VisiData MCP Server

by moeloubani

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.7
Disambiguation3/5
There is significant functional overlap between tools, particularly among the analysis and visualization tools. For example, 'analyze_data', 'analyze_salary_by_location_and_skills', 'analyze_skills_by_location', 'get_column_stats', and 'create_graph' all perform data analysis with unclear boundaries. However, the descriptions help differentiate some specific use cases like salary analysis or skills parsing.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., 'analyze_data', 'convert_data', 'filter_data', 'sort_data', 'load_data'). There are minor deviations with tools like 'get_column_stats' and 'get_data_sample' using 'get_' prefix instead of action verbs, and 'parse_skills_column' uses 'parse_' rather than 'analyze_' or 'process_', but overall the naming is readable and predictable.
Tool Count4/5
With 15 tools, the count is slightly high but reasonable for a data analysis server covering loading, conversion, filtering, sorting, analysis, and visualization. It's borderline heavy but each tool appears to serve a specific function in the data workflow, though some redundancy exists.
Completeness4/5
The toolset provides comprehensive coverage for data analysis workflows including loading, conversion, filtering, sorting, sampling, statistical analysis, and visualization. Minor gaps exist such as no explicit data cleaning or transformation tools beyond parsing skills, and no update/delete operations for datasets, but agents can work around these with the available tools.
Average 3/5 across 15 of 15 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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": [
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  ]
}
```
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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

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 that the tool 'Perform[s] basic analysis' and returns 'Analysis results including statistics and data types', but fails to specify what 'basic analysis' includes (e.g., summary statistics, data profiling), any constraints (e.g., file size limits, supported formats), or side effects. 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 appropriately concise and structured with clear sections for 'Args' and 'Returns'. It uses three sentences efficiently, though the first sentence 'Perform basic analysis on a dataset' could be more specific. There is no redundant information, and the structure aids readability, but it lacks depth that might justify additional content.
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 complexity (basic analysis with one parameter), no annotations, and the presence of an output schema (which handles return values), the description is minimally complete. However, it fails to address key contextual aspects like what 'basic analysis' entails compared to siblings, supported data formats, or limitations. The output schema mitigates some gaps, but the description remains inadequate for fully informed tool selection.
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 description adds minimal semantics beyond the input schema. It states 'file_path: Path to the data file', which echoes the schema's title 'File Path' without providing additional context (e.g., supported file formats, path requirements). With 0% schema description coverage and only one parameter, the baseline is 4, but the description does little to compensate, offering only basic clarification rather than meaningful elaboration.
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 the tool 'Perform[s] basic analysis on a dataset', which provides a vague but understandable purpose. It specifies the verb ('analyze') and resource ('dataset'), but lacks specificity about what 'basic analysis' entails compared to more specialized sibling tools like 'get_column_stats' or 'create_distribution_plots'. The purpose is not tautological but remains broad without distinguishing from 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?
The description provides no guidance on when to use this tool versus its many siblings. With tools like 'get_column_stats', 'create_distribution_plots', 'filter_data', and 'load_data' available, there is no indication of what makes 'analyze_data' distinct or when it should be preferred over more specific analysis tools. The description implies usage but offers 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?
With no annotations provided, the description carries the full burden but only states basic functionality without disclosing behavioral traits like file format support, error handling, performance, or whether it modifies original files. It mentions optional output saving but lacks details on defaults or 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 appropriately sized and front-loaded with the core purpose, followed by a structured Args and Returns section. Sentences are efficient, though the parameter explanations could be more detailed without sacrificing brevity.
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 5 parameters with 0% schema coverage and no annotations, the description partially compensates by listing parameters and mentioning an output schema exists, but it lacks details on data formats, error cases, and integration with siblings. It's minimally adequate but has clear gaps for a data filtering 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 description coverage is 0%, so the description must compensate. It lists parameters with brief explanations (e.g., 'Column name to filter on'), adding some meaning beyond schema titles, but doesn't specify formats (e.g., file types for 'file_path', data types for 'value') or constraints, leaving gaps in 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 states 'Filter data based on a condition' which is a clear verb+resource but remains vague about the data format (e.g., CSV, JSON) and lacks differentiation from sibling tools like 'sort_data' or 'get_data_sample'. It specifies the action but not the context or uniqueness.
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 such as 'sort_data' or 'get_data_sample' for data manipulation, nor prerequisites like data loading. The description implies filtering but doesn't specify scenarios or exclusions, 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 full burden. It mentions 'analyze' and returns 'salary analysis,' implying a read-only operation, but doesn't disclose behavioral traits like whether it modifies files, requires specific data formats, has performance considerations, or what 'analysis' entails (e.g., statistical summaries, visualizations). The mention of 'output_path' suggests file creation, but this isn't explicitly stated.
