Skip to main content
Glama

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one creates diagrams, the other edits them. There is no overlap in functionality, so an agent can easily select the right tool based on whether a new diagram or a modification is needed.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern consistently: generate_diagram and edit_diagram. This provides a predictable and intuitive naming convention.

    Tool Count3/5

    With only two tools, the set is on the thin side. For a diagram service, one might expect additional operations like list or delete, but the narrow scope of generating and editing makes the count borderline acceptable.

    Completeness4/5

    The core lifecycle of diagram creation and modification is covered. However, there is no way to list, delete, or retrieve a specific diagram by ID, which could be considered minor gaps, but the shareable URL and edit-by-ID cover the essential workflows.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the transparency burden. It discloses that the change is applied and that an updated share URL is returned, but it does not mention whether the original diagram is overwritten, whether changes are reversible, or any error conditions. This is adequate but leaves gaps.

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

    Conciseness5/5

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

    The description is two sentences with no redundant phrases. It front-loads the action and purpose, then provides a concrete example and return value, making every word earn its place.

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

    Completeness4/5

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

    For a simple, two-parameter tool with no output schema, the description covers the essential context: what it does, an example of the instruction format, and the return value. It could add prerequisites or caveats, but the information provided is sufficient for basic use.

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

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents both parameters. The description adds an example instruction, but does not provide additional semantic detail beyond what the schema gives, meeting the baseline for full coverage.

    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?

    The description starts with a specific verb+resource: 'Apply a plain-English change to a diagram previously created with generate_diagram.' It clearly distinguishes this from the sibling generate_diagram by focusing on editing existing diagrams, and includes a concrete example.

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

    Usage Guidelines4/5

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

    The phrase 'previously created with generate_diagram' gives a clear prerequisite for when to use this tool. However, it does not explicitly state exclusions (e.g., when not to use it) or alternative tools beyond the implicit contrast with generate_diagram.

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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description conveys key behavioral aspects: it creates a diagram, accepts various source formats, and returns a URL plus diagram_id. It does not disclose potential side effects like resource consumption or rate limits, but for a generation tool, the core behavior is disclosed.

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

    Conciseness5/5

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

    The description is compact and front-loaded. The first sentence states the primary action and input types; the second explains the output and follow-up capability. No unnecessary words or repetition.

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

    Completeness4/5

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

    Given the absence of an output schema, the description appropriately explains what is returned (shareable URL and diagram_id). It covers input formats and hints at subsequent editing. It lacks error-handling or authentication details, but these are not critical for a generation tool and the description is fairly complete.

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

    Parameters3/5

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

    Schema coverage is 100%, so the schema already documents both parameters well. The description adds context about input types and the auto-detection default, but this largely reinforces what the enum descriptions already convey. No significant additional parameter semantics are needed.

    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?

    The description clearly states the tool generates diagrams from natural language or source code, specifying the output (shareable URL + diagram_id) and hinting at the sibling tool for follow-up edits. It distinguishes itself well from edit_diagram.

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

    Usage Guidelines4/5

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

    The description states the input types (natural-language description or source code formats) and mentions that the returned diagram_id is for follow-up edits, implying when to use edit_diagram instead. However, it does not explicitly state when not to use this tool or provide direct alternative comparisons.

    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

draft1-mcp MCP server

Copy to your README.md:

Score Badge

draft1-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/abdelhadi-azouni/draft1-mcp'

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