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EchoLedger

by io.github.icmoore

DetectRugSignals

Detect rug-pull signals on a Uniswap V2 or V3 pool via three threshold checks: suspiciously low TVL, top-LP concentration above a limit, and inactive-pool-with-liquidity. Composes over CheckPoolHealth and returns per-signal booleans plus a count-based risk level (low/medium/high/critical). The inactive-with-liquidity signal is V2-only; V3 pools report False for it with a note in details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rpc_urlYesAn Ethereum (or L2) JSON-RPC URL used to read live pool state. Required; supplied by you per call (BYO-RPC) and may carry your provider key. The endpoint stores and logs nothing — the URL is never persisted or written to logs.
chain_idNoOptional guard. If supplied and the RPC reports a different chain id, the call is rejected. Defaults to 1 (Ethereum mainnet) conceptually; omit to skip the check.
pool_typeYesWhich protocol the pool at pool_address belongs to. This tool accepts uniswap_v2 | uniswap_v3.
tvl_floorNoMinimum acceptable TVL in token0 numeraire. Values at or below fire the tvl_suspiciously_low signal. Default 10.0 is nominal; override for your pair.
block_numberNoOptional block number to pin the read to a historical block. Omit to read the latest block.
pool_addressYesOn-chain address of the pool/pair to analyze (Uniswap V2/V3 pair, Balancer weighted pool, or Curve stableswap pool). Required. Lowercase, uppercase, or checksum casing all work.
lp_concentration_thresholdNoTop-LP share (strict-greater-than) that triggers the concentration signal. In (0, 1]; default 0.90; pass 1.0 to disable.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description discloses key behavior: it returns per-signal booleans, a count-based risk level, and the V2-only restriction for inactive-pool-with-liquidity. It also notes it composes over CheckPoolHealth. However, it does not explicitly state side-effect-free behavior, but that is implied and the transparency is strong.

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 sentences, front-loaded with the core purpose, and each sentence adds essential value: listing checks, specifying output and composition, and clarifying V2-only behavior. No redundancy.

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?

The description covers the main logic, output type, and V2 nuance, which is sufficient for a tool with no output schema. It doesn't document every parameter or external requirement (like needing RPC URL), but those are in the schema. Overall, it gives a clear picture of functionality and expectations.

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 description coverage is 100%, so baseline is 3. The description adds some meaning by linking signals (low TVL, top-LP concentration, inactive pool) to parameters, but it does not provide detailed parameter semantics beyond what the schema already offers. It adds context but not extensive new info.

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's function: detecting rug-pull signals on Uniswap V2/V3 pools via three specific threshold checks. It uses a specific verb ('Detect') and resource, and its composition over CheckPoolHealth distinguishes it from sibling tools.

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 provides clear context for when to use it (when rug-pull signals are needed) and notes it composes over CheckPoolHealth, but it does not explicitly state exclusions or alternatives. The intended use case is clear, though not exhaustive.

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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TDQS

A4.2/5.0
Disambiguation4/5

Tools are mostly distinct by protocol and action, but AnalyzePosition and the protocol-specific Analyze*LP tools overlap in purpose, as do SimulatePriceMove and Simulate*Move. Descriptions clearly differentiate them, so an agent can select correctly with careful reading, but the generic versus specific split introduces some ambiguity.

Naming Consistency5/5

All tool names follow a consistent VerbNoun pattern (Analyze, Assess, Build, Calculate, Check, Detect, Simulate) with no mixing of conventions. The naming is uniform and predictable across the entire set.

Tool Count5/5

11 tools is well within the ideal range for a specialized LP analysis server. Each tool covers a distinct function across protocols, and none feel redundant or superfluous. The count is well-scoped for the server's purpose.

Completeness4/5

The tool set covers the full lifecycle of LP analysis: position PnL analysis, price simulations, depeg risk, slippage, pool health, rug detection, and state serialization. Minor gaps exist, such as fee attribution for Balancer and Curve pools and a generic simulate tool for all protocols, but these are protocol limitations or acceptable omissions.

Resources