U301 URL Shortener MCP Server
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 tool has a single, clear purpose: batch shortening URLs with various customization options.
Naming Consistency5/5There is only one tool name, so consistency is inherently perfect. The naming follows a descriptive verb_noun pattern (u301_shortening_urls_in_bulk) that clearly indicates its function.
Tool Count2/5A single tool for a URL shortener server is too few for the apparent scope. While batch shortening is useful, the lack of tools for individual URL operations (e.g., create, retrieve, update, delete, list) or analytics makes the surface feel incomplete and thin for the domain.
Completeness2/5The tool set is severely incomplete for a URL shortener domain. It only supports batch creation of shortened URLs, with no tools for retrieving, updating, deleting, listing, or analyzing existing short links, creating significant gaps that will hinder agent 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 MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the batch limit (200 URLs) and custom domain support, which are useful behavioral traits. However, it lacks critical details like authentication requirements, rate limits, error handling, or what the response format looks like, leaving significant gaps for a mutation 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 appropriately sized and front-loaded with the core purpose. However, it includes minor redundancy (e.g., repeating 'optional' for parameters already labeled as such in the text) and could be more streamlined. Most sentences earn their place by adding value, but slight editing could improve efficiency.
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 complexity (batch mutation with multiple optional parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose, batch limit, domain, and parameter semantics well, but lacks behavioral details like authentication, response format, or error handling, which are important for a mutation tool.
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?
The schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all parameters: the required 'urls' array structure, and for each URLItem, it explains 'url' (required), 'slug' (optional with default behavior), 'expiredAt' (format example), 'password', and 'comment' (dashboard display). This adds substantial meaning beyond the bare schema.
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 specific action ('batch shorten long URLs'), resource ('U301's short link service API'), and scope ('up to 200 URLs per request'). It distinguishes this as a bulk operation tool with no siblings to differentiate from, making the purpose highly 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 usage for batch URL shortening with custom domain support, but provides no explicit guidance on when to use this tool versus alternatives (e.g., single URL shortening), prerequisites, or exclusions. With no sibling tools, the context is limited to the implied batch scenario.
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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