ASCII Table MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: analysis, debugging, creation from various inputs, preview, and validation. Overlap is minimal, and agents can easily select the right tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.
Tool Count5/5
Seven tools is within the ideal range (3-15) and each tool contributes meaningfully to the server's table-focused scope without being excessive.
Completeness5/5
The tool set covers the full lifecycle: creation from raw data, CSV, JSON, preview, analysis, debugging, and validation. No obvious gaps for the intended domain.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 65 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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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 present, so the description carries the full burden. It describes output but does not disclose side effects, read-only behavior, or any constraints. For a diagnostic tool, it likely is read-only, but this is not 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 concise and front-loaded with the purpose. It uses a brief structure with a summary line followed by args and returns. Every sentence contributes, though it could be slightly more organized.
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 3 optional parameters and an output schema (not shown), the description covers the basic behavior and return type. However, it lacks details about input-output relationships, error cases, or differentiation from sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only repeats parameter names and basic types: headers (optional column headers), rows (data rows), data (alias for rows). This adds minimal value beyond the schema, lacking format constraints or usage details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool renders and diagnoses a table, showing exact character positions. It lists output components (plain table, annotated positions, drift comparison). However, it does not explicitly distinguish from sibling tools like analyze_table or make_table, so it is clear but lacks 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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or situations where other tools would be more appropriate.
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?
Without annotations, the description carries the burden. It explains the return format (Markdown code block or raw pipe table) and notes that rows and data are mutually exclusive. However, it does not disclose error behavior (e.g., for invalid rows) or side effects. It is adequate but not exhaustive.
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 a bulleted Args list. It is slightly lengthy but front-loads the key action. Some redundancy (e.g., 'data' alias explanation) could be trimmed for conciseness.
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 7 parameters, no annotations, and an output schema, the description is thorough. It explains the output format, parameter interactions (e.g., mutual exclusivity), and formatting options. The agent can confidently invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates. It details all 7 parameters, including fmt options (grid, box, pipe, safe), style variants, and the auto_format/safe_width flags. This provides significant added meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Render data as a table,' which identifies the tool's core function. However, it does not explicitly contrast with siblings like make_table_from_csv or make_table_from_json, which accept different input formats. This slightly reduces 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 lacks explicit guidance on when to use this tool versus its siblings. It does not mention that this tool is for rendering raw arrays, while siblings handle CSV/JSON input. The agent must infer usage from parameter types.
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 provided, so description carries full burden. Mentions auto-format feature and returns 'Formatted table' but lacks details on error handling, performance, or 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?
Description is well-structured with a clear main purpose, examples, and parameter list. Slightly verbose but still efficient.
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 input formats, parameters, and return value. Lacks error handling details, but overall sufficient given 5 parameters and no annotations.
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 coverage is 0%, but description explains each parameter's purpose with examples (e.g., json_data, has_header, fmt, style, auto_format), adding significant meaning beyond the schema names.
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?
Explicitly states it parses a JSON array and renders as a table, distinguishing it from sibling tools that handle CSV (make_table_from_csv) or other formats.
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?
Describes two acceptable input formats but does not provide guidance on when to use this tool versus alternatives like make_table_from_csv or debug_table.
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?
Describes key behaviors: uses wcwidth for display-width, handles Thai/Pali combining marks, scans '+' markers, validates '|' alignment, and returns an analysis report. No annotations provided, so description carries full burden; it is detailed but could be more explicit about being read-only.
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 structured into paragraphs with args/returns and key implementation details. Each sentence adds value, though it could be slightly more streamlined without losing clarity.
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 simplicity of the tool (single parameter, clear output), the description covers input, behavior, and output adequately. It explains Unicode handling and return structure, though error cases are not mentioned.
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 only parameter 'table_text' is explained as 'A +---+ grid table as raw text', adding meaning beyond the schema's type string. The description compensates for 0% schema coverage effectively.
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 the tool analyzes column positions in a grid table, with specific details like scanning '+' markers and validating '|' alignment. It distinguishes itself from sibling tools by focusing on analysis rather than table creation or preview.
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?
No explicit guidance on when to use this tool versus alternatives like 'validate_table_text' or 'debug_table'. The description implies usage for analyzing grid table formatting but does not provide exclusions or contrasts.
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 are provided, so the description carries full burden. It clearly states the tool prints a preview, indicating no destructive side effects. However, it doesn't explicitly mention that no modifications are made, which would be ideal for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-line purpose, then a bulleted list of parameters with all values enumerated. Every sentence earns its place, and it is front-loaded with the action.
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 is a simple preview with no side effects and an output schema exists (from context signals), the description is complete. It covers the purpose, all parameters, and the return value is implied by the output schema. No gaps remain.
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 explains each parameter: style with example values ('thai' with Thai/Pali, 'simple' with English), fmt with options ('grid', 'box', 'pipe', 'safe'), and table_style with examples ('mysql', 'separated'). This adds significant meaning beyond 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 prints a preview/example table with sample data. This is a specific verb+resource combination that distinguishes it from siblings like make_table (which presumably creates actual tables) or analyze_table (which analyzes tables).
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 implicitly suggests using this tool for previewing table formats, but it does not explicitly state when to use it vs. alternatives or provide exclusions. The docstring implies usage through its purpose, but lacks direct 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?
No annotations are provided, so the description carries the full burden. It explicitly lists the checks performed: auto-detecting format, border/data line alternation, marker position consistency, column count stability, and correct border type. It also states the return type: a validation report with structural issues. This gives the agent a clear understanding of 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 efficiently structured: a one-line purpose, a bullet list of checks, and clear Args/Returns sections. Every sentence adds value, and the most critical information (purpose) appears first. No redundant phrases or filler.
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 context (1 required parameter, output schema exists), the description covers the essential aspects: the single parameter with format constraints, the checks performed, and the return type. The output schema handles return structure details, so the description does not need to list fields. A complete and sufficient description for the agent.
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 input schema has one parameter 'table_text' with only a title and no description (0% coverage). The description adds meaning by stating 'Raw table text (box, grid, or pipe format)', clarifying the acceptable formats and that it is raw text. This is valuable beyond the schema, though a note about expected input length or encoding would improve it further.
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 validates structural integrity of tables in various formats (box/grid/pipe). It specifies the resource (table text), the action (validate), and the scope of validation (structural checks). This distinguishes it from sibling tools like analyze_table, which may perform deeper analysis, and make_table which creates tables.
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 does not provide guidance on when to use this tool versus alternatives like analyze_table or debug_table. There is no mention of prerequisites, constraints, or scenarios where validation is appropriate. The agent must infer usage from context, which could lead to incorrect tool selection.
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 describes the transformation (parse and render) and return value, but could more explicitly state that the tool is read-only or has no side effects. However, given the tool's nature, this is adequate.
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 concise docstring with a one-sentence summary followed by parameter details. Every sentence is necessary, and the structure front-loads the core purpose.
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?
With 6 parameters (1 required) and an output schema present, the description covers all parameters and states the return value ('Formatted table'), making it complete for an agent to use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description includes an Args section that explains every parameter (csv_text, delimiter, has_header, fmt, style, auto_format) with defaults and meaning, adding full value beyond 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 'Parse a CSV/TSV string and render as a table' uses a specific verb ('parse', 'render') and resource ('CSV/TSV string', 'table'), clearly distinguishing it from siblings like 'make_table_from_json' and 'make_table'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage with CSV/TSV strings but does not explicitly state when to use this tool versus alternatives (e.g., 'make_table_from_json') or provide '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.

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