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wot_influence

Simulate the network impact of following or unfollowing pubkeys by visualizing ripple effects using differential PageRank analysis.

Instructions

Simulate what happens if one pubkey follows/unfollows another. Shows ripple effect across the network using differential PageRank.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pubkeyYesThe pubkey taking the action
otherYesThe pubkey being followed/unfollowed
actionNofollow or unfollowfollow
payment_hashNoPayment hash if free tier exhausted

Implementation Reference

  • The handler for the wot_influence tool. It calls wotGet with the provided parameters and formats the result.
    async ({ pubkey, other, action, payment_hash }) => {
      const data = await wotGet("/influence", { pubkey, other, action }, payment_hash);
      return textResult(formatL402(data));
    }
  • The Zod schema defining the input parameters for the wot_influence tool.
    {
      pubkey: z.string().describe("The pubkey taking the action"),
      other: z.string().describe("The pubkey being followed/unfollowed"),
      action: z.enum(["follow", "unfollow"]).default("follow").describe("follow or unfollow"),
      payment_hash: z.string().optional().describe("Payment hash if free tier exhausted"),
    },
  • src/index.ts:242-255 (registration)
    The registration of the wot_influence tool using the server.tool method.
    server.tool(
      "wot_influence",
      "Simulate what happens if one pubkey follows/unfollows another. Shows ripple effect across the network using differential PageRank.",
      {
        pubkey: z.string().describe("The pubkey taking the action"),
        other: z.string().describe("The pubkey being followed/unfollowed"),
        action: z.enum(["follow", "unfollow"]).default("follow").describe("follow or unfollow"),
        payment_hash: z.string().optional().describe("Payment hash if free tier exhausted"),
      },
      async ({ pubkey, other, action, payment_hash }) => {
        const data = await wotGet("/influence", { pubkey, other, action }, payment_hash);
        return textResult(formatL402(data));
      }
    );
Behavior3/5

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

Discloses simulation nature (safe, read-only) and algorithm (PageRank) which is crucial given no annotations. However, fails to mention the free tier/payment constraint indicated by the payment_hash parameter schema, leaving a gap in behavioral disclosure since annotations are absent.

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 sentences with zero waste. First sentence establishes the core simulation scenario; second sentence establishes the analytical method and scope. Every word earns its place.

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

Completeness3/5

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

Adequate given 100% input schema coverage, but gaps remain: no output schema exists yet the description only vaguely hints at output ('Shows ripple effect') without indicating return format (scores, rankings, probabilities). Also omits the payment/free tier constraint.

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

Parameters4/5

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

Despite 100% schema coverage (baseline 3), the description adds value by binding parameters into a narrative: 'one pubkey follows/unfollows another' contextualizes how pubkey, other, and action relate semantically. This exceeds the isolated parameter descriptions in the schema.

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: specifies 'Simulate' as the action, the resource (network effects of follow/unfollow), the scope (ripple effect across network), and methodology (differential PageRank). Distinguishes from siblings like wot_predict_link and wot_score by focusing on hypothetical scenario modeling rather than prediction or current-state scoring.

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?

The term 'Simulate' implies hypothetical usage (previewing before actual action), but lacks explicit when-to-use guidance versus similar siblings like wot_predict_link or wot_follow_quality. No explicit alternatives or exclusions are named.

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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