Data Analytics MCP Toolkit

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, with clear separation between data loading/cleaning, plotting, model training, and evaluation functions. However, 'run_analytics' overlaps significantly with the specialized tools, as it can perform many of the same functions through a single interface, which could cause confusion about when to use it versus the specific tools.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern throughout, with clear and predictable naming conventions. All tools use snake_case, and verbs like 'clean', 'evaluate', 'load', 'plot', 'run', 'train', and 'train_test_split' are applied consistently to their respective nouns, making the set highly readable and predictable.
Tool Count4/5
With 16 tools, the count is slightly high but reasonable for a comprehensive data analytics toolkit. It covers a broad range of functions from data ingestion to visualization and machine learning, though it might be borderline heavy for some use cases. Each tool appears to earn its place without obvious redundancy, except for the overlap with 'run_analytics'.
Completeness5/5
The tool set provides complete coverage for a data analytics workflow, including data loading, cleaning, splitting, multiple types of plots, training for classification, regression, and clustering models, and corresponding evaluation metrics. The inclusion of 'run_analytics' as a high-level tool further ensures no gaps, offering a flexible alternative for common tasks.
Average 2.9/5 across 16 of 16 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 the full burden of behavioral disclosure. It fails to mention that this tool likely generates a plot (implied but not stated), what format the output is in, whether it modifies data, or any performance considerations. For a tool with 5 parameters and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just one sentence with no wasted words. It's front-loaded with the core purpose, making it easy to scan. Every word earns its place by conveying essential information about what the tool does.
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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, output format, or parameter meanings. While conciseness is high, the lack of contextual details makes it inadequate for an agent to fully understand how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 parameters. It only mentions 'numeric column', which partially covers the 'column' parameter but ignores 'data_id', 'bins', 'title', and 'session_id'. This leaves most parameters undocumented, failing to add meaningful semantics beyond the bare schema.
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 as creating a histogram for a numeric column to show distribution. It specifies the verb ('Histogram') and resource ('numeric column'), distinguishing it from other plotting tools like plot_bar or plot_scatter. However, it doesn't explicitly differentiate from plot_box, which also shows distribution, leaving room for slight ambiguity.
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 prerequisites (e.g., data must be loaded first), compare to other plotting tools for distribution analysis, or specify when a histogram is preferred over other visualizations like box plots. This lack of context makes it harder for an agent to select this tool appropriately.
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 the full burden of behavioral disclosure. It mentions the output (model_id) but fails to describe critical behaviors: whether training is resource-intensive, if it modifies existing data, what happens on errors, or any performance characteristics. This leaves significant gaps for an AI agent to understand the tool's operational impact.
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 brief and front-loaded with the core action, consisting of two concise sentences. However, the second sentence about evaluate_regression feels slightly tacked on without integration into a broader workflow explanation, slightly reducing efficiency.
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 machine learning training tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It lacks details on input formats, error handling, performance, and integration with sibling tools, making it incomplete for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 adds no parameter semantics. It doesn't explain what train_data_id refers to, how target_column and feature_columns should be formatted, or the purpose of session_id. This leaves all 4 parameters undocumented beyond their schema types, creating ambiguity for proper usage.
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 action ('Fit a linear regression model') and the resource ('model'), distinguishing it from siblings like train_kmeans or train_logistic_regression. However, it doesn't specify what 'fit' entails beyond returning a model_id, leaving some ambiguity about the training process itself.
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 like train_logistic_regression or train_kmeans, nor does it mention prerequisites such as needing pre-loaded data via load_data. It only hints at a follow-up action (evaluate_regression) without explaining the context for choosing linear regression over other methods.
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 accuracy, implying a read-only evaluation, but doesn't cover critical aspects like whether it modifies data, requires specific permissions, handles errors, or returns detailed metrics beyond accuracy. For a tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Compute accuracy for a classification model on test data.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by conveying essential information.
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 (evaluation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'accuracy' entails (e.g., a single metric or detailed report), how results are returned, or any behavioral nuances. For a tool with no structured support, the description should do more to guide the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the three parameters (model_id, test_data_id, session_id) are documented in the schema. The description doesn't add any parameter semantics—it doesn't explain what these IDs refer to, their formats, or how they relate to the computation. This fails to compensate for the low schema coverage, leaving parameters largely unexplained.
