Md2svg-mcp
Server Quality Checklist
Latest release: v0.1.8
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as converting markdown to SVG images, making it unambiguous in isolation.
Naming Consistency5/5The single tool name 'markdown_to_svg' follows a clear verb_noun pattern (convert markdown to SVG). With only one tool, consistency is inherently perfect as there are no other names to compare against.
Tool Count2/5A single tool is too few for most practical server purposes, as it limits functionality and flexibility. While it might suffice for a very narrow task like markdown-to-SVG conversion, it feels thin and under-scoped for typical MCP server use cases.
Completeness3/5For the narrow domain of markdown-to-SVG conversion, the single tool covers the core function. However, there are obvious gaps such as configuration options (e.g., styling, sizing), error handling tools, or complementary operations like SVG validation or batch processing, making the surface notably incomplete for broader agent workflows.
Average 2.4/5 across 1 of 1 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
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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 only states the conversion function without mentioning side effects (e.g., file creation, overwriting), error handling, performance, or output details. This is inadequate for a tool that likely writes files and processes input, leaving the agent with 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a single phrase, which is efficient and front-loaded. However, it includes a typo ('mardown'), slightly reducing clarity. Overall, it's appropriately sized for a simple tool but could benefit from minor correction.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It only states the basic function without addressing parameter meanings, behavioral traits, or output format. For a conversion tool with file output, this leaves significant gaps in understanding how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameters, but it adds no parameter information beyond the tool's name. Parameters like 'md_text', 'output_file_path', 'width', and 'padding' are undocumented in both schema and description, leaving their purposes unclear. The description fails to provide any semantic context for the inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'mardown转换为svg图片' (markdown to SVG image) clearly states the tool's function as converting markdown to SVG, which is a specific verb+resource. However, it contains a typo ('mardown' instead of 'markdown'), and there are no sibling tools mentioned, so differentiation isn't applicable. The purpose is understandable but lacks precision due to the error.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, prerequisites, or context. It simply states what the tool does without any usage instructions or exclusions. Since no sibling tools are listed, this isn't a major gap, but it still lacks basic operational context.
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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