x402-dataviz-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool in the server, there is zero possibility of confusing it with another tool. The purpose of the tool is clearly described, covering bar, line, and area charts.
Naming Consistency5/5The single tool name 'render_chart' follows a clean verb_noun pattern. With only one tool, naming consistency is trivially achieved.
Tool Count3/5The server contains exactly one tool, which feels thin for a typical MCP server. However, the scope is narrow (chart rendering), so the single tool is not entirely unreasonable, but it is borderline.
Completeness3/5The tool covers the core function of rendering a chart, but lacks auxiliary operations such as data validation, chart configuration, or output handling. For a focused dataviz server, this may suffice but leaves some gaps for more complex workflows.
Average 4.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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 are provided, so the description carries the full burden. It discloses that the tool is 'Free, unauthenticated,' returns an 'SVG string,' and is 'the same renderer' as the paid API. This gives critical behavioral context (auth, cost, output format) beyond the schema. It doesn't detail error handling or side effects, but for a pure chart-rendering tool, this is substantial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core function, and every word serves a purpose. It includes the key output format, cost, authentication status, and alternative context without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (4 params, 100% schema coverage, no output schema), the description is complete: it states the purpose, output format, auth requirements, and distinguishes from the paid API. It doesn't need to explain return values beyond the SVG string since that's the output. No critical gaps remain.
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 doesn't add parameter-specific details beyond what the schema already provides; it merely references 'structured data' and chart types, which are already documented in the schema. It adds no extra semantic value for parameters.
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's function with a specific verb ('Render'), resource ('chart'), and output ('SVG string'). It also distinguishes itself from the paid HTTP API by positioning as the free twin, effectively differentiating it from an alternative.
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 explicitly names an alternative (the paid x402-gated API) and positions this free MCP tool as the recommended option, saying 'no payment required.' It implies when to use it (when you need chart rendering without cost), though it doesn't explicitly list when not to use it. This is clear contextual guidance, but lacks explicit exclusions.
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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