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: 'Compute accuracy for a classification model on test data.' It specifies the verb 'compute accuracy' and the resource 'classification model on test data,' making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like evaluate_clustering or evaluate_regression, which lowers 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. It doesn't mention prerequisites (e.g., needing a trained model and test data), exclusions, or comparisons to sibling tools like evaluate_clustering or evaluate_regression. This lack of context leaves the agent to infer usage from the purpose alone.
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 action ('compute silhouette score') but lacks details on what this entails: e.g., whether it's a read-only operation, if it modifies data, performance characteristics, or output format. For an evaluation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Compute silhouette score for a clustering model on test data.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes essential information.
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 (evaluation operation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the silhouette score metric, what the output looks like, error conditions, or dependencies on other tools (e.g., train_kmeans). For a tool with no structured support, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds no information about the parameters (model_id, test_data_id, session_id), such as what they represent, expected formats, or how they relate to the computation. It fails to compensate for the low coverage, leaving parameters largely undocumented.
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: 'Compute silhouette score for a clustering model on test data.' It specifies the verb ('compute'), resource ('silhouette score'), and scope ('clustering model on test data'), which distinguishes it from siblings like evaluate_classification or evaluate_regression. However, it doesn't explicitly differentiate from all siblings (e.g., run_analytics might also involve evaluation), so it's not a perfect 5.
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 prerequisites (e.g., needing a trained clustering model), when not to use it (e.g., for non-clustering models), or direct alternatives among siblings. The context is implied (evaluation after training), but no explicit usage rules are given.
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, the description carries full burden but only states what the tool does, not behavioral traits. It doesn't disclose whether this is a read-only operation, if it modifies data, what permissions are needed, or how results are returned (e.g., format, potential errors).
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 that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every word contributing to understanding.
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, 0% schema coverage, no output schema, and three parameters, the description is incomplete. It lacks details on parameter meanings, behavioral context (e.g., safety, output format), and usage guidelines, making it inadequate for a tool with this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 adds no parameter information. It doesn't explain what model_id, test_data_id, or session_id represent, their formats, or how they relate to the computation, leaving all three parameters undocumented.
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 action ('Compute') and the specific metrics (MSE and R²) for a regression model on test data. It distinguishes from siblings like evaluate_classification and evaluate_clustering by specifying regression, but doesn't explicitly contrast with them.
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 like evaluate_classification or evaluate_clustering. The description implies usage with regression models but doesn't specify prerequisites (e.g., needing a trained model and test data) 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?
With no annotations provided, the description carries full burden but offers minimal behavioral information. It mentions the default column behavior but doesn't disclose whether this is a read-only operation, what permissions are needed, how the plot is displayed/returned, or any performance considerations. For a visualization tool with 4 parameters, this is insufficient.
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 with just two sentences that directly address the tool's core functionality and one parameter behavior. Every word earns its place with zero wasted text, though this conciseness comes at the cost of completeness.
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 visualization tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (plot format, file, display), how to interpret results, or provide sufficient parameter guidance. The minimal description leaves too many gaps for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 4 parameters, the description only partially addresses the 'columns' parameter behavior. It doesn't explain data_id (required parameter), title, or session_id. The description adds minimal value beyond the bare schema, failing to compensate for the complete lack of schema descriptions.
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 creates a 'Heatmap of correlation matrix' with a specific behavior when columns are omitted ('uses all numeric columns'). This provides a specific verb ('plot') and resource ('correlation matrix'), though it doesn't explicitly distinguish from sibling plotting tools like plot_bar or plot_scatter.
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 like plot_scatter for correlation visualization or other plotting tools. It mentions the default behavior when columns are omitted but doesn't specify use cases, prerequisites, 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?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose whether this creates a new plot, modifies an existing one, requires specific data formats, has side effects, or how errors are handled. The description is minimal and lacks 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 with a single sentence that front-loads the essential information. Every word earns its place, and there's no wasted text 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?
For a 5-parameter tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a plot object, file path, or visualization), doesn't cover all parameters, and provides minimal behavioral context despite the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 only mentions x_column and y_columns. It doesn't explain data_id, title, or session_id parameters, leaving 3 of 5 parameters undocumented. The description adds minimal value beyond the schema's property names.
