ai-visibility-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly unique and distinct.
Naming Consistency5/5A single tool with a descriptive snake_case verb_noun name. There is no inconsistency to assess, but the naming is clear and follows conventional MCP patterns.
Tool Count3/5A single tool feels thin for a dedicated MCP server, even for a focused purpose. The borderline count leaves little room for related operations but is not excessively sparse.
Completeness4/5The tool directly covers the core stated purpose of checking AI visibility. Minor gaps exist, such as batch checks or historical analysis, but they do not block the primary use case.
Average 3.5/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
- 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, and it only states the tool's purpose. It does not reveal whether the check involves a live network fetch, what signals are analyzed (e.g., robots.txt, llms.txt, meta tags), or whether results are instantaneous or sampled. Nothing is disclosed that contradicts annotations because no annotations exist.
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 a single, front-loaded sentence with zero wasted words, stating the action before the object. It is efficient, though slightly terse — a phrase clarifying what 'discoverable' means could be added without hurting conciseness.
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?
This is a low-complexity tool with one required parameter and an output schema present, so the description does not need to describe return values and the input is fully documented in the schema. The main context gap is the absence of any explanation of what 'AI discoverability' is based on, but for such a simple tool the definition is nearly complete.
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% — the url parameter already documents its type and accepted formats ('example.com' or 'https://example.com'). The tool description adds no additional meaning about the parameter, so the baseline 3 is appropriate.
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 states a clear action ('Check') and a specific resource/scope ('how discoverable a website is to AI assistants and AI search engines'), so an agent can tell what the tool does at a glance. There are no sibling tools to distinguish from, and the term 'discoverability' is left slightly undefined, so it falls just short of a 5.
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?
Because there are no sibling tools, there are no alternatives to exclude, but the description still provides only implied use context: if an agent wants to know a site's AI visibility, this is the tool. There is no explicit when-to-use guidance, exclusions, or prerequisites, making the usage guidance minimally adequate.
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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