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Coinversaa

Coinversaa Pulse

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live_cohort_bias

Reveal the net long/short bias for each trader cohort (smart money, whales, etc.) on any coin. Know what different tiers are doing right now.

Instructions

See what each trader cohort is doing on a specific coin RIGHT NOW. Returns the net long/short bias for every tier (money_printer, smart_money, whales, etc.) on the given coin. Answers questions like 'are the smart money traders long or short ETH?'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
useToonFormatNoReturn data in compact toon format (default: true). Set to false for standard JSON.
coinYesCoin symbol (e.g. BTC, ETH, SOL). For builder dex markets use prefix:COIN (e.g. xyz:SILVER, km:OIL, cash:TSLA)

Implementation Reference

  • src/index.ts:711-723 (registration)
    Registration of the 'live_cohort_bias' tool via server.registerTool. It registers the tool with input schema (useToonFormat + coin) and a handler that calls the API endpoint /live/cohort-bias/{coin}.
    // TOOL 16: Cohort Bias
    // ══════════════════════════════════════════════════════════
    if (shouldRegister("live_cohort_bias")) server.registerTool(
      "live_cohort_bias",
      {
        description: "See what each trader cohort is doing on a specific coin RIGHT NOW. Returns the net long/short bias for every tier (money_printer, smart_money, whales, etc.) on the given coin. Answers questions like 'are the smart money traders long or short ETH?'",
        inputSchema: {
          useToonFormat: useToonFormatSchema,
          coin: z.string().min(1).max(20).describe("Coin symbol (e.g. BTC, ETH, SOL). For builder dex markets use prefix:COIN (e.g. xyz:SILVER, km:OIL, cash:TSLA)"),
        },
      },
      async ({ useToonFormat, coin }) => toolResult(await callAPI(useToonFormat, `/live/cohort-bias/${normalizeCoin(coin)}`))
    );
  • Handler function for live_cohort_bias. It takes the coin parameter, normalizes it, and calls the backend API at /live/cohort-bias/{normalizedCoin}. The result is passed through toolResult which formats it as JSON text content.
    async ({ useToonFormat, coin }) => toolResult(await callAPI(useToonFormat, `/live/cohort-bias/${normalizeCoin(coin)}`))
  • Input schema for live_cohort_bias. Defines two inputs: useToonFormat (optional boolean, default true) and coin (required string, 1-20 chars, with description about coin symbol format including builder dex support).
    {
      description: "See what each trader cohort is doing on a specific coin RIGHT NOW. Returns the net long/short bias for every tier (money_printer, smart_money, whales, etc.) on the given coin. Answers questions like 'are the smart money traders long or short ETH?'",
      inputSchema: {
        useToonFormat: useToonFormatSchema,
        coin: z.string().min(1).max(20).describe("Coin symbol (e.g. BTC, ETH, SOL). For builder dex markets use prefix:COIN (e.g. xyz:SILVER, km:OIL, cash:TSLA)"),
      },
Behavior3/5

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

No annotations provided, so description carries full burden. It implies a read operation returning real-time bias data. Does not mention data freshness, latency, or permissions, but is adequate for a straightforward query tool.

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?

Three concise sentences with no fluff. Front-loaded purpose, then return detail, then example. Every sentence adds value.

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?

Given no output schema or annotations, the description sufficiently explains input and output (net long/short bias per cohort). Could mention output format details but example question compensates.

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%, so description adds minimal extra meaning to parameters. The 'coin' parameter is illustrated with examples, and 'useToonFormat' is explained in schema. Baseline 3 is appropriate.

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 returns net long/short bias per trader cohort for a given coin, with an example query. It distinguishes from siblings like live_cohort_bias_history by emphasizing 'RIGHT NOW'.

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?

Provides a concrete example of when to use (e.g., checking smart money bias on ETH). Lacks explicit 'when not to use' or comparison to siblings, but the context is clear.

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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