YieldSignal

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

With no annotations provided, the description fully discloses behavioral traits: data source (protocol's own books), conditional behavior when amountUsd is passed, handling of protocols without borrowed-vs-supplied data (marked unmeasured, never recommended as executable), and asset-specific limitations (WETH lacks USD oracle). This is comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded, with four sentences each conveying distinct information: core function, motivation, unmeasured protocols, and asset constraints. It avoids redundancy, though it could be slightly tighter.

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

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

The description covers core outputs, edge cases, and limitations. Since there is no output schema, it explains the nature of results (utilization, liquidity, exit verdict) but does not specify the exact response structure. Given the tool's moderate complexity, this is adequate but leaves some structural detail unspecified.

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

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

Although the schema covers parameter names and basic types, the description adds crucial semantics: asset determines USDC full figures vs WETH utilization-only, and amountUsd triggers an exit verdict. It explains the consequences of omitting amountUsd and clarifies asset-specific behavior, surpassing the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as measuring exit capacity for Base lending markets, specifying outputs such as utilization, withdrawable liquidity, and an optional exit verdict. It does not explicitly contrast with sibling yield tools, but the distinct focus on exit capacity makes its purpose unambiguous.

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

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

The description provides context for when to use the tool: to separate advertised yield from actual ability to exit, noting that high APY at high utilization means a market won't let you out. It does not explicitly mention alternatives or exclusions, but the intended use case is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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

With no annotations provided, the description carries the full burden and does so excellently. It discloses that readings are live, provides a concrete example, notes which protocols are unmeasured and why, and explicitly states the tool does not predict utilization movement. This is rich, non-obvious behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a strong purpose statement and every subsequent sentence adds critical detail (return contents, example, protocol limitations, caveat). No wasted words; it is dense but appropriately sized for the tool's complexity.

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

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

Given there is no output schema, the description thoroughly explains what is returned (utilization, kink, headroom, borrow APY, multiplier) and covers edge cases (unmeasured protocols, non-prediction). It is complete enough for an agent to understand the tool's behavior without additional context.

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

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

Schema description coverage is 100% and the single parameter (asset) is fully documented with enum and default. The description adds minimal extra meaning beyond calling it a 'Base lending market' and 'stress-test', so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool measures proximity to a lending market's kink where borrow rates explode, with a specific verb ('How close... is') and resource ('Base lending market'). It distinguishes itself from sibling tools by focusing on rate sensitivity rather than exit capacity or yield signals.

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

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

It explicitly identifies applicable protocols (Aave and Compound only) and explains why others are excluded, which serves as when-not guidance. It also clarifies that it describes current state, not a prediction. However, it does not explicitly mention alternative sibling tools for comparison, so it stops short of full differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it exceeds that burden. It details how the tool attributes exposure per protocol, uses live data ('Measured live'), and even explains why Aave is reported unattributed (splitting would imply non-existent diversification). It also highlights the tool's unique value proposition (two-hop contagion) and its limitation (not a correlation estimate). This is rich behavioral context beyond what any schema or annotation could provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than most, but it is well-structured: a clear definition, a vivid motivating example, and a concise methodology section. The Stream Finance anecdote is illustrative but arguably non-essential; the final sentence 'Structural shared exposure, not a correlation estimate' is a strong close. It is front-loaded with the core purpose, and every remaining sentence adds meaningful context. It is slightly overlong but never redundant.

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

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

Given the tool's complexity, no annotations, and no output schema, the description is remarkably complete. It covers the core inputs, the methodology per protocol, the rationale behind those methodologies, and concrete examples that clarify expected output. It does not describe a return schema, but the description's phrase 'per factor, how much of your capital sits behind...' gives enough shape. The tool's limitations and boundaries are also disclosed, making it fully contextualized for an agent.