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 sized and well-structured: a clear purpose statement followed by 'Args' and 'Returns' sections. Every sentence earns its place, with no redundant information. It could be slightly more front-loaded by integrating parameter hints into the main description, but overall it's efficient.
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 5 parameters with 0% schema coverage and no annotations, the description provides basic parameter semantics and mentions an output path, but lacks details on data processing behavior, error conditions, or analysis methodology. The presence of an output schema (implied by 'Has output schema: true') reduces the need to explain return values, but overall completeness is moderate for a data analysis tool with multiple 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 0%, so the description must compensate. It lists all 5 parameters in the 'Args' section with brief explanations (e.g., 'Path to the data file'), adding basic semantics beyond schema titles. However, it doesn't provide details like expected file formats, column data types, or how 'comma-separated skills' are processed, leaving gaps in parameter understanding.
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: 'Analyze salary statistics by location and skills combination.' It specifies the verb 'analyze' and the resource 'salary statistics' with dimensions 'location and skills.' However, it doesn't explicitly differentiate from siblings like 'analyze_skills_by_location' or 'create_skills_location_heatmap,' which appear related.
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 multiple sibling tools for analysis (e.g., 'analyze_data,' 'analyze_skills_by_location'), there's no indication of when this specific salary-focused analysis is preferred or what prerequisites exist (e.g., data format requirements).
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 analysis and optional saving of results, but doesn't describe what the analysis actually produces (beyond the vague 'Skills analysis by location'), whether it modifies input data, what formats are supported, error conditions, or performance characteristics. For a data analysis tool with 4 parameters, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with purpose statement, parameter documentation, and return value indication. It's appropriately sized for a tool with 4 parameters. The 'Args' and 'Returns' sections are clear, though the return description is vague. Every sentence serves a purpose with minimal redundancy.
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 has an output schema (which should document return values), the description doesn't need to detail return structure. However, with no annotations and 4 parameters, the description should provide more behavioral context about what the analysis entails, supported data formats, and how it differs from sibling tools. The current description is minimally adequate but leaves significant gaps in understanding the tool's behavior.
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 description includes an 'Args' section that documents all 4 parameters with brief explanations. However, with 0% schema description coverage, the description doesn't provide format details (e.g., file formats supported, column name conventions), validation rules, or examples. It compensates somewhat by listing parameters but lacks depth needed for full understanding.
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: 'Analyze skills frequency and distribution by location.' This specifies the verb ('analyze'), resource ('skills'), and dimension ('by location'). It distinguishes from siblings like 'analyze_salary_by_location_and_skills' by focusing solely on skills analysis, but doesn't explicitly contrast with 'create_skills_location_heatmap' which might produce visual output.
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 'analyze_data', 'create_skills_location_heatmap', and 'parse_skills_column', there's no indication of when this specific analysis tool is preferred. The description only states what it does, not when it should be selected.
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 tool uses pandas and describes basic input/output paths, but lacks critical behavioral details: it doesn't specify what formats are supported (beyond vague examples), whether the conversion is destructive to the original file, error handling behavior, or performance characteristics. The description is too minimal for a tool that performs file operations.
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 sized and well-structured with clear sections (purpose, Args, Returns). Each sentence serves a purpose, though the 'Returns' section could be more specific. The front-loaded purpose statement is clear, and there's no unnecessary verbiage, making it efficient for an agent 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 the tool's complexity (file format conversion with 3 parameters), no annotations, and an output schema present (which handles return values), the description is moderately complete. It covers the basic purpose and parameters but lacks important context: supported format details, error conditions, pandas dependency requirements, and how it differs from sibling data tools. The output schema reduces the need to explain returns, but other gaps remain significant.
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 description provides parameter information in the Args section, listing all three parameters with brief explanations. However, with 0% schema description coverage, the description doesn't fully compensate: it doesn't explain parameter constraints (e.g., what file paths are valid, what specific formats are supported beyond 'csv, json, xlsx, etc.'), or that output_format is optional with a default of null. The parameter explanations are present but insufficient given the complete lack of schema documentation.
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: 'Convert data from one format to another using pandas.' This specifies the verb (convert), resource (data), and implementation method (pandas). However, it doesn't explicitly differentiate from sibling tools like 'load_data' or 'filter_data' that might also handle data files, so it doesn't reach the highest level of sibling differentiation.
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. There's no mention of prerequisites (e.g., needing pandas installed), when not to use it (e.g., for non-file data), or comparison to sibling tools like 'get_supported_formats' that might help determine compatible formats. The only implicit usage context is format conversion, but no explicit alternatives are named.
