code-shot
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct input: render_code handles source code blocks, while render_diff handles unified diffs. No overlap in purpose.
Naming Consistency5/5Both tools follow the verb_noun pattern (render_code, render_diff), maintaining a consistent naming convention.
Tool Count4/5Two tools is minimal but appropriate for the narrow domain of code visualization; each tool serves a clear and distinct purpose.
Completeness4/5The server covers the primary use cases of rendering code and diffs as images. Minor gaps like batch rendering or line numbers are not essential but would be nice additions.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 19 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. Describes input format (standard git diff output), parsing of @@ hunk headers, and color coding. Lacks details on error handling or non-XSS, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear front-loaded purpose. Slightly verbose, and contains a typo ('deltions' instead of 'deletions'), but overall concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Explains inputs well, but missing details on output (e.g., whether result is a URL, base64, or file). No output schema, so description should cover this.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions. Description adds minor context (e.g., diff structure, output_format='png' shorthand) but does not significantly augment schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb ('Render') and specific resource ('git unified diff as a beautiful syntax-highlighted image (SVG or PNG)'). Distinguishes from sibling 'render_code' by focusing on diffs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases ('PR reviews, sharing code changes on mobile, visualising changes'). Does not exclude alternatives, but context is clear.
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?
Despite no annotations, the description discloses key behaviors: default output is SVG string, PNG saves to temp file, supports 40+ themes and languages. It does not mention side effects or rate limits, but these are unlikely for a render tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear first sentence followed by bullet-like lists of themes and languages. It is concise but includes all necessary details without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no output schema), the description covers essential aspects: output format, supported themes/languages, and usage guidance. It lacks mention of return value structure but that is acceptable without an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds context about output format behavior but does not significantly enhance parameter meaning beyond the schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool renders source code as a syntax-highlighted image (SVG or PNG) for showing code visually to humans. It distinguishes from sibling tool 'render_diff' by focusing on code rather than diffs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'Perfect for AI agents to show code visually to humans on mobile devices' and instructs to include full code and notify the user. It could explicitly compare with 'render_diff' but overall offers good 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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- Evaluate tool definition quality.
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