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Server Quality Checklist

83%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clearly defined.

    Naming Consistency5/5

    A single tool named 'check_cabal_risk' uses a clear verb_noun pattern. There are no other tools to introduce inconsistency.

    Tool Count3/5

    The server has only one tool, which feels minimal for a general risk-checking service. While the tool itself is comprehensive, a broader set could include additional analytical or comparison tools.

    Completeness5/5

    The single tool covers all stated detection needs—exit liquidity, cabal scoring, funding clusters, bundle detection, dump detection, deployer history, and honeypot checks—in one call. It fully addresses its domain.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 12 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior5/5

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

    The description discloses all major behavioral traits: what it detects (cabal score, funding clusters, Jito-bundle, coordinated-dump, serial rug history, honeypot), the evidence returned, and rate limits (250 scans/month, no API key). No annotations exist, so the description fully carries the burden.

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

    Conciseness4/5

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

    The description is relatively long but well-structured: it opens with the core purpose, enumerates all detection components, and concludes with usage guidance. Every sentence adds value, though some redundancy exists (e.g., 'rug detection' and 'serial-rug deployer').

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

    Completeness5/5

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

    Given the complexity of the tool (multiple detection types, no output schema), the description is extremely complete. It covers all detection mechanisms, the output verdict, evidence details, and practical usage context, ensuring an agent can select and invoke the tool appropriately.

    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?

    There is only one parameter ('mint') with full schema coverage (100%). The description adds an example address but no further semantic value beyond what the schema provides. This meets the baseline of 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 clearly states the tool's purpose: real-time on-chain coordinated-wallet and rug detection for Solana token mints. It specifies the exact output (Exit-Liquidity Risk verdict, cabal score, detection details) and the target use case (pump.fun, PumpSwap, Raydium tokens).

    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 explicitly tells when to use the tool: 'before an agent buys a ... token to answer: are you the exit liquidity?' It also mentions the free tier (250 scans/month) but does not discuss when not to use or alternatives (though no siblings exist).

    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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