CURRENT•SEA
Server Quality Checklist
Latest release: v0.0.4
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5The single tool name 'scan_ambiguity' follows a clear verb_noun convention, which is consistent and predictable. No conflicting naming styles exist.
Tool Count2/5A single tool feels too thin for most server purposes. While the scope is narrow, one tool provides minimal utility and may limit agent flexibility, aligning with the 'too few' criterion.
Completeness5/5For the stated purpose of detecting ambiguous wording, the tool covers the core functionality well. It handles multiple ambiguity types (timing, quantities, references, commitments, standards) with no obvious gaps within that narrow domain.
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
- 5 commits in the last 12 weeks
- Last stable release on
- 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.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the service retains invocation metadata but never the submitted text, and that outputs are 'possible issues, not proof of ambiguity.' These details go beyond the schema and meaningfully set expectations, though it doesn't explicitly state read-only or non-destructive status.
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 purpose is front-loaded in the first sentence, followed by usage conditions and behavioral caveats. Each sentence contributes distinct value—scope, when-to-use, data handling, and output interpretation—with no redundancy or filler.
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?
For a single-parameter tool with an output schema already present, the description fully covers purpose, usage conditions, behavioral traits, and interpretation of results. An agent has enough information to decide whether and how to invoke the tool correctly.
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% and the schema already provides a clear description for `text`: 'Text to inspect; it is processed but not retained.' The tool description reinforces the non-retention behavior but adds no new parameter-level details beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Find wording that may need clarification,' naming a specific verb and resource. It then enumerates the concrete categories of ambiguity (vague timing, quantities, references, commitments, standards), making the tool's scope precise and unmistakable even without sibling tools.
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 states 'Use this when an agent needs an inexpensive, deterministic, explainable check...' giving clear triggering conditions. It does not discuss when not to use it or alternatives, but since no sibling tools are provided, this is the maximum useful 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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- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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