Skip to main content
Glama
Mlancoliee

EdgeOne Makers MCP

by Mlancoliee
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.0.3

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity among tools. The description is clear and specific.

    Naming Consistency5/5

    With a single tool, naming consistency is inherently perfect. The name 'deploy_folder' is descriptive and follows a verb_noun pattern.

    Tool Count4/5

    A single deployment tool for EdgeOne Pages is narrowly focused but appropriate given the server's stated purpose. However, one tool feels slightly thin for a typical MCP server.

    Completeness2/5

    The server lacks essential lifecycle operations such as listing, updating, or deleting deployments. Only deployment is supported, leaving significant gaps for common workflows.

  • Average 4.7/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 6 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations provided, so description carries full burden. It discloses that deployment returns URL and metadata, and includes error handling and region preference behavior. Does not explicitly state it is a write operation, but 'deploy' implies mutation, which is clear enough.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Well-structured with intro, rules, and parameter details, but somewhat lengthy. Could tighten phrasing in rules without losing clarity. Still, every sentence adds value.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    No output schema, but description mentions return of deployment URL and metadata. Parameter coverage is thorough, and context about project type detection, region, and naming is complete. No gaps for effective usage.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, but description adds substantial value: region explains when optional, projectName gives naming rules beyond schema, projectType includes detection logic, workspacePath and builtFolderPath clarified. Significantly aids agent in filling parameters correctly.

    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?

    Description clearly states it deploys a folder with web assets to EdgeOne Pages, listing supported project types. It explicitly positions itself as the only deployment method for EdgeOne Pages/Makers, distinguishing it from any potential siblings.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides comprehensive rules: exclusive deployment method, no fallback, asks for region preference when token missing, naming conventions, and post-deployment preview instructions. Explicit when-to-use and when-not-to-fallback guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

edgeone-makers-mcp MCP server

Copy to your README.md:

Score Badge

edgeone-makers-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Mlancoliee/edgeone-makers-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server