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 creates a line chart with specific axis mappings ('x_column on x-axis, one or more y_columns as lines'), which is a specific verb+resource combination. It distinguishes from siblings like plot_bar or plot_scatter by specifying the chart type, though it doesn't explicitly contrast with all visualization alternatives.
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 like plot_scatter or plot_histogram. The description mentions the chart type but doesn't indicate appropriate data scenarios (e.g., time series, continuous data) or when other tools might be better suited.
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 (creates a scatter plot) but doesn't describe how it behaves: e.g., whether it displays the plot, saves it to a file, returns an image, requires specific data formats, or has any side effects. For a visualization tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single sentence that directly states the tool's function. It's front-loaded with the core action and avoids any unnecessary words. Every part of the sentence earns its place by specifying the plot type and key parameters.
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 visualization tool with 5 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a plot object, file path, or nothing), how errors are handled, or dependencies on other tools like load_data. This leaves significant gaps for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 undocumented parameters. It mentions x_column and y_column, which are two of the five parameters, but doesn't explain data_id, title, or session_id. The description adds minimal value beyond the schema, failing to clarify what these parameters mean or how they should be used.
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: creating a scatter plot with specific x and y columns. It uses the verb 'plot' and specifies the resource (data columns), distinguishing it from siblings like plot_bar or plot_line by mentioning the scatter plot type. However, it doesn't explicitly differentiate from other visualization tools beyond the plot type.
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 when scatter plots are appropriate compared to other plot types (e.g., plot_line for trends, plot_histogram for distributions) or other tools like evaluate_regression for analysis. There's no context about prerequisites or typical use cases.
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. It mentions the tool 'fits' a model and returns a model_id, implying it performs computation and creates a persistent resource, but lacks details on computational requirements, error conditions, side effects, or what 'fitting' entails operationally. This is inadequate for a modeling tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two clear sentences that efficiently state the action and output. Every word earns its place, with no redundant or vague phrasing, making it easy to parse 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?
For a machine learning training tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks essential context such as parameter explanations, behavioral traits, error handling, and performance characteristics, leaving significant gaps for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 parameters, but it provides no parameter information. The four parameters (data_id, n_clusters, feature_columns, session_id) are completely undocumented in the description, leaving their purposes and usage unclear beyond what minimal titles in the schema suggest.
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: 'Fit K-means clustering' specifies the verb and algorithm, and 'Returns model_id for evaluate_clustering' indicates the output and downstream use. However, it doesn't explicitly differentiate from sibling training tools like train_linear_regression or train_logistic_regression, 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 minimal guidance by mentioning the output is for evaluate_clustering, but offers no explicit when-to-use criteria, prerequisites, or comparisons to alternatives. With multiple sibling training tools available, there's no guidance on when to choose K-means over other algorithms or methods.
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, the description carries full burden but only states what the chart does, not behavioral traits like output format (e.g., image file, display), permissions, side effects, or error handling. It lacks details on what 'creates' entails operationally, leaving gaps for an agent to understand execution.
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 that front-loads the core functionality with zero wasted words. It directly explains the chart mapping and conditional behavior (y_column omission), making it highly concise and well-structured.
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 5 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It explains basic parameter roles but misses details on data_id usage, session_id purpose, title handling, and what the tool returns, leaving significant gaps for proper agent invocation.
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 0%, but the description adds meaning for x_column and y_column by explaining their roles (categories and values/count). It doesn't cover data_id, title, or session_id, leaving 3 of 5 parameters without semantic context, partially compensating but not fully.
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 creates a bar chart with specific mapping rules (x_column as categories, y_column as values, count if y omitted). It distinguishes from siblings like plot_line or plot_scatter by specifying bar chart type, though it doesn't explicitly contrast with other chart types beyond the basic function.
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 on when to use this tool versus alternatives like plot_histogram or plot_box is provided. The description implies usage for categorical vs. value data but doesn't specify scenarios, prerequisites, or exclusions compared to sibling visualization 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. It mentions the tool creates a box plot but lacks critical behavioral details: it doesn't specify if this is a read-only operation, what happens to the plot (e.g., displayed, saved, returned as data), whether it modifies data, or any performance considerations. The description is too sparse for a tool with potential side effects like visualization output.
