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Neurobyteio

AgentRisk MCP

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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only a single tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is uniquely defined.

    Naming Consistency5/5

    The tool name follows a clear verb_noun pattern ('check_token_risk'), and with only one tool there are no inconsistent naming conventions to flag.

    Tool Count3/5

    A single tool feels thin for a server, even though the tool itself is comprehensive and well-scoped. It sits at the borderline of being too few, but the tool's depth partially compensates for the lack of additional tools.

    Completeness5/5

    For the stated purpose of token risk checking on Base, the tool covers a wide range of critical checks including honeypot detection, deployer history, impersonation, LP-lock verification, and live sell simulation. It provides a clear decision, leaving no obvious dead end within its domain.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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, the description carries the full burden. It discloses the internal checks (honeypot, deployer history, impersonation, LP-lock, live sell simulation), the return value (risk score/level/should-trade decision with structured reasons), and the cost (0.15 USDC via x402, free trials). This is comprehensive transparency beyond a typical tool description.

    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 three dense sentences: purpose first, then method details, then payment terms. No filler, and the most important decision-relevant info (safe to trade, payment) is prominently placed.

    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?

    For a single-parameter tool with no output schema, the description fully covers what input is needed, what operations will be performed, what result shape to expect, and the cost constraint. An agent can decide whether to use it and invoke it correctly. The only minor omission is how the x402 payment is initiated, but that is likely handled outside the tool call.

    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?

    The schema already documents token_address as 'Base ERC-20 contract address (0x...)' with 100% coverage. The description reinforces the Base network context but adds no new parameter-level meaning, so it stays at the schema-coverage 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 opens with a clear verb (checks), resource (token on Base), and purpose (safe to trade before buying or swapping). It then enumerates the specific checks performed, making the scope concrete. No sibling differentiation is needed because no sibling tools exist.

    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 states when to use: before buying or swapping a Base token. It does not mention alternatives or exclusions, but with no sibling tools that is not a significant gap. The payment requirement also implies a usage consideration, which is useful context.

    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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  • Evaluate tool definition quality.

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