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

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

The schema already covers both parameters with 100% coverage, each having a clear description and example. The tool description does not add significant meaning to individual parameters; it mentions the positions format ('protocol:usd pairs') but the schema already does. The conceptual explanation of how capital is mapped to risk factors is valuable but not parameter-specific. Given the high schema coverage, a baseline 3 is appropriate, and the description's marginal contribution does not push it higher.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Shared risk exposure across a declared portfolio' and 'returns, per factor, how much of your capital sits behind the same collateral, price oracle or vault curator.' It uses a specific verb ('returns') and resource ('shared exposure'), and the examples ('Morpho USDC vault... 93.7% behind cbBTC vs Compound's 43.1%') make the purpose unmistakable. It also implicitly distinguishes itself from sibling tools (exit capacity, rate sensitivity, yield signals) by focusing on structural risk rather than yield or liquidity.

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

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

The description explicitly tells when to use this tool: 'This is the question the market does not answer: depeg and hack alerts tell you an event happened, not whether you are two hops from it.' It also tells when not to use it: 'Structural shared exposure, not a correlation estimate.' The methodology section explains the treatment of each protocol (Morpho per isolated market, Compound by posted collateral, Aave unattributed), giving clear guidance on what to expect and how to interpret results.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses deterministic behavior, EIP-712 signing, and reproducibility via re-run. It does not explicitly state read-only or destructive traits, but the context suggests read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each earning its place: purpose, outputs, determinism, comparison to sibling. Front-loaded and efficient.

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

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

Given no output schema and no annotations, the description explains return values (net gain, break-even days, confidence tier) and deterministic nature. It could mention how confidence tier works, but overall sufficient.

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

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 mention parameters generically but does not add new meaning beyond what the schema already provides for each parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a MOVE/HOLD decision based on position, size, move cost, and horizon. It distinguishes itself from the sibling 'plain signal tool' by emphasizing it sells the decision, not raw data.

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

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

The description implies when to use this tool over the sibling ('Priced above the plain signal tool'), but does not explicitly state alternative use cases or when not to use it. More direct guidance would improve clarity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full disclosure burden. It explicitly states the decomposition limitation, that undecomposable protocols are never assumed incentive-free, that no ranking claim is made in such cases, and describes the bestVerifiableFloor return field. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-organized: a leading question, a breakdown of outputs, a key limitation, and a clarifying note about stress testing vs forecasting. Every sentence adds unique value with no fluff.

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

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

Given the tool's moderate complexity (one optional param, no output schema), the description covers behavior, edge cases, limitations, and key return values comprehensively. It is self-contained enough for an agent to select and invoke the tool correctly.

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

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

The schema already fully documents the single optional asset parameter with its enum and default. The description does not add new parameter details beyond what the schema provides, but it does add context about the tool operating on 'each protocol's yield' within the chosen market. Baseline 3 is appropriate given 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear, specific question ('How much of the current APY survives if incentives stop?') and then details exactly what the tool computes: base vs reward yield, post-incentive floor, and leader changes. This differentiates it from sibling tools about exit capacity, decisions, or signals.

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

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

The description makes the tool's purpose very clear and adds a crucial usage caveat: it's a stress test of current readings, not a forecast. It does not explicitly name alternative tools or state when not to use it, but the behavioral scope is well-defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description fully discloses key behavioral traits: signed output (EIP-712), source tagging (never estimated), different chains/categories, and verification mechanisms (on-chain registration, EAS attestations). This is highly transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long but each sentence adds value. It is well-structured, front-loading the core functionality before detailing verification aspects. Minor redundancy could be trimmed, but overall concise for the complexity.

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

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

Given the complexity (multiple sources, chains, signed data), the description covers key aspects: asset categories, sources, verification, and offline use. It lacks explicit return format details, but the context of 'yield signal' and 'signed data' suffices for an agent.

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

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

The input schema has one parameter with enum and 100% description coverage. The description adds meaningful context by explaining what each enum value represents (e.g., ETH_STAKING = liquid staking APY on Ethereum mainnet) and notes the default (USDC), enhancing understanding beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches a real-time risk-weighted yield signal for specific assets (USDC, WETH lending on Base; ETH_STAKING on Ethereum mainnet). It distinguishes between lending and staking categories with explicit chains and sources, making the purpose unambiguous.

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

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

The description explains when to use each asset type but does not provide guidance on when to use this tool versus its sibling get_yield_decision. It implies this tool retrieves raw signals while the sibling may handle decisions, but this is not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.