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OwlTing

PayNow Component MCP Server

by OwlTing
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct and singular.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name follows a clear verb_noun pattern (search_paynow_component_documentation).

    Tool Count2/5

    A single tool for a server named 'PayNow Component MCP Server' feels too thin and incomplete for the apparent domain. It suggests significant gaps in functionality beyond documentation search.

    Completeness2/5

    The server's name implies a domain related to PayNow components, but the tool only covers documentation search. There are obvious gaps, such as creating, updating, or managing components, which would be expected for a component server.

  • Average 3.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • 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.

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

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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

  • Behavior3/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It adds valuable context about auto-translation of non-English input, which isn't obvious from the schema alone. However, it doesn't describe other behavioral traits like rate limits, authentication needs, result format, or pagination. The description provides one useful behavioral insight but leaves other aspects unspecified.

    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 perfectly concise - two sentences with zero waste. The first sentence states the core purpose, the second adds crucial behavioral context about auto-translation. Every word earns its place, and the information is front-loaded appropriately for a simple search tool.

    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 single-parameter search tool with no annotations and no output schema, the description is adequate but has clear gaps. It explains the auto-translation feature well but doesn't describe what kind of results to expect, how they're formatted, or any limitations. The description covers the basic operation but leaves the agent guessing about the response format and potential constraints.

    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 fully documents the single 'query' parameter. The description repeats the auto-translation behavior mentioned in the schema description, adding no new parameter semantics. This meets the baseline of 3 when schema does the heavy lifting, but doesn't provide additional value beyond what's already in structured data.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Search PayNow Component documentation' - a specific verb ('Search') and resource ('PayNow Component documentation'). It distinguishes the target resource but doesn't differentiate from siblings since none exist. The purpose is unambiguous but not maximally specific about what 'documentation' entails.

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

    Usage Guidelines3/5

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

    The description provides implied usage guidance through the auto-translation feature statement, suggesting this tool handles multilingual queries. However, it lacks explicit when-to-use guidance, alternatives, or exclusions. With no sibling tools, the need for differentiation is reduced, but no proactive usage context is provided.

    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.

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