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Degenscan Intel

by io.github.tradewr333-lgtm
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Funding alerts

funding_alerts
Read-onlyIdempotent

Which perps have extreme funding RIGHT NOW on Hyperliquid: sorted by |hourly rate| with annualized %, which side is paying (crowded longs vs shorts), open interest and predicted next funding per venue (Hyperliquid, Binance, Bybit). Use every 5–15 min to detect crowded positioning or to pick a side to receive funding. $0.001/call. Use to find which perps have extreme funding now (cross-venue); for every Hyperliquid dex incl. HIP-3 use carry_funding_matrix.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "funding_alerts"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
min_abs_rate_1hNoAlert threshold on |hourly funding|. Default 0.0003 (=0.03%/h ≈ 263%/yr).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered. The description adds genuinely useful context the annotations don't: a $0.001/call cost and the 5–15 min refresh cadence, plus what is returned (annualized %, which side pays, OI, predicted next funding per venue).

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?

Dense but front-loaded: purpose, then cadence, then the routing alternative. Every clause carries information, though the single long sentence would read better split. No filler.

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?

No output schema exists, so the description appropriately enumerates the return fields (hourly/annualized rate, paying side, OI, predicted next funding across venue). Combined with the alternative-tool routing, an agent has almost everything needed, with only the limit parameter left unexplained.

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 50%: min_abs_rate_1h is documented in the schema (threshold semantics and default), but limit has no description anywhere. The description's mention of sorting by |hourly rate| implies a threshold notion but never explains limit, so it only partially compensates for the gap. 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?

Opens with a specific verb+resource+scope: which perps have extreme funding right now on Hyperliquid, sorted by |hourly rate|. It explicitly distinguishes itself from carry_funding_matrix for every Hyperliquid dex incl. HIP-3, so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit cadence and purpose ('Use every 5–15 min to detect crowded positioning or to pick a side to receive funding') and names the alternative tool plus the condition that selects it (carry_funding_matrix for every dex incl. HIP-3). Nothing is left to inference.

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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