MCP ASCII Charts

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting a specific chart type: bar charts, histograms, line charts, scatter plots, and sparklines. The descriptions explicitly differentiate their use cases (e.g., 'frequency distribution' for histograms vs. 'temporal data visualization' for line charts), leaving no ambiguity for an agent to misselect.
Naming Consistency5/5
All tool names follow a perfectly consistent 'create_*_chart/plot/line' pattern with snake_case, using 'create' as the verb and descriptive nouns (e.g., bar_chart, histogram, line_chart). This uniformity makes the set predictable and easy to understand at a glance.
Tool Count5/5
With 5 tools, the server is well-scoped for generating ASCII charts, covering common chart types without bloat. Each tool earns its place by addressing a distinct visualization need, making the count appropriate for the domain's typical use cases.
Completeness5/5
The tool set provides complete coverage for ASCII chart generation, including bar charts, histograms, line charts, scatter plots, and sparklines. This covers the core chart types needed for data visualization, with no obvious gaps that would hinder an agent's ability to create relevant charts.
Average 3.1/5 across 5 of 5 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
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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

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 states the tool creates ASCII histograms but doesn't mention output format details (e.g., text-based representation), potential side effects, or performance considerations. For a tool with no annotations, this leaves significant gaps in understanding how it 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 and front-loaded, consisting of a single, clear sentence: 'Create ASCII histograms for frequency distribution.' There's no wasted text, and it immediately conveys the core purpose without unnecessary elaboration, making it efficient 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 the complexity (6 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output format (critical for ASCII-based tools), behavioral traits, and usage context. While the schema covers parameters well, the overall tool understanding is insufficient for effective agent use without additional behavioral or output information.
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 doesn't add any parameter-specific information beyond what's already in the input schema, which has 100% coverage with clear descriptions for all parameters (e.g., data array, optional title, width/height defaults). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate or add extra context.
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 ASCII histograms for frequency distribution.' It specifies the verb ('create'), resource ('ASCII histograms'), and function ('for frequency distribution'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like create_bar_chart or create_scatter_plot, which prevents a score of 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 sibling tools or contexts where a histogram is preferred over other chart types (e.g., bar charts for categorical data). Without any usage instructions, the agent lacks direction on tool selection, though it's not misleading.
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 states what the tool does ('Generate ASCII line charts') but doesn't describe important behavioral aspects like output format (ASCII text), whether it's read-only or has side effects, error handling, or performance characteristics. 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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every word earns its place in this concise formulation.
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 tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like (ASCII format), doesn't mention behavioral constraints, and provides minimal context about when to use it. The description should do more to compensate for the lack of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Generate') and resource ('ASCII line charts'), and specifies the use case ('for temporal data visualization'). However, it doesn't explicitly differentiate from sibling tools like create_bar_chart or create_scatter_plot, which would require 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 its siblings (create_bar_chart, create_histogram, create_scatter_plot, create_sparkline). It mentions 'temporal data visualization' which implies time-series data, but doesn't explicitly state when to choose line charts over other chart types or 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.
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. While it mentions the chart type (ASCII bar charts) and orientation options, it doesn't describe output format (e.g., text string), error handling, performance characteristics, or any constraints beyond what's in the schema. For a tool with 7 parameters and no annotations, 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 a single, efficient sentence: 'Create horizontal or vertical ASCII bar charts.' It's front-loaded with the core purpose, uses no unnecessary words, and every element (creation action, orientation options, chart type) earns its place.
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 (7 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context about output format, error conditions, or when to use versus siblings. With no annotations to cover behavioral aspects, the description should provide more completeness for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't explain data formatting, label alignment, or color usage). The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create horizontal or vertical ASCII bar charts.' It specifies the verb ('create'), resource ('ASCII bar charts'), and distinguishes chart types (horizontal/vertical). However, it doesn't explicitly differentiate from sibling tools like create_histogram or create_line_chart, 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 its siblings (create_histogram, create_line_chart, create_scatter_plot, create_sparkline). It doesn't mention use cases, prerequisites, or alternatives. The only implied usage is for creating bar charts, but no context for choosing this over other chart types.
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 generates ASCII scatter plots, implying a read-only output operation, but doesn't cover critical behaviors: whether it requires specific inputs beyond the schema, how errors are handled, if there are rate limits, or what the output format looks like (e.g., text-based plot). For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 extremely concise and front-loaded: 'Generate ASCII scatter plots for correlation analysis' is a single, efficient sentence that immediately conveys the core function. Every word earns its place, with no redundancy or unnecessary elaboration, making it easy for an AI agent to parse quickly.
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 (6 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output format, error handling, or usage context relative to siblings. Without annotations or an output schema, the description should do more to explain behavioral aspects, but it meets a basic threshold by clarifying the tool's purpose and type of visualization.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema (e.g., 'data' as 'Array of y-values (x-values will be indices)'). The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'data' relates to 'correlation analysis' or clarifying 'ANSI color name' options. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.
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: 'Generate ASCII scatter plots for correlation analysis.' It specifies the verb ('generate'), resource ('ASCII scatter plots'), and context ('for correlation analysis'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like create_line_chart or create_bar_chart, which likely also generate ASCII charts for different visualization types.
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 'correlation analysis,' which implies a specific use case, but doesn't clarify when to choose scatter plots over line charts, histograms, or other siblings for similar data analysis tasks. There are no explicit when/when-not statements or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on output format (e.g., text string), error handling, performance characteristics, or any constraints beyond what's implied by 'compact' and 'inline'. 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.
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 purpose without unnecessary words. Every part of the sentence ('Generate compact ASCII sparklines for inline charts') contributes essential information, 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.
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 purpose but lacks details on output (what the sparkline looks like as text), usage guidelines, and behavioral traits. With no output schema, the description should ideally hint at the return format, but it doesn't, leaving gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain data formatting, color options, or width implications). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Generate') and resource ('compact ASCII sparklines for inline charts'), specifying both the output format (ASCII sparklines) and use case (inline charts). It effectively distinguishes from sibling tools like create_bar_chart or create_line_chart by focusing on compact, text-based visualizations rather than full graphical charts.
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_line_chart or create_bar_chart. It mentions 'inline charts' but doesn't explain scenarios where sparklines are preferable over other chart types, nor does it mention any prerequisites or exclusions for usage.
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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