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

Beyond the read-only annotations, it discloses that junk-farm filters are applied, that the tool ranks by rate alone, and that fees and rate durability should be checked elsewhere. It leaves minor gaps around exact filter thresholds and no-result behavior.

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 purpose is front-loaded in the first sentence, and each subsequent sentence adds useful routing or caveat information. There is no redundant repetition of the title or schema.

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?

For a simple one-parameter lookup with read-only annotations, the description explains what is returned at a high level, how results are filtered, and what to verify before acting. It could be slightly more complete by mentioning fallback behavior when no pool qualifies.

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 coverage is 100%, and the single `asset` parameter is already well documented as an asset symbol. The description adds only the framing that the asset is the subject of a 'best rate for X' question, 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 states a specific verb and resource: it returns the single highest-APY pool on Base for one asset, with explicit filtering. This clearly distinguishes it from list-oriented siblings such as list_base_yields and list_lending_vaults.

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 gives a clear use case ('where is the best rate for X') and names follow-up tools (`market_profile`, `yield_history`) before committing capital. It does not explicitly state when not to use it or compare it directly to list_base_yields as an alternative.

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?

Annotations already declare readOnly/openWorld/non-destructive, so the bar is lower. The description adds meaningful behavior beyond that: results are pre-filtered to exclude 'obvious traps' (minimum TVL, APY ceiling) and explicitly framed as market data rather than a recommendation, which sets agent expectations about result trustworthiness.

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?

Three tightly packed sentences, front-loaded with the core purpose, then routing, then the filtering caveat. Nothing is redundant and every clause carries information.

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?

No output schema exists, but the description compensates by naming the returned fields (APY, TVL, project, asset) and the `pool` id usable with `yield_history`. Combined with the pre-filtering note, an agent has everything needed to call and interpret results.

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 the schema already documents all four parameters fully. The description adds no additional parameter information (no filter interaction or default behavior detail), so baseline 3 applies.

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?

States a specific verb and resource ('Survey staking and lending opportunities on Base') and enumerates the returned fields (APY, TVL, project, asset). It clearly distinguishes itself from best_yield by scope: broad landscape vs. single-asset best option.

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?

Explicitly names the alternative `best_yield` with the condition that selects it ('when one asset's single best option is wanted'), and also points to `yield_history` for drill-down via the pool id. When-to-use and cross-tool routing are both covered.

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?

Annotations already declare read-only, non-destructive, open-world behavior. The description adds meaningful context beyond that: it discloses the underlying protocols, that rates are read directly from contracts, that some markets may be missing from aggregators, and that Morpho entries include a risk block with specific risk dimensions.

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 three dense sentences with no wasted text. It front-loads the tool's purpose and then details the returned data, though the opening phrase is slightly abstract compared with the concrete protocol and field details that follow.

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?

There is no output schema and no input schema, so the description must explain what the tool returns. It does so thoroughly: protocol coverage, deposit address, asset address and decimals, Morpho risk fields, and the source of Aave/Moonwell rates. This is sufficient for an agent to call 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 tool has zero input parameters, so parameter semantics carry little burden. The description appropriately focuses on returned fields rather than inputs, and the baseline for a zero-parameter tool is 4.

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 resource: Base lending markets across Morpho, Aave V3, and Moonwell, each with a deposit contract address and asset metadata. It distinguishes the tool as returning deposit-ready markets rather than generic yield data, though it does not explicitly name sibling tools or contrast itself with them.

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?

Usage is implied by phrases like 'deposit-ready' and 'an agent can actually act on', and by the `depositTo` field, suggesting this is for finding where to deposit. However, it does not explicitly state when to use this versus `list_base_yields`, `best_yield`, or `market_profile`.

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?

Annotations already cover read-only, non-destructive, open-world operation, so the bar is lower. The description still adds real behavioral context: missing-data signals report null and are excluded from weighting rather than guessed, weights are exposed so the score can be recomputed, and the output is backward-looking 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?

The purpose and use case are front-loaded in the first sentence, and the remaining text is spent enumerating the six signals and the null-handling rule, each of which earns its place given there is no output schema. No filler sentences.

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?

With no output schema, the description carries the full burden of explaining return values, and it does: score range, band values, the signals object contents, per-signal raw value and sub-score, method.weights, and null behavior. An agent can call this and interpret the response without further information.

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 coverage is 100% and the single `pool` parameter is fully documented in the schema with format examples. The description only indirectly scopes it ('one Morpho vault or Aave reserve'), so it adds little beyond what the schema already provides; baseline 3 applies.

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?

States a specific resource and scope: a risk profile for one Morpho vault or Aave reserve, returned as score, band and a signals breakdown. It is clearly distinguishable from the sibling yield tools (list_base_yields, best_yield, yield_history), which surface rates rather than risk.

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?

Gives a clear usage context ('for use before committing capital') and an explicit pairing rule ('Pair with `yield_history` for an unusually high rate'), which routes the agent to a sibling. It also bounds expectations with 'Describes what has happened, not what will,' but offers no explicit when-not-to-use case.

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?

Annotations already declare readOnly, idempotent, non-destructive, so the safety profile is covered; the description adds genuine value by disclosing the interpretation hazard that a short `observations` series should not be read as stability. It does not cover cost, rate limits, or pagination, so it is useful context rather than complete disclosure.

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?

Three sentences, each doing distinct work: what it returns, why it beats a live rate, and a caveat on sparse data. The most actionable instruction (call before acting on a high rate) is front-loaded in the middle of the text without preamble.

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?

With no output schema, the description carries the burden of describing returns and does so concretely (daily series plus min/median/max/volatility and an `observations` count). For a two-parameter read-only tool, nothing an agent needs to call it correctly is missing.

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%, with `pool` even documenting its source in list_base_yields and list_lending_vaults, so the baseline is 3. The description adds no further syntax or format detail for `days` or `pool` beyond what the schema already states.

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?

States a specific resource (daily APY and TVL history for one market) plus the derived statistics returned (min, median, max, volatility), which makes its role unambiguous. It also carves out its niche against the live-rate siblings (list_base_yields / best_yield) by framing itself as the tool that answers what 'a live rate cannot.'

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?

Gives a clear triggering condition: 'Call it before acting on any unusually high rate,' and explains the question it answers (typical vs. spike). It does not explicitly name an alternative sibling or state a when-not case, so it stops short of full routing guidance.

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