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luquitared

MCP Server Boilerplate

by luquitared
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Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one is for creating MCP servers (get-mcp-docs), and the other is for greeting users (hello-world). There is no overlap or ambiguity in their functions, making it easy for an agent to choose the correct tool.

    Naming Consistency2/5

    The naming is inconsistent: get-mcp-docs uses kebab-case and a verb-object structure, while hello-world uses kebab-case but is a noun-phrase without a clear verb. This mix of conventions lacks a predictable pattern, though both use kebab-case.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a 'boilerplate' purpose, which typically implies a foundational set of utilities. This minimal count limits functionality and may not adequately cover common boilerplate needs.

    Completeness2/5

    For a boilerplate server, there are significant gaps: it lacks tools for configuration, testing, deployment, or other common boilerplate tasks. The two tools provided (documentation and greeting) do not form a complete surface for building or managing MCP servers.

  • Average 2.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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

  • Behavior1/5

    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. However, it only states 'Make an MCP server' without explaining what this entails—whether it's a read operation, a creation process, what permissions are needed, or what the output might be. This leaves critical behavioral traits unspecified.

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

    Conciseness3/5

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

    The description is a single phrase, 'Make an MCP server', which is concise but under-specified. While it avoids unnecessary words, it lacks the structure and detail needed to be truly helpful, making it more of a placeholder than an informative description.

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is incomplete for a tool with one parameter. It fails to explain what 'Make an MCP server' does in practice, what the result is, or how it differs from the sibling tool, leaving significant gaps in understanding the tool's functionality and context.

    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?

    The input schema has 100% description coverage, with the single parameter 'name' documented as 'The name of the MCP server'. The description adds no additional meaning beyond this, so it meets the baseline score of 3 where the schema does the heavy lifting without extra value from the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Make an MCP server' is a tautology that essentially restates the tool name 'get-mcp-docs' in different words. It doesn't specify what action is performed (e.g., retrieve, generate, or create documentation) or what resource is involved. The purpose remains vague rather than clearly articulated.

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

    Usage Guidelines1/5

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

    No guidance is provided on when to use this tool versus the sibling tool 'hello-world' or any alternatives. The description offers no context about appropriate use cases, prerequisites, or distinctions from other tools, leaving the agent with no usage direction.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. It states 'Say hello to the user', which implies a read-only or informational output, but doesn't disclose behavioral traits like whether it requires authentication, has side effects, or how it interacts with the user. It's minimal and lacks transparency for an agent.

    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 a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place without redundancy.

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

    Completeness2/5

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

    Given the tool's simplicity (1 parameter, no annotations, no output schema), the description is incomplete. It doesn't explain what 'say hello' means in practice, such as the format of the greeting or any return values. For even a simple tool, more context would help an agent understand its behavior.

    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?

    The input schema has 1 parameter with 100% description coverage ('The name of the user'), so the schema does the heavy lifting. The description adds no meaning beyond the schema, as it doesn't explain parameter usage or constraints. Baseline 3 is appropriate given high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Say hello to the user' states a clear action (say hello) but is vague about the resource or mechanism. It doesn't distinguish from the sibling tool 'get-mcp-docs', which appears unrelated. The purpose is understandable but lacks specificity about what 'say hello' entails operationally.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions. The sibling tool 'get-mcp-docs' seems unrelated, but no comparison or usage scenarios are offered.

    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.

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