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rad1092

firstcall-demo-mcp-server

by rad1092
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JavaScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    Only one tool exists, so there is no risk of confusion between tools. However, the vague description 'Verified API tool recipes for AI agents' does not clearly distinguish its purpose.

    Naming Consistency3/5

    With a single tool, naming consistency is not a significant issue, but the name 'github_user_lookup' uses a noun-heavy pattern that is not a standard verb_noun convention.

    Tool Count2/5

    A single tool for a server named 'firstcall-demo-mcp-server' suggests an extremely narrow scope, likely insufficient for meaningful interaction without additional tools.

    Completeness2/5

    The single tool only covers user lookup, which is a minimal subset of potential GitHub operations. Obvious gaps like repository or issue management exist, making the surface incomplete.

  • Average 2.1/5 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.

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

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

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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?

    Annotations clearly indicate read-only, non-destructive, idempotent behavior. The description adds no additional behavioral context beyond what the annotations already provide. Since annotations are explicit, the bar is lower, and a score of 3 is appropriate as the description does not contradict them.

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

    Conciseness2/5

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

    The description is a single sentence but is vague and uninformative. It does not earn its place; conciseness should provide value, not just brevity. The sentence could be replaced with nothing without loss of information.

    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?

    Despite having a simple structure with one parameter and an output schema, the description fails to explain the tool's core function (looking up GitHub users). A complete description would state the purpose clearly; this one is insufficient for an agent to understand its role.

    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% coverage with a single parameter 'username' described as 'path'. The description adds no further meaning, so it does not enhance understanding beyond the schema. Baseline score of 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.

    Purpose1/5

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

    The description 'Verified API tool recipes for AI agents' is extremely generic and does not specify what the tool does. It lacks a verb and resource, and fails to distinguish it from any other tool. The tool name 'github_user_lookup' hints at the purpose, but the description does not reinforce it.

    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?

    There is no guidance on when to use this tool versus alternatives. The description provides no context about the tool's purpose or appropriate use cases, leaving the agent to infer from the name alone. No exclusion criteria or alternative tools are mentioned.

    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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