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pjqdyd

MCP-TS-DEMO

by pjqdyd
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation1/5

    Both tools 'say-hello' and 'say-hi' perform essentially the same greeting action with no meaningful distinction, making it impossible for an agent to choose correctly.

    Naming Consistency5/5

    Both tool names follow a consistent 'verb-noun' pattern with a hyphen, demonstrating a predictable naming convention.

    Tool Count2/5

    With only two tools that are semantically identical, the tool count is effectively one duplicated tool, which is too few for a coherent API surface.

    Completeness2/5

    The server offers only greeting operations with no other capabilities, leaving the tool surface severely limited and lacking in depth or breadth.

  • Average 1.3/5 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

  • 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": [
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  ]
}

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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?

    With no annotations and a minimal description, the description does not disclose any behavioral traits such as side effects, idempotency, or required permissions. The agent has no information about the tool's behavior beyond the generic verb 'say'.

    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 extremely short, but this is under-specification rather than conciseness. It does not provide enough information to be useful, and every sentence is needed but missing.

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

    Completeness1/5

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

    The tool has no output schema and no explanation of return values. The description is insufficient for an agent to understand how to invoke the tool or what to expect as a result, given the low schema coverage and lack of annotations.

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

    Parameters1/5

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

    The schema has 0% description coverage, and the description does not explain the parameter 'name' at all. It only mentions 'the world' in the description, failing to clarify that 'name' customizes the greeting.

    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 'Say hello to the world' is vague and does not specify what the tool actually does (e.g., returns a greeting or logs a message). With a sibling tool 'say-hi', there is no differentiation, making it hard for an agent to select the correct tool.

    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 'say-hi' or in what context. The description lacks any usage context or prerequisites.

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

  • Behavior1/5

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

    No annotations are present, and the description fails to disclose any behavioral traits such as side effects, required permissions, or output format. The tool's behavior is completely opaque beyond a vague greeting.

    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 single sentence is brief but lacks content, resulting in under-specification rather than efficient conciseness. Important information is missing, so the brevity is a drawback.

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

    Completeness1/5

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

    Given the lack of output schema and annotations, the description is severely inadequate. It does not explain what the tool returns or how to interpret its output, leaving critical gaps for correct usage.

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

    Parameters1/5

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

    The input schema parameter 'title' is not mentioned in the description, despite 0% schema description coverage. The description adds no meaning or guidance on how to use the parameter correctly.

    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 'Say Hi to the world' indicates a greeting action, but it is vague and does not differentiate from the sibling tool 'say-hello'. It lacks specificity about what 'say' means (e.g., return a string, print, etc.), making it unclear for an agent to understand the exact purpose.

    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 alternatives like 'say-hello'. There is no context about prerequisites, expected inputs, or suitable scenarios.

    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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