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Baneado98

contract-auditor

by Baneado98
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

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

    Naming Consistency5/5

    Single tool has a clear verb_noun name ('audit_contract'), which is consistent with itself and follows a predictable pattern.

    Tool Count4/5

    One tool is borderline but acceptable given the narrow focus of the server. The tool is comprehensive and not trivial.

    Completeness5/5

    The server fully covers its stated purpose of performing a quick security scan. No obvious gaps for the intended use case.

  • 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
    • 7 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.

  • This repository includes a glama.json configuration file.

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

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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 fully carries the transparency burden. It details that the tool fetches verified source from Sourcify (key-less), reads live on-chain state via public RPC, and statically scans for specific patterns (owner controls, selfdestruct, delegatecall, etc.). It also states the limitation 'Heuristic, not a formal audit,' providing honest behavioral context.

    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 long paragraph but is well-structured: it starts with the primary purpose and then delves into details. While comprehensive, it is slightly verbose and could be broken into shorter sentences for easier scanning without losing information.

    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 tool has 4 parameters, no output schema, and no annotations, the description is remarkably complete. It explains inputs, the process (what data is fetched and analyzed), output format (verdict types), and limitations. All critical aspects are covered, making the description self-sufficient for an agent to understand and use the tool correctly.

    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 all parameters have descriptions. The tool description explains the overall process but does not add significant meaning beyond the schema for parameters; for example, it mentions 'source' as optional when no verified source exists, but the schema already covers that. Baseline is 3 as the schema handles the burden.

    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 security quick-scan on smart contracts before interacting or sending funds, specifying it returns a SAFE/CAUTION/HIGH-RISK verdict with an explained risk score. It lists the checks performed (verified source, on-chain state, static scans), making the purpose specific and actionable.

    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 says 'Use whenever you're about to approve, buy, fund, or integrate a contract you don't fully trust,' providing clear usage context. It also notes it's a heuristic, not a formal audit, implying caution but does not explicitly state when not to use or mention alternatives, which would improve the score.

    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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