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wot_sybil_check

Detect Sybil attacks on Nostr pubkeys by analyzing follower quality, mutual trust, follow diversity, temporal patterns, and community integration to classify accounts as genuine or suspicious.

Instructions

Run 5-signal Sybil detection on a Nostr pubkey. Analyzes follower quality, mutual trust ratio, follow diversity, temporal patterns, and community integration. Returns classification: genuine, likely_genuine, suspicious, or likely_sybil.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pubkeyYesNostr public key in hex format
payment_hashNoPayment hash if free tier exhausted

Implementation Reference

  • The handler for the wot_sybil_check tool, which calls the /sybil endpoint on the WOT API.
    async ({ pubkey, payment_hash }) => {
      const data = await wotGet("/sybil", { pubkey }, payment_hash);
      return textResult(formatL402(data));
    }
  • src/index.ts:137-148 (registration)
    Registration of the wot_sybil_check tool with the MCP server.
    server.tool(
      "wot_sybil_check",
      "Run 5-signal Sybil detection on a Nostr pubkey. Analyzes follower quality, mutual trust ratio, follow diversity, temporal patterns, and community integration. Returns classification: genuine, likely_genuine, suspicious, or likely_sybil.",
      {
        pubkey: z.string().describe("Nostr public key in hex format"),
        payment_hash: z.string().optional().describe("Payment hash if free tier exhausted"),
      },
      async ({ pubkey, payment_hash }) => {
        const data = await wotGet("/sybil", { pubkey }, payment_hash);
        return textResult(formatL402(data));
      }
    );
Behavior4/5

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

Without annotations, the description carries significant burden and delivers: it discloses the 5 specific analysis dimensions and the 4 possible classification outputs (filling the gap of no output schema). However, it omits discussion of the payment/free tier model implied by the payment_hash parameter.

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?

Two efficient sentences with zero waste. First sentence establishes the operation and target; second sentence details the methodology and return values. Information density is optimal.

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

Completeness4/5

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

For a 2-parameter tool with no output schema, the description adequately compensates by enumerating the classification return values. It explains what the analysis entails (5 signals) but could strengthen completeness by noting the optional payment requirement when free tier is exhausted.

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% (both pubkey and payment_hash have descriptions), establishing baseline 3. The description adds no additional parameter semantics beyond what the schema already provides, but none is needed given complete schema coverage.

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?

Excellent clarity with specific verb 'Run', resource 'Nostr pubkey', and distinct scope '5-signal Sybil detection'. The five specific signals listed (follower quality, mutual trust ratio, etc.) clearly differentiate this from siblings like wot_follow_quality or wot_score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides implied usage through the Sybil detection focus and classification outputs, but lacks explicit guidance on when to choose this over wot_anomalies or wot_score. No explicit when-not or alternative recommendations provided.

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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