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ktmcp-cli

@ktmcp/betfair

by ktmcp-cli
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with another tool. The tool set has zero ambiguity in terms of tool selection.

    Naming Consistency4/5

    With a single tool, naming consistency is trivially maintained. However, the name 'post__request' is unconventional and not a clear verb_noun pattern, making it somewhat unpredictable.

    Tool Count1/5

    A single trivial tool for what appears to be a Betfair server is an extreme mismatch. The tool does not cover the expected scope of a betting exchange API.

    Completeness1/5

    The server provides no apparent functionality for Betfair operations such as market data, betting, or account management. The one tool is a low-level socket request, leaving the domain severely incomplete.

  • Average 1.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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

  • Behavior1/5

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

    No annotations exist, so the description carries full responsibility for behavioral disclosure. It only states the protocol delimiter, omitting details about side effects, return values, error behavior, or any operational characteristics.

    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 sentence, which is concise, but it is under-specified rather than efficiently informative. It conveys one fact but lacks essential purpose and usage details.

    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 tool's simplicity (1 parameter, no output schema), the description is severely incomplete. It fails to explain what the tool does, expected body structure, or response behavior, leaving the agent without essential 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?

    Schema coverage is 100% with a single 'body' parameter described as 'Request body'. The description adds no additional semantic meaning, but the high schema coverage yields a baseline of 3. The CRLF note hints at body format but is vague.

    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 does not state what the tool does; it only describes the protocol as CRLF-delimited socket (not HTTP). There is no verb or resource indicating the action, making the purpose vague and misleading.

    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 or how it differs from alternatives. The mention of 'socket protocol' implies low-level network use, but no explicit context or exclusions are given.

    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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