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 sentence—with zero wasted words. It front-loads the core functionality ('Box plot') and efficiently explains key parameter behavior. Every part earns its place, 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 the complexity (a visualization tool with 4 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on what the tool returns (e.g., plot object, file path, or nothing), error conditions, dependencies on other tools like load_data, and how it fits into the broader analytics workflow. This is inadequate for guiding an agent in a multi-tool environment.
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 0%, so the description must compensate. It explains the 'column' parameter's behavior (single numeric column or all if omitted), adding meaning beyond the schema's basic type definitions. However, it doesn't cover other parameters like data_id, title, or session_id, leaving them undocumented. With 4 parameters total and only partial coverage, this meets the baseline for moderate schema coverage gaps.
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 creates a box plot visualization from numeric data, specifying 'single numeric column, or all numeric columns if column is omitted.' This is a specific verb+resource combination that distinguishes it from siblings like plot_bar or plot_histogram. However, it doesn't explicitly mention data visualization or differentiate from all siblings like plot_heatmap in terms of statistical vs. correlation focus.
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 minimal guidance: it implies usage when you want a box plot from numeric data, but offers no explicit when-to-use advice, prerequisites (e.g., data must be loaded first), or alternatives among siblings. For example, it doesn't clarify when to choose plot_box over plot_histogram for distribution analysis or how it relates to evaluation tools like evaluate_regression.
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. It mentions that the tool 'Returns model_id for evaluate_classification,' which hints at output behavior, but fails to disclose critical traits such as whether it modifies data, requires specific data formats, has computational costs, or error conditions. This leaves significant gaps in understanding how the tool behaves beyond basic functionality.
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 with two sentences that directly state the tool's purpose and output. There is no wasted language, and it is front-loaded with the main action, 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 the complexity of a machine learning training tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on parameter meanings, behavioral constraints, error handling, and output specifics beyond a model_id, making it incomplete for safe and effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 undocumented parameters. It adds no information about parameters like train_data_id, target_column, feature_columns, or session_id, leaving their meanings and usage unclear. This results in inadequate guidance for proper tool invocation.
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 action ('Fit a logistic regression classifier') and the resource (a classifier model), distinguishing it from sibling tools like train_kmeans or train_linear_regression. However, it doesn't specify what type of data it works with (e.g., tabular data) or the exact nature of the output beyond returning a model_id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for training a classification model, suggesting it should be used when a binary or multi-class classification task is needed, as opposed to regression or clustering tools like train_linear_regression or train_kmeans. However, it lacks explicit guidance on when not to use it or alternatives for similar tasks.
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. It mentions the return values (train_data_id and test_data_id) but doesn't disclose critical behavioral traits: whether this is a destructive operation (e.g., modifies original data), authentication needs, rate limits, or error conditions. For a tool with 5 parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: two sentences with zero waste. The first sentence states the core purpose, and the second explains the output's utility. Every word earns its place, 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 the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain parameter meanings, behavioral details (e.g., randomness, session handling), or output structure beyond IDs. For a data-splitting tool in a machine learning context, more guidance on usage, assumptions, and limitations is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 undocumented parameters. It adds no information about parameters like data_id, target_column, test_ratio, session_id, or random_state. The mention of 'split dataset' implies data_id and possibly test_ratio, but doesn't explain their semantics, formats, or constraints beyond what the schema titles provide.
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: 'Split dataset into train and test.' It specifies the action (split) and resource (dataset), distinguishing it from siblings like load_data or train_* tools. However, it doesn't explicitly differentiate from run_analytics or other data processing tools beyond the split operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance by mentioning the output is 'for use in train_* and evaluate_* tools,' suggesting this is a preprocessing step. However, it lacks explicit when-to-use rules, alternatives (e.g., cross-validation), or exclusions (e.g., when data is too small). The context is clear but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 'Updates the dataset in place' (implying mutation) and 'returns data_id and row count' (output behavior), which covers basic operational traits. However, it lacks details on permissions, error handling, or side effects (e.g., whether the original data is recoverable).
