Structured-Argumentation
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'structuredArgumentation' has a clearly defined purpose for systematic dialectical reasoning and argument analysis, so agents cannot misselect among non-existent alternatives.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'structuredArgumentation' follows a clear and descriptive pattern (adjective+noun), and there are no other tools to compare it against for inconsistency.
Tool Count2/5A single tool is generally too few for a server's purpose, especially one as broad as 'Structured-Argumentation' which involves complex reasoning tasks. While the tool is feature-rich, having only one tool limits flexibility and forces all operations through a single interface, which feels thin for the apparent scope.
Completeness3/5The tool covers a wide range of argumentation features (creation, critique, synthesis, visualization), but as a single tool, it may lack granular operations. For example, there are no separate tools for specific tasks like listing arguments, updating confidence scores, or managing argument hierarchies, which could lead to inefficiencies or dead ends in complex workflows.
Average 3.9/5 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
This repository is licensed under Apache 2.0.
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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes behavioral traits like 'facilitates creation, critique, and synthesis' and 'evaluates argument strengths,' which imply mutation and analysis capabilities. However, it lacks details on permissions, rate limits, error handling, or what the tool returns (e.g., structured output, visualizations as mentioned but not explained). The description adds value but is incomplete for a complex 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 sections (purpose, usage, features, parameters), front-loading key information. It's appropriately sized for a complex tool but includes some redundancy (e.g., 'facilitates dialectical progression' could be condensed). Most sentences earn their place, though minor trimming is possible.
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 high complexity (13 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, usage, features, and parameters well, but lacks output details (what the tool returns, e.g., analysis results or visualizations) and behavioral specifics like error cases. For a tool with rich input but no structured output, more context on results is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It includes a 'Parameters explained' section that defines all 13 parameters (e.g., 'claim: The central proposition,' 'confidence: Your confidence level'), adding clear meaning beyond the bare schema. This fully addresses the coverage gap, making parameters understandable for an AI agent.
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 clearly states the tool's purpose as 'systematic dialectical reasoning and argument analysis' with specific verbs like 'analyze,' 'create,' 'critique,' and 'synthesize.' It distinguishes the tool's focus on formal argumentation structures, though without sibling tools, differentiation isn't tested. The purpose is specific but slightly abstract, lacking concrete examples of what the tool outputs.
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 includes a 'When to use this tool' section with four explicit scenarios (e.g., evaluating competing perspectives, analyzing ethical dilemmas), providing clear context for application. However, it lacks exclusions or alternatives (e.g., when not to use it, or comparisons to other reasoning tools), and with no sibling tools, alternative guidance isn't applicable.
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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