nyyon-figures

by LevNyyon

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a distinct purpose: reasoning prompt, settings, template listing, and three rendering variants. No overlap in functionality, so an agent can clearly distinguish them.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (get_, list_, render_). This predictability aids agent selection and code generation.
Tool Count5/5
With 6 tools covering the core workflow (prompt, settings, templates, rendering), the count is well-scoped for figure generation without being too few or excessive.
Completeness4/5
The tool surface covers the essential workflow: obtaining a prompt, checking settings, listing templates, and rendering figures/covers. A minor gap is the lack of a tool to retrieve or delete rendered files, but this is not critical for typical usage.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 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
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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, the description carries full burden. It discloses the resolution (1200x630) and return type (file path), but omits critical behavioral traits: side effects (file creation), permissions, error handling, performance, or file format. The description is insufficient for safe agentic use.
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 a single, dense sentence that front-loads key information (resolution) and covers core elements. It is concise without being vague. However, it could benefit from breaking into multiple sentences for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 5 parameters, the description should be more comprehensive. It mentions output but not file format, naming conventions, or whether it overwrites. The tool's complexity (image rendering) warrants richer context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 80%, so baseline is 3. The description mentions 'headline with one word in accent colour' which maps to title and highlight, but adds no new parameter semantics beyond what the schema already provides. No improvement over 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 what the tool does: renders a 1200x630 featured/hero cover with specific elements (wordmark, shapes, headline). It also mentions the output (returns file path). However, it does not explicitly distinguish from sibling tools like render_figure or render_set, which may have overlapping purposes.
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 are no when-to-use or when-not-to-use statements, and no mention of prerequisites or context. This is a significant gap for an AI agent deciding between render_cover, render_figure, or render_set.
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 provided. The description does not disclose behavioral traits such as caching, required permissions, side effects, or rate limits. Being a getter, it is likely non-destructive, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is directly informative without any fluff. Every word earns its place.
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?
Without an output schema, the description reasonably details the return contents. It covers major aspects of the brand theme but does not specify format or structure.
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?
No parameters exist, so schema coverage is 100%. The description adds value by listing what the return includes, which goes beyond the empty schema. Baseline for 0 params is 4.
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 returns the active brand theme with specific elements (colors, fonts, canvas sizes, env vars). It distinguishes from sibling tools like render_cover and list_templates which are about rendering or listing different resources.
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 vs alternatives. For a simple getter it may be obvious, but lacks any context about prerequisites or when not to use.
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 full responsibility. It mentions the return value (list of written paths) but does not disclose side effects (e.g., file writing behavior), prerequisites (e.g., output directory existence), or error handling. The absence of such details limits transparency for an AI agent.
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, consisting of two sentences that front-load the core purpose and output. Every sentence adds value without redundancy, achieving maximum information density with minimal 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 complexity (nested objects, 3 parameters, no output schema), the description covers the basic intent and structure but lacks details on return value format, error scenarios, and constraints (e.g., max items). It is adequate for a simple tool but not fully self-contained for an AI agent to use confidently without schema inspection.
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 low (33%, only out_dir has a description). The tool description adds minimal parameter context beyond what the schema already exposes, such as the structure of figures (template and slots) and cover being optional. It does not explain the semantics of 'slots' or the behavior of the cover fields, leaving the agent to infer from the schema alone.
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 renders a full article set in one call, specifying input structure (array of figures plus optional cover) and output (list of written paths). It distinguishes itself from sibling tools by mentioning it is a convenience over calling render_figure repeatedly, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use this tool ('convenience over calling render_figure repeatedly'), implicitly suggesting it is ideal for multiple figures. However, it does not explicitly state when not to use it (e.g., for a single figure or standalone cover) or provide alternative scenarios, leaving some room for ambiguity.
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?
Discloses output as file path and 2x rendering for crisp output. However, with no annotations, more information would be beneficial, such as whether it overwrites existing files, idempotency, or required permissions. Still, key behaviors are covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no redundancy. First sentence states purpose, second explains inputs, third describes output and quality. Every sentence adds value.
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 3 parameters, no output schema, and no annotations, the description is fairly complete. It covers input requirements, output format, and quality hint. Could mention the output path format or directory confinement, but overall adequate for the tool complexity.
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 has 33% coverage; the description compensates by referencing list_templates for slot schema and explaining that slots are validated there. However, it does not detail the structure of slots or add meaning beyond the schema for out_path. Adequate but not excellent.
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?
Clearly states it renders a single in-article diagram to PNG. Distinguishes from sibling tools like render_set (multiple) and render_cover (cover image) by specifying 'single' and 'in-article'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Instructs to provide template name and slots, and references list_templates for slot schema, providing clear context. Does not explicitly state when not to use, but the purpose is well-defined and alternatives are implied by sibling names.
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 full burden for behavioral disclosure. It states that embedding the article modifies the prompt, but does not mention potential side effects, idempotency, rate limits, or authentication requirements. For a tool that likely has no destructive side effects, this is adequate but not complete.
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 a single paragraph of four sentences, front-loaded with the core purpose. Each sentence provides necessary information: what the prompt does, how to pass the article, what to do with the output. It is concise without omitting critical details.
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 returns a prompt for figure creation, the description explains the overall workflow and references sibling tools (render_figure, render_cover). It does not describe the output schema or prompt format, but that is reasonable for a prompt-returning tool. It covers the key context for use.
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%, so the description must compensate. It explains that title, excerpt, and body_text are parts of the article to embed in the prompt, and that omitting all of them yields a different output (rules + template menu). This adds significant meaning beyond the parameter names alone.
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 that the tool returns a reasoning prompt for turning an article into figures and a cover, with specific details about template selection and sentence anchoring. It distinguishes itself from sibling tools like render_figure and render_cover by indicating that this tool produces the prompt to guide those subsequent calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to pass article parameters (to get the full prompt) and when to omit them (to get just the rules and template menu). It also provides the downstream workflow: use the prompt to produce figure-spec JSON, then call render_figure/render_cover. This covers both usage scenarios and next steps.
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?
With no annotations, the description fully explains the tool's behavior: it lists templates with slot schemas. It implicitly indicates a read-only, non-destructive operation. However, it could explicitly state that no modifications are made or mention any rate limits, but for a simple list, this is sufficient.
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?
Two sentences contain all essential information without fluff. The first sentence immediately states the action, and the second provides usage guidance. Every word earns its place.
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 that the tool has no parameters and no output schema, the description is fully complete. It tells the agent what the tool does, what it returns (list of templates with slot schemas), and how it should be used (called first). There is no missing context.
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 zero parameters, so schema coverage is 100% and there is no need for additional parameter semantics. The description does not attempt to add information about non-existent parameters, which is appropriate.
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 identifies the tool's function: lists all diagram templates (16) and the featured cover with their slot schemas. It uses specific verb 'list' and specifies the resource 'diagram templates' and content 'slot schemas'. It distinguishes itself from sibling render tools which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Call this first to learn what shapes exist and exactly which slots each render tool expects.' This provides clear guidance on the tool's role as a precursor to render tools, effectively excluding misuse.
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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