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srmcguirt

MCP Server Starter Kit

by srmcguirt
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Echo and fetch_url are completely distinct in purpose—one verifies connectivity, the other retrieves web content. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    Both names are imperative and lowercase, but one is a bare verb (echo) while the other follows a verb_noun pattern (fetch_url). This mixed convention, while readable, is not fully consistent.

    Tool Count3/5

    Two tools is appropriate for a starter kit that aims to demonstrate basic MCP functionality, but the count feels thin compared to typical servers that offer more comprehensive operations.

    Completeness4/5

    The tool set covers the intended purpose of a starter kit: verifying server operation and fetching URL content. There are no obvious gaps for this narrow scope, though it lacks broader capabilities.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 18 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description must fully disclose behavioral traits. It does mention the return size limit ('up to 100KB'), but this conflicts with the schema's max_bytes property which allows up to 1MB, creating ambiguity. The description also does not address response format, error behavior, redirects, or authentication, leaving key behavioral characteristics undefined.

    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 just two sentences, with the action front-loaded. Every sentence adds value: the first states the core function, the second notes the size limit and common use cases. There is no redundant or filler content.

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

    Completeness3/5

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

    For a simple fetch tool with a documented schema and no output schema, the description is mostly adequate. However, it omits details about what 'text content' means (raw HTML vs extracted text), how errors are surfaced, and the fact that the 100KB limit is only the default and can be increased. These omissions, combined with the size-limit ambiguity, prevent a higher score.

    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 schema provides descriptions for all three parameters, with 100% coverage. The description does not add any parameter-specific detail beyond what is already in the schema; it only generalizes about the return size. Per the rubric, with high schema coverage, the baseline score is 3.

    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 begins with a specific verb+resource pair: 'Fetch the text content of a URL.' This clearly distinguishes the tool from its sibling 'echo', which simply echoes input. The mention of HTTP endpoints further clarifies the tool's scope.

    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 provides usage context by stating it is 'Useful for reading web pages, APIs, or any HTTP endpoint.' However, it does not explicitly state when not to use the tool or mention alternatives beyond the sibling 'echo', which is clearly not an alternative. This earns a 4 for clear context without explicit exclusions.

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

  • 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. It states the core behavior ('Echo back a message') but does not add context about side effects, authentication, or limits. For a simple echo tool, this is adequate but not rich.

    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 the purpose front-loaded and the usage context in the second sentence. No wasted words.

    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?

    The tool is simple (one parameter, no output schema), and the description covers what it does and when to use it. The 'echo back' phrasing implies the return value, making it sufficiently complete for a test/debugging tool.

    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% (the only parameter 'message' is described in the schema). The description adds no extra parameter info, aligning with the baseline 3 when the schema covers parameters.

    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 'Echo back a message' clearly states the tool's function with a specific verb and resource. It is distinct from the sibling tool fetch_url, which fetches URLs, so there is no ambiguity.

    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 provides a clear context: 'Use to verify the MCP server is working.' It tells when to use the tool but does not explicitly mention alternatives or when-not-to-use cases, though these are not critical for an echo utility.

    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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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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