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 creating and saving plots but lacks critical behavioral details: permission requirements for file paths, whether it overwrites existing files, performance considerations for large datasets, or error handling. The description is minimal and doesn't compensate for the absence of 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?
The description is well-structured and appropriately sized, with a clear purpose statement followed by parameter and return sections. Each sentence adds value, though the parameter explanations could be more detailed. There's no unnecessary repetition or fluff, making it efficient for an agent 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, 0% schema coverage, and an output schema present, the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context (e.g., file handling, errors) and doesn't leverage the output schema to explain return values. For a tool with 4 parameters and file operations, more detail is needed for full adequacy.
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 lists all four parameters with brief explanations (e.g., 'Optional list of specific columns to plot'), adding some meaning beyond the schema's titles. However, it doesn't detail parameter constraints (e.g., file formats for 'file_path', valid 'plot_type' options beyond those listed), leaving gaps in documentation.
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: 'Create distribution plots for numeric columns.' It specifies the verb ('create'), resource ('distribution plots'), and target ('numeric columns'). However, it doesn't explicitly differentiate from sibling tools like 'create_correlation_heatmap' or 'create_graph' that also create visualizations, 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. It doesn't mention sibling tools like 'create_correlation_heatmap' for relationships or 'get_column_stats' for statistical summaries, nor does it specify prerequisites (e.g., requiring numeric data). Usage is implied but not explicitly defined.
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 that the tool 'Get statistics' and returns 'Column statistics in JSON format,' but it doesn't cover critical aspects like whether it's read-only, if it modifies data, error handling, performance implications, or rate limits. 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 well-structured and concise, using a clear purpose statement followed by 'Args' and 'Returns' sections. Each sentence serves a purpose without unnecessary elaboration, making it easy to parse. However, the 'Args' and 'Returns' labels are slightly redundant with the schema, but overall it's efficient.
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 that there is an output schema (implied by 'Has output schema: true'), the description doesn't need to detail return values. However, with no annotations and low schema coverage, it lacks context on behavioral traits and parameter nuances. For a simple tool with two parameters, it's minimally adequate but could benefit from more guidance on usage and behavior.
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 description lists the parameters ('file_path' and 'column') and their roles, but with 0% schema description coverage, it doesn't fully compensate by providing details like expected formats, constraints, or examples. It adds basic meaning beyond the schema's property names, but falls short of fully explaining the parameters, resulting in a baseline score.
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: 'Get statistics for a specific column.' This specifies the verb ('Get statistics') and resource ('a specific column'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'analyze_data' or 'create_distribution_plots' that might also involve statistical analysis, so it doesn't reach the highest 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, context, or comparisons to sibling tools such as 'analyze_data' or 'get_data_sample', leaving the agent to infer usage based on the name alone. This lack of explicit direction reduces its effectiveness in tool selection.
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 loads data and returns a string representation, but lacks details on permissions, error handling, rate limits, or what happens if the file is invalid. For a tool with no annotations, this is insufficient, leaving the agent uncertain about operational risks.
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 sized and front-loaded: the first sentence states the purpose clearly, followed by structured 'Args' and 'Returns' sections. There's no wasted text, and the formatting aids readability. It could be slightly more concise by integrating the sections into a single paragraph, but it's efficient overall.
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 has an output schema (implied by 'Has output schema: true'), the description doesn't need to detail return values. However, with no annotations, 2 parameters, and 0% schema coverage, it should provide more context on usage and behavior. The description covers basics but leaves gaps in guidelines and transparency, making it minimally adequate but incomplete.
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 adds meaning by explaining 'file_path' as 'Path to the data file' and 'file_type' as 'Optional file type hint (csv, json, xlsx, etc.)', which clarifies beyond the schema's basic types. However, it doesn't cover all nuances (e.g., file path formats, default behaviors for null file_type), so it only partially addresses the coverage gap.
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: 'Load data from a file using VisiData.' It specifies the verb ('Load') and resource ('data from a file'), and mentions the tool/context ('using VisiData'). However, it doesn't explicitly differentiate from siblings like 'convert_data' or 'get_data_sample', which might also involve file operations, so it doesn't reach the highest 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 siblings like 'convert_data' (which might transform data) or 'get_data_sample' (which might retrieve subsets), nor does it specify prerequisites or exclusions. The only implied usage is loading data from files, but this is too vague for effective tool selection.
