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Baneado98

solidity-sentinel

by Baneado98
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Server Quality Checklist

75%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no risk of confusion. The tool's purpose is clearly distinct from any other potential tool.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (audit_contract), which is consistent and descriptive. No inconsistencies to evaluate.

    Tool Count3/5

    With only one tool, the server is on the thin side. However, the tool is comprehensive, encapsulating both free and premium audits via parameters, which somewhat justifies the low count.

    Completeness4/5

    The tool covers a wide range of security detectors and audit modes (source code, bytecode, deep analysis). Minor gaps like gas analysis or integration checks are absent but not critical for the core audit purpose.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

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

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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?

    With no annotations provided, the description carries full burden and excels. It details the free and premium audit behaviors, the detectors used, output format (verdict, risk score, findings), and the prerequisite for deep audit (API key). It clearly sets expectations.

    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 a single dense paragraph but front-loads the core purpose. Every sentence adds value, though it could be more structured (e.g., bullet points) for quicker scanning. Slightly verbose but still efficient.

    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 no output schema and two parameters, the description is comprehensive. It covers input types, tiers, output expectations, and use cases. No gaps are evident for an AI agent to understand and invoke the tool correctly.

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

    Parameters5/5

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

    The description adds significant meaning beyond the 100% schema coverage. For 'source', it expands to accept bytecode. For 'deep', it explains the premium audit capabilities in detail. This extra context aids correct parameter selection.

    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 performs a static security audit of Solidity smart contracts or deployed bytecode, specifying the verb 'audit' and the resource. It effectively distinguishes the tool's function even without sibling tools.

    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 provides explicit usage guidance: 'Call this BEFORE deploying, approving, or sending funds to a contract.' It also explains when to use the free vs premium tier, though it does not list alternatives or when not to use.

    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.

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