per row), what format the input should be, or how results are presented. For a data processing tool with 1 parameter but no structured output documentation, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'input_data_file_path' well-documented in the schema. The description adds no additional parameter information beyond what the schema provides, which is acceptable given the high schema coverage. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Maximum values' and the resource 'across columns for each row', which specifies what the tool does. It distinguishes from some siblings like data_count or data_shape, but doesn't explicitly differentiate from data_max (which might be vertical) or data_min_horizontal (which is the opposite operation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools performing different statistical operations (mean, median, min, max, etc.), there's no indication of when horizontal maximum calculation is appropriate versus vertical maximum or other aggregations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but reveals minimal behavioral traits. It states what computation is performed but doesn't disclose format requirements, error conditions, memory/performance characteristics, or what happens with missing/invalid data. The description doesn't contradict annotations (none exist), but provides inadequate behavioral context for a computational tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that communicates the core computation. There's no wasted verbiage or redundancy. However, it could be slightly more complete by mentioning the input type or output format to be fully self-contained.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a computational tool with no annotations, no output schema, and siblings performing similar operations, the description is insufficient. It doesn't explain what the tool returns (vector of row means? modified dataset?), data format expectations, or how it differs from similar tools. The agent would need to infer too much from the name and minimal description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the single parameter 'input_data_file_path' is fully described in the schema). The description adds no parameter-specific information beyond what the schema provides. With high schema coverage and only one parameter, the baseline score of 3 is appropriate - the description doesn't need to compensate but adds no extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Mean values across columns for each row' clearly states the mathematical operation (mean) and the direction of computation (across columns for each row). It distinguishes from siblings like 'data_mean' (likely overall mean) and 'data_max_horizontal' (different operation). However, it doesn't specify the resource/input type (data file) or output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'data_mean' (likely overall mean), 'data_median' (different central tendency), or other horizontal operations like 'data_max_horizontal'. The description implies row-wise computation but doesn't explicitly contrast with column-wise or overall alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Minimum values for each column' implies a read-only statistical computation, but it doesn't specify whether this requires specific data formats, what happens with missing values, whether it modifies the input file, or what the output format looks like. For a data processing tool with zero annotation coverage, this is insufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words ('Minimum values for each column'). It's front-loaded with the core purpose and contains zero wasted words. Every word earns its place by conveying essential information about what the tool computes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of data processing operations and the lack of both annotations and output schema, the description is incomplete. It doesn't explain what format the minimum values are returned in, whether the operation handles different data types, what happens with non-numeric columns, or any error conditions. For a statistical computation tool with no structured metadata, the description should provide more complete context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'input_data_file_path' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema already provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Minimum values for each column' clearly states the tool's purpose as computing minimum values for data columns. It uses specific terminology ('minimum values', 'each column') that distinguishes it from siblings like data_max or data_mean. However, it doesn't explicitly mention what resource it operates on (data files), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools performing different statistical operations (data_max, data_mean, data_median, etc.), there's no indication of when minimum values are appropriate versus other summary statistics. The description lacks any context about use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but provides minimal information. It states what the tool calculates but doesn't describe how it handles missing values, data types, errors, or the format of results. For a data transformation tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core functionality and uses precise mathematical terminology. Every word earns its place in communicating the essential operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a data transformation operation with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., modified dataset, summary statistics, new file), how results are formatted, or any limitations. For a tool that performs mathematical operations on data files, more context about behavior and outputs is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter fully documented in the schema. The description adds no parameter-specific information beyond what the schema already provides about 'input_data_file_path'. Since the schema handles parameter documentation adequately, the baseline score of 3 is appropriate despite the description's lack of parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: calculating minimum values across columns for each row. It uses specific mathematical terminology ('minimum values') and specifies the operation direction ('across columns for each row'), which distinguishes it from vertical operations. However, it doesn't explicitly differentiate from sibling tools like 'data_min' (which likely calculates minimums across rows for each column).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'data_min' (vertical minimum), 'data_max_horizontal' (horizontal maximum), or other statistical operations. There's no context about appropriate data types, when horizontal vs vertical operations are needed, or prerequisites for using this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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