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 that the tool creates and saves a heatmap image, implying a write operation, but doesn't disclose critical behaviors like file format requirements, error handling, performance characteristics, or whether it modifies the original dataset. For a tool that writes files and processes data, this lack of transparency 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 well-structured and front-loaded with the core purpose, followed by parameter explanations in a clear 'Args' and 'Returns' format. Every sentence earns its place: the first states the action, and the subsequent lines efficiently clarify inputs and outputs without redundancy. It's appropriately sized for the tool's complexity.
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 (3 parameters, file I/O, data processing), no annotations, and an output schema present, the description is partially complete. It covers the basic what and how but misses context like error conditions, performance limits, or dependencies on other tools (e.g., 'load_data'). The output schema handles return values, so the description doesn't need to detail them, but overall it leaves gaps for safe and 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 provides basic semantics for all three parameters: 'file_path' as the data source, 'output_path' as the save location, and 'columns' as an optional filter. However, it lacks details like supported file formats, path requirements, or column selection rules. The description adds value beyond the bare schema but doesn't fully bridge the coverage gap, warranting a baseline 3.
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: 'Create a correlation heatmap from numeric columns in the dataset.' It specifies the verb ('create') and resource ('correlation heatmap'), and distinguishes it from siblings like 'create_distribution_plots' or 'create_skills_location_heatmap' by focusing on correlation analysis. However, it doesn't explicitly differentiate from all possible alternatives, keeping it at a 4 rather than a 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 when to choose this over other visualization tools like 'create_distribution_plots' or 'create_skills_location_heatmap', nor does it specify prerequisites (e.g., needing numeric data). The agent must infer usage from the purpose alone, which is insufficient for clear decision-making.
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 tool creates and saves a heatmap image, which implies a write operation, but doesn't address permissions, file format requirements, error conditions, or what happens if the output path already exists. The return statement is vague about what information is provided.
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 a clear purpose statement followed by organized Args and Returns sections. It's appropriately sized for a 6-parameter tool, though the Returns statement could be more specific. Every sentence serves a purpose.
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?
For a data visualization tool with 6 parameters and no annotations, the description covers the basic purpose and parameters adequately. However, it lacks important context about input file formats, heatmap characteristics, error handling, and how it differs from similar sibling tools. The existence of an output schema helps with return values, but behavioral aspects remain underspecified.
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?
With 0% schema description coverage, the description compensates well by explaining all 6 parameters in the Args section. Each parameter gets a clear explanation of its purpose, including defaults for optional parameters. This adds significant value beyond what the bare schema provides.
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 showing skills distribution across locations, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_correlation_heatmap' or 'analyze_skills_by_location', which might have overlapping functionality.
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 'create_correlation_heatmap' or 'analyze_skills_by_location'. There's no mention of prerequisites, input data requirements, or comparison with sibling tools that might handle similar analyses.
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 the return format ('JSON format') but lacks details on permissions, file format support, error handling, or whether the operation is read-only or has side effects. For a tool with no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a structured breakdown of args and returns. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
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 (2 parameters, no annotations, but has an output schema), the description is minimally adequate. It covers the basic purpose and parameters but lacks usage context and behavioral details. The output schema likely handles return values, so the description's mention of 'JSON format' is sufficient but not comprehensive.
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 description coverage is 0%, so the description must compensate. It adds meaningful context: 'file_path: Path to the data file' clarifies the parameter's purpose, and 'rows: Number of rows to return (default: 10)' explains the default behavior. This goes beyond the schema's basic titles, though it doesn't cover constraints like valid file paths or row limits.
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: 'Get a sample of data from a file.' It specifies the verb ('Get') and resource ('data from a file'), making the action explicit. However, it doesn't differentiate from siblings like 'load_data' or 'filter_data' that might also involve data retrieval, 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 siblings such as 'load_data' (likely for full loading) and 'filter_data' (likely for filtering), there's no indication of scenarios where sampling is preferred over these other operations, leaving the agent without context for selection.
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. While it mentions parsing and one-hot encoding, it doesn't describe what happens to the original data (is it modified or copied?), file format requirements, error handling, or performance characteristics. The description is functional but lacks operational context needed for safe use.
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 efficiently structured: a clear purpose statement followed by parameter explanations. Every sentence adds value, with no redundant information. The Args/Returns sections are appropriately formatted and contribute to understanding without verbosity.
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 complexity (data parsing and transformation), no annotations, and an output schema that presumably documents return values, the description is minimally adequate. It covers the core operation and parameters but lacks important context about file formats, data validation, error conditions, and how the one-hot encoding is structured in output.
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?
With 0% schema description coverage, the description must fully compensate. It provides clear semantic meaning for all three parameters: 'file_path' (path to data file), 'skills_column' (column containing skills), and 'output_path' (optional save location). This adds substantial value beyond the bare schema, though it doesn't specify file format expectations or column naming conventions.
