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YieldSignal

by io.github.Stakemate369

get_leadership_persistence

How long this service's own leadership calls actually hold, measured from its hourly on-chain attestations (EAS on Base). Returns the median duration a protocol stayed the best pick, survival rates at 6/24/72h, how much of the rotation is the same two protocols trading places, and what chasing the leader is worth per $10k before gas. Measured over 24 days: the WETH leader never changed once, the ETH staking leader changed every ~26h, the USDC leader every ~2h with half the switches being a round trip between the same pair — three assets sold at one price with wildly different reliability. Also answers, empirically, whether a wider lead lasts longer: it does not (Spearman -0.04 over 177 completed spells), so gap size is not a usable proxy for confidence. Every input is a public attestation UID: recompute it yourself from base.easscan.org and get the same number. Observed history with sample sizes attached, never a forecast — assets whose lead has not changed in the window are reported as a floor with a censoring flag, never as a median.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetNoWhich market's leadership durability to measure: USDC/WETH lending on Base, or ETH_STAKING liquid staking. Defaults to USDC.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: data source (EAS on Base), sample sizes, censoring flags, the empirical finding (Spearman -0.04), and explicitly states it is never a forecast. This is exemplary transparency.

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?

The description is longer than average, but each sentence contributes meaningful context—methodology, sample sizes, and interpretation. A few example findings could be trimmed without loss, but the structure is coherent and front-loaded with the core purpose.

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

Completeness5/5

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

Despite having no output schema, the description enumerates all key return components (median, survival rates, rotation measure, value before gas, censoring flag) and clarifies edge cases. It is fully self-sufficient for an agent to understand what the tool returns and how to interpret it.

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?

The schema provides 100% coverage for the only parameter (asset) with an enum and description. The tool description adds no additional parameter details beyond what the schema already states, so 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 measures how long leadership calls hold, listing specific outputs (median duration, survival rates, rotation ratio, value per $10k). It distinguishes itself from sibling metrics like get_yield_durability by focusing on leadership persistence and empirical reliability.

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 rich context, such as the 24-day measurement window and the methodology, making the intended use clear. However, it does not explicitly compare against alternative tools or state when not to use it, so it falls short of a 5.

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

A4.5/5.0
Disambiguation5/5

Each tool addresses a distinct aspect of yield analysis: signal provides raw data, durability stress-tests incentive dependence, decision recommends an action, and exit capacity evaluates liquidity. There is no overlap; an agent can clearly select the right tool for the task.

Naming Consistency5/5

All tools follow a consistent get_<yield_>?<specific> pattern, using snake_case and a uniform verb. get_exit_capacity fits the pattern with 'get_' and a clear noun even without 'yield' in the name, maintaining predictability.

Tool Count5/5

Four tools is a well-scoped set for a focused yield analytics server. Each tool earns its place by covering a distinct part of the decision workflow without redundancy or unnecessary bloat.

Completeness5/5

The tool set covers the full yield analysis lifecycle: raw signal, durability under incentive changes, decision recommendation, and exit liquidity. There are no obvious dead ends; an agent can go from data to action to exit assessment without missing critical operations.

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