decode_plantuml
Decode encoded PlantUML string back to PlantUML code
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| encoded_string | Yes | Encoded PlantUML string to decode |
xpay✦ DevTools Collection
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.8/5.0
Behavior3/5
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 clearly states the transform (decode to PlantUML code) but does not mention error handling, input validation, or side effects. For a simple transform, this is adequate but not rich; a more explicit note about non-mutating behavior would elevate it.
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 a single, clear sentence with no redundancy. It is front-loaded with the action verb and immediately conveys the purpose. 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?
For a simple one-parameter tool with full schema coverage, the description is complete enough. It states the input and the output implicitly ('back to PlantUML code'). Since no output schema exists, the description's mention of the output helps, though it could specify the return format (e.g., UTF-8 string) for absolute clarity.
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?
The input schema covers 100% of the parameter with a description ('Encoded PlantUML string to decode') that mirrors the tool description. The description adds no additional semantic detail beyond what the schema already provides, so the baseline of 3 applies.
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 uses a specific verb ('decode') with a clear resource ('encoded PlantUML string') and outcome ('back to PlantUML code'). It distinguishes itself from sibling tools like encode_plantuml and generate_plantuml_diagram by explicitly stating the inverse operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you have an encoded PlantUML string. However, it does not explicitly state when to use this tool over alternatives, nor does it mention preconditions or exclusions. The sibling context suggests encode_plantuml as the inverse, but the description itself provides no direct comparison.
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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TDQS
C2.4/5.0
Disambiguation2/5
Several tools have overlapping or ambiguous purposes that could confuse an agent. For example, analyze_code, analyze_patterns, and analyze_design_patterns all involve code analysis with unclear boundaries, while check_deceptive_patterns and check_placeholders seem like subsets of analyze_code. The NPM tools form a coherent group but are distinct from the rest, creating a fragmented toolset.
Naming Consistency2/5
Naming conventions are highly inconsistent across the toolset. Some tools use snake_case (e.g., analyze_code, execute_code), others use camelCase (e.g., npmAlternatives, npmChangelogAnalysis), and there are mixed styles like query-docs with hyphens. The NPM tools follow a consistent npmPrefix pattern internally, but this is not applied to other tools, leading to overall chaos.
Tool Count2/5
With 39 tools, this server is overloaded for a 'DevTools Collection' scope. The count feels excessive, as many tools could be consolidated (e.g., multiple analysis tools) or logically grouped. While the NPM tools are numerous but focused, the overall set lacks cohesion, making it cumbersome for an agent to navigate and select appropriate tools efficiently.
Completeness3/5
The toolset covers a broad range of development tasks, including code analysis, execution, documentation, and package management, but there are notable gaps. For example, there is no tool for code generation or refactoring, and the Microsoft and NPM tools are well-covered but isolated from other functionalities. The surface is extensive but not fully integrated, with some dead ends in workflow transitions.