diagram-studio-mcp
Server Quality Checklist
Latest release: v0.1.1
- Disambiguation5/5
The two tools have entirely distinct roles: rendering diagrams versus listing supported engines. No overlap in purpose.
Naming Consistency5/5Both tools follow a clear verb_noun pattern (render_diagram, list_engines), making the API predictable.
Tool Count3/5With only two tools, the server feels minimal but appropriate for a narrow scope; however, it borders on thin for a typical server.
Completeness4/5The core rendering capability is present alongside engine information, but additional conveniences like validation or format conversion are absent.
Average 3.6/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states a noun phrase, giving no information about whether the operation is read-only, the output format, or any side effects. The agent cannot infer safety or return structure from the description.
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 extremely concise, using only four words. It is front-loaded and wastes no words, but it is so sparse that it sacrifices clarity. Still, for a tool with no parameters and a simple purpose, the brevity is acceptable.
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?
Given the tool's low complexity (zero parameters, no output schema), the description is minimally viable. However, it does not explain what the returned data will look like (e.g., a list of engine names and a URL) or any context on how to use the results with the sibling tool. Some gaps remain, but the purpose is clear enough for a simple list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema fully covers the input (empty object). According to the guidelines, a baseline of 4 is appropriate for no-parameter tools. The description adds no parameter semantics, but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Engines soportados y URL de Kroki' clearly identifies the resource as supported engines and the Kroki URL, and the tool name 'list_engines' explicitly indicates the action. It distinguishes itself from the sibling tool 'render_diagram' by focusing on engine discovery rather than rendering.
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 the sibling 'render_diagram'. Usage context is only implied by the tool name and the fact that rendering requires knowing available engines, but no explicit when-to-use or alternatives are mentioned.
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 provided, the description carries the full disclosure burden. It goes beyond a simple action statement by revealing the return type (file path and base64) and the truncation behavior for large diagrams. This is additional behavioral context. However, it does not mention side effects like filesystem writes (though implied by 'path del archivo'), network dependencies, or possible error conditions, so it is not fully transparent.
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 long, immediate and front-loaded with the main action. It wastes no space and includes only relevant details: the rendering service, supported engines, and the concise return format. Every word earns its place.
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?
For a tool with 4 parameters, no annotations, and no output schema, the description provides a solid foundation: it explains the purpose, engine options, and return format with a caveat. It relies on the schema for outputPath default and format enum details, which is acceptable. It does not cover error cases or clarify what constitutes 'large', but it is sufficient for an agent to understand and invoke the tool correctly in most scenarios.
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 low at 25%, with only outputPath having a schema description. The description adds the list of engines, which duplicates the schema enum but also clarifies that graphviz expects DOT syntax. It does not provide additional meaning for the format or source parameters, which remain minimally explained. Thus, the description partially compensates for the low schema coverage but does not fully fill the gap.
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 verb 'Renderiza' with the resource 'source' and identifies Kroki as the rendering service. It also lists supported engines, which distinguishes it from the sibling list_engines tool whose purpose is simply listing engines. The action is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (to render diagrams) and indirectly contrasts with list_engines, but it never explicitly states 'use this when you need to render' or 'use list_engines to see available engines'. There are no prerequisites, exclusions, or alternative scenarios mentioned, so the guidance remains implicit rather than direct.
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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