eda-mcp

by MLMecham

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: loading data, per-column statistics, bulk statistics, diagnostic plots, correlations, and full report generation. Descriptions further clarify when to use each, with references between tools to avoid confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern: load_dataset, get_column_summary, get_all_summaries, get_diagnostic_plot, get_correlations, generate_report. No mixing of styles or vague verbs.
Tool Count5/5
With 6 tools, the server is well-scoped for its EDA purpose. Each tool serves a necessary function without redundancy, and the count is appropriate for a focused toolkit.
Completeness4/5
The tool surface covers the core EDA workflow: loading, single-column summaries and plots, multi-column summaries, correlations, and a comprehensive report. Minor gaps like bivariate categorical analysis or data profiling exist, but the set is reasonably complete for exploratory analysis.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
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- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It hints at performance implications for large datasets but does not explicitly state non-destructiveness, error handling, or return behavior constraints.
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?
Three succinct sentences, front-loaded with purpose, no redundant or vague language.
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 output schema and no annotations, the description is adequate but missing details like return format, supported file types, and what happens when table is null.
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%, and the description does not explain file_path or table parameters beyond context of 'dataset' and 'columns'. The table parameter remains mostly undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it returns summary statistics for every column, using clear verb+resource 'Return summary statistics'. It also distinguishes from sibling get_column_summary by noting it's equivalent to calling that once per column.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (complete overview) and when not to (large datasets with many columns), and names alternative get_column_summary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description thoroughly discloses behavior: auto-detects column type and lists all computed statistics for continuous, categorical, binary, temporal, and high_cardinality columns.
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 structured with bullet points for clarity and front-loaded with the main action. However, it is somewhat verbose in listing all statistics, which could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of column types, the description comprehensively covers return values. It provides usage context relative to load_dataset but lacks details on error cases or edge conditions.
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%, and the description does not explain parameters (file_path, column, table) in detail. The table parameter is not mentioned at all, leaving ambiguity about its role.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Return full summary statistics for a single column' and details the specific statistics for each type, distinguishing it from siblings like 'get_all_summaries' and 'load_dataset'.
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?
Explicitly says to use 'after calling load_dataset to identify columns of interest', providing context for when to use this tool. However, it does not explicitly contrast with 'get_all_summaries' or mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behaviors: auto-selects plot type per column type, handles high_cardinality by returning a message, saves to output_dir, returns file path. This is comprehensive and leaves no ambiguity about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with front-loaded purpose and organized bullet list for plot types. It is detailed but not overly verbose; each section serves a purpose. Minor redundancy in listing all plot types could be condensed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple column types, automatic selection) and lack of output schema, the description covers all essential behavior: input handling, output path, return value, and special cases (high_cardinality). It is sufficient for an agent to correctly invoke the 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?
Schema coverage is 0%, but the description adds value for output_dir by explaining its purpose. However, file_path, column, and table are not described beyond schema names. The description partially compensates but leaves gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates and saves a diagnostic plot as PNG, and details automatic plot type selection per column classification. This distinguishes it from siblings like get_column_summary or get_correlations which serve different purposes.
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 the tool is for diagnostic visualization but does not explicitly state when to use it versus siblings like get_column_summary or get_all_summaries. No guidance on when not to use it or prerequisites is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses supported file formats, return structure (column names, dtypes, classifications, missing values), and SQLite auto-load behavior. It implies a read-only operation but doesn't explicitly state 'read-only'. Still thorough given no annotations.
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?
Description is well-structured: starts with purpose, then output details, then supported formats and special behavior. At ~150 words, it is concise but not overly brief. Every sentence adds value, though some repetition could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool supports multiple formats and returns structural metadata. Description covers supported formats, column classifications, missing values, and SQLite table handling. It lacks details on file path resolution or permissions, but for an initial loading tool, it is sufficiently complete. No output schema, so description serves as return documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0% description coverage; description adds meaning by explaining file_path as the dataset path and table as optional SQLite table name with default behavior. This compensates well for the schema's lack of descriptions, though file_path could be more specific (e.g., local vs remote).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Load a dataset and return a structural overview', with specific verb 'load' and resource 'dataset'. It distinguishes from sibling tools (e.g., generate_report, get_all_summaries) by explicitly saying 'Call this first when exploring an unfamiliar dataset'.
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?
Description explicitly advises 'Call this first when exploring an unfamiliar dataset', implying when to use. It also explains behavior for SQLite files when table is omitted. However, it does not explicitly state when not to use it or provide alternatives beyond the implicit ordering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It details the report's contents (overview, flags, per-column summaries) and side effects (saves markdown and PNGs). It does not mention resource usage or error conditions but is otherwise transparent for a read-only operation.
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 well-structured with a single paragraph and bullet points. Every sentence is informative, front-loads the main purpose, and includes alternatives at the end with no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 params, no output schema), the description covers the return value (file path), report contents, and side effects. It lacks explanation for the 'table' parameter and error conditions but is otherwise sufficient for 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 0%, so the description must compensate. It explains file_path and output_dir implicitly but fails to describe the 'table' parameter. This partial coverage leaves meaning gaps, though the described parameters add value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a complete EDA markdown report for the entire dataset, specifying verb and resource. It distinguishes from sibling tools by noting that for single-column inspection, one should use get_column_summary or get_diagnostic_plot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly positions this as the main tool for thorough end-to-end analysis and provides clear alternatives for quick inspection of a single column, giving both when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully discloses behavioral traits: returns both Pearson and Spearman matrices, strongest pairs (max 10), flags for high correlation, generated plot paths, scatter plot cap (10 pairs), and automated column type exclusions.
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?
Description is moderately sized with several sentences, but each adds necessary detail. Well-structured: first paragraph overview, second paragraph return values, third paragraph exclusions, fourth paragraph parameter guidance. Could be slightly more concise, but no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all key aspects: what tool computes, parameters, returns, exclusions, and parameter behavior. With 4 params, 0% schema coverage, and no output schema, it provides sufficient information for an agent to use effectively. Minor gaps in file_path format, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, so description must explain parameters. It clarifies threshold behavior with examples and mentions file_path (implied input), output_dir, and table. Does not detail file_path format or output_dir structure, but adds significant value over bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it computes pairwise correlations and generates Spearman heatmap and scatter plots. It distinguishes from sibling tools (generate_report, get_all_summaries, etc.) which serve different purposes.
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?
Provides clear context on when to use (numeric columns) and exclusions (categorical, binary, etc.). Also gives practical advice on threshold adjustment. Lacks explicit when-not-to-use, but overall strong guidance.
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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