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 highly concise and front-loaded: two sentences that efficiently convey purpose and behavior without waste. Every phrase ('Clean dataset', 'optionally drop NA rows', 'z-score normalize', 'Updates in place', 'returns data_id and row count') earns its place by adding critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the core operation and output but lacks details on parameter interactions, error cases, or integration with sibling tools. Without annotations or output schema, more context on behavioral constraints would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 mentions 'optionally drop NA rows' (mapping to 'drop_na') and 'z-score normalize numeric columns' (mapping to 'normalize'), adding meaning for two parameters. However, it doesn't explain 'data_id' or 'session_id', leaving half the parameters undocumented. The value added is partial.
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: 'Clean dataset: optionally drop NA rows and z-score normalize numeric columns.' It specifies the verb ('clean') and resource ('dataset') with concrete operations. However, it doesn't explicitly differentiate from sibling tools like 'load_data' or 'run_analytics' that might also manipulate datasets.
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 mentions optional parameters but doesn't indicate scenarios where cleaning is needed, prerequisites, or how it relates to sibling tools like data loading or evaluation tools. The agent must infer usage from the purpose alone.
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. It mentions the server picks pipelines and returns either charts or ML metrics, which is useful. However, it doesn't address critical behavioral aspects: whether this is a read-only or mutating operation, potential data size limits, execution time expectations, error handling, or authentication requirements. For a complex analytics tool with 12 parameters, this 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey the core functionality. The first sentence establishes the high-level nature with clear examples, and the second explains the server's role and possible outputs. There's no wasted text, though it could benefit from slightly more structure given the tool's complexity.
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 complex analytics tool with 12 parameters, no annotations, no output schema, and many sibling tools, the description is insufficiently complete. It doesn't explain the relationship with sibling tools, doesn't clarify what types of analysis are supported beyond the examples, doesn't address data format requirements beyond mentioning CSV/JSON, and provides minimal guidance on parameter usage. The agent would struggle to use this tool effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 12 parameters, the description must compensate but provides minimal parameter guidance. It mentions 'intent' and 'data_source' through examples but doesn't explain the purpose of the other 10 parameters like 'drop_na', 'normalize', 'test_ratio', or column-specific parameters. The description doesn't clarify how parameters interact or which are required for different analysis types.
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: it's a high-level analytics tool that takes natural language intent and data to perform analysis, with the server selecting appropriate pipelines. It distinguishes from siblings by being a general-purpose entry point rather than specific operations like 'plot_scatter' or 'train_linear_regression'. However, it doesn't explicitly contrast with all siblings like 'clean_data' or 'load_data'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for high-level analytics requests where you describe what you want in natural language. It implies this is the entry point for analysis tasks, with siblings being more specific implementations. However, it doesn't explicitly state when NOT to use it (e.g., for simple plotting or data loading) or name specific alternatives among the many siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns a 'data_id and schema summary' and that this ID is used in subsequent tools, which adds useful context about output and workflow. However, it lacks details on behavioral traits like error handling, rate limits, authentication needs, or what happens with invalid data, leaving gaps for a tool with mutation implications (data ingestion).
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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds crucial usage context. Every sentence earns its place with no wasted words, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 4 parameters with 0% schema coverage, the description is incomplete. It covers purpose and basic usage but lacks details on parameters, error cases, or behavioral nuances. For a data ingestion tool with potential complexity, this leaves significant gaps, though it meets a minimum viable level by explaining the workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 mentions 'CSV or JSON (inline string or URL)' which partially explains the 'source' parameter, but doesn't address 'format', 'session_id', or 'delimiter'. With 4 parameters and low coverage, the description adds minimal meaning beyond the schema, failing to fully clarify parameter roles or usage.
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: 'Ingest data from CSV or JSON (inline string or URL).' It specifies the verb ('ingest') and resource ('data'), and mentions the input formats. However, it doesn't explicitly differentiate from siblings like 'run_analytics' or 'train_test_split' which might also involve data handling, though the focus on ingestion is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Use the returned data_id in subsequent tools (clean_data, plot_*, train_*, etc.).' This indicates this tool is a prerequisite for other operations, giving good guidance on when to use it. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings, such as if other tools might handle data loading differently.
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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