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: 'Parse comma-separated skills into individual skills and create one-hot encoding.' This specifies both the parsing action and the transformation (one-hot encoding). However, it doesn't explicitly differentiate from sibling tools like 'analyze_skills_by_location' or 'create_skills_location_heatmap' that might also process skills 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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'analyze_skills_by_location', 'filter_data', and 'convert_data' that might handle similar data, there's no indication of when this specific parsing/encoding operation is appropriate versus other data manipulation 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 for behavioral disclosure. The description mentions that it 'returns information about the sorted data' but doesn't specify what that information includes (e.g., success/failure, row count, file path). It also doesn't mention potential side effects like file creation when output_path is provided, error conditions, or performance characteristics. For a tool with 4 parameters and no annotations, 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 well-structured and appropriately sized. It begins with a clear purpose statement, then provides an organized Args section with bullet-like formatting, and concludes with a Returns statement. Every sentence earns its place, with no redundant information. The formatting with clear sections makes it 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 4 parameters with 0% schema coverage and no annotations, the description does a reasonable job explaining parameters but lacks important context. There's an output schema (indicated by 'Has output schema: true'), so the description doesn't need to detail return values. However, for a data manipulation tool with siblings, it should provide more guidance on usage context and behavioral expectations. The parameter explanations are good, but overall completeness is only adequate.
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 description coverage is 0%, so the description must compensate. The Args section provides clear explanations for all 4 parameters: file_path, column, descending, and output_path. Each parameter's purpose is explained, including the default for 'descending' and optional nature of 'output_path'. This adds significant value beyond the bare schema, though it doesn't specify file format requirements or column validation rules.
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: 'Sort data by a specific column.' This specifies the verb ('sort') and resource ('data'), but it doesn't distinguish this tool from sibling tools like 'filter_data' or 'analyze_data' beyond the sorting action. The title is null, so the description carries the full burden of purpose definition.
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 sibling tools like 'filter_data', 'analyze_data', and 'convert_data', there's no indication of when sorting is appropriate versus other data manipulation operations. The description only states what the tool does, not when it should be selected.
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. While it mentions the tool creates and saves a graph image, it doesn't disclose important behavioral aspects like file format requirements, error conditions, whether it overwrites existing files, or what 'Information about the created graph' actually means in the return value.
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 clear sections (description, Args, Returns) and efficiently communicates the core functionality. Every sentence serves a purpose, though the main description could be slightly more detailed about the tool's scope and limitations.
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?
For a 6-parameter tool with no annotations, the description provides good parameter documentation but lacks behavioral context. The existence of an output schema helps with return values, but the description should better explain what the tool actually does beyond parameter mapping, especially given the complex data visualization domain.
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?
With 0% schema description coverage, the description compensates well by explaining all 6 parameters in the Args section, including data types (numeric columns), optional status, and purpose. It provides meaningful context beyond just parameter names, though it could elaborate more on format requirements for file paths.
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 creates a graph/plot from data using matplotlib/seaborn, specifying both the action (create) and resource (graph/plot). It distinguishes itself from sibling tools like create_correlation_heatmap and create_distribution_plots by being a general-purpose graph creation tool rather than specialized visualizations.
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 create_correlation_heatmap or create_distribution_plots. It doesn't mention prerequisites (e.g., needing data loaded first) or when other tools might be more appropriate for specific visualization needs.
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?
No annotations are provided, so the description carries the full burden. It discloses the return type ('List of supported formats and their descriptions'), which is helpful, but lacks details on behavioral traits like rate limits, error handling, or whether it's a static list. It doesn't contradict annotations, but could be more informative for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured: two sentences that directly state the action and return value, with no wasted words. It's front-loaded with the core purpose, 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations) and the presence of an output schema, the description is reasonably complete. It explains what the tool does and what it returns, which is sufficient for a simple lookup tool. However, it could benefit from more context on usage relative to siblings, slightly reducing completeness.
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?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, which is fine here, but it doesn't compensate for any gaps since there are none. A baseline of 4 is appropriate as it's complete for a parameterless tool.
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: 'Get a list of supported file formats in VisiData.' It specifies the verb ('Get') and resource ('supported file formats'), making the action unambiguous. However, it doesn't explicitly differentiate from siblings like 'load_data' or 'convert_data', which might also involve formats, so it misses full sibling distinction.
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, context, or exclusions, such as whether it's for reference before loading data or for validation. With siblings like 'load_data' and 'convert_data' that involve formats, this lack of differentiation leaves usage unclear.
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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