Gas Fee Predictor

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

No annotations are provided, so the description carries full burden. It discloses the return contents (recommendation, savings, next window) which is helpful, but says nothing about data freshness, prediction reliability, latency, or whether this is a read-only query. Some behavioral context is present but not rich.

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 a single well-structured sentence that front-loads the core decision ('now or wait'), then lists the return values. No wasted words, though it could benefit from a short usage note.

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 zero-parameter, no-output-schema, advisory tool, the description sufficiently tells the agent what it returns and its decision-support purpose. It lacks explicit usage guidance and behavioral details like data recency, but is adequate given the tool's simplicity.

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 takes zero parameters, so the baseline is 4 per the scoring rubric. There are no parameter semantics to clarify via the description.

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 a specific purpose — determine whether to send a transaction now or wait — and enumerates what the tool returns (recommendation, expected savings, next cheaper gas window). This is distinct from siblings like get_current_gas and get_gas_forecast which provide raw data rather than a recommendation.

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?

There is no explicit when-to-use vs when-not-to-use guidance, no named alternatives, and no statement of prerequisites or context conditions. The implied usage is clear from the name and description (agent deciding whether to transact now), but nothing routes the agent away from or toward siblings explicitly.

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?

No annotations are provided, so the description carries the full burden. It does convey meaningful behavior — the estimate is 'current' (live data), denominated in USD, and varies by priority tier — but it is silent on safety profile (read-only, no side effects), auth requirements, and the shape of the returned estimate. Adequate but with real gaps.

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?

One front-loaded sentence that names the computation, the enumeration of valid actions, the custom fallback, and the tier modifier with no filler or repetition.

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 3-parameter read-style estimator with no output schema and no annotations, the description covers the essential inputs and the estimation basis. It would be fully complete only if it stated the read-only/no-side-effect nature or the return shape, but nothing critical to invoking 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%, and the schema already documents tier semantics, the action enum, and the custom/gas_units dependency, so the baseline is 3. The description only reinforces the tier concept ('chosen priority tier') and re-lists the actions already in the enum, adding little beyond the 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?

States a specific verb (Estimate) and resource (USD gas cost of a named Ethereum mainnet action) and enumerates the supported action set, plus the custom gas-units fallback. It is clearly distinguishable from get_current_gas and get_gas_forecast in intent, though it never names a sibling to sharpen the distinction.

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 the description ('estimate cost of a specific action at a chosen tier'), but there is no explicit when-to-use guidance and no exclusion relative to get_current_gas or best_time_to_transact, which also touch gas-price data. The agent must infer the boundary itself.

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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return contents and that a send-now-vs-wait recommendation is included, but says nothing about data freshness/source, caching, or rate limits — relevant for a 'current' price tool. The 'get' verb implies a read, which is the only safety signal.

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?

Two tight sentences, zero filler, with the core capability front-loaded and the usage cue second. 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?

For a no-param, read-only tool with no output schema or annotations, the description covers the essential return shape and intent-matching cue, which is what an agent needs to select and call it. It omits freshness/source of the 'current' values and does not resolve the ETH/USD overlap with get_eth_price.

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 takes zero parameters, so the baseline is 4. The description usefully names the value tiers returned (low/average/high, Gwei), which is output semantics rather than parameter semantics, but there is nothing else for it to document.

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 ('Get the current Ethereum mainnet gas price') and adds the return payload (low/average/high in Gwei, ETH/USD, recommendation). The qualifiers 'current' and 'mainnet' implicitly separate it from get_gas_forecast, get_gas_history, and get_l2_gas, but it overlaps with get_eth_price on the ETH/USD output without explaining the distinction, so sibling differentiation is incomplete.

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 explicit routing context: 'Use this to answer "what is the gas fee right now".' That tells the agent the user intent it serves. It stops short of naming alternatives or when-not-to-use (e.g., forecast or L2 queries), so it is context without exclusions.

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?

No annotations are provided, so the description carries the full behavioral burden. It discloses the return payload (spot price plus 24h change) and 'Get' implies a safe read, but it says nothing about data source, refresh cadence/staleness, rate limits, or error 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?

A single front-loaded sentence that names the resource and the two values returned, with no wasted words. Appropriate size for a trivial zero-arg lookup.

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 no-parameter, no-output-schema tool the description covers what is returned, which is the main thing an agent needs. Minor gaps remain around data freshness and source, but nothing blocking invocation.

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 takes no parameters, so the schema requires no explanation and the baseline is 4. The description correctly omits parameter discussion rather than padding it.

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 ('Get'), a specific resource ('ETH/USD spot price'), and an additional data point ('24-hour change'). None of the siblings (gas and transaction-cost tools) overlap with price retrieval, so an agent can select this unambiguously.

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?

There is no explicit when-to-use or when-not-to-use guidance and no alternatives named, but the usage is strongly implied by the name and the gas/price split among siblings. For a zero-parameter read tool this is adequate but not instructive.

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 the full burden, and it does disclose the meaningful trait: this is a read-only retrieval bounded to a 24-hour horizon that also computes a cheapest window. It omits any note on auth requirements, refresh/prediction cadence, or behavior at the boundary, but the operation is low-risk and self-evident from 'Get'.

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?

Two sentences, zero filler, with the resource and horizon front-loaded and usage triggers trailing. Every clause earns its place.

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, so the description must carry return semantics, and it does characterize the payload as hourly forecast data plus a predicted cheapest window. It leaves the exact shape of the hourly values unspecified, but for a simple read-only forecast tool this is nearly complete.

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 `hours` parameter already documents its 1-24 range and default of 12. The description only restates the 24-hour ceiling already implied by the schema, adding no syntax or interpretation value beyond it. Baseline 3 applies when the schema does the heavy lifting.

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+resource ('Get the forward-looking hourly Ethereum gas forecast') with an explicit horizon (up to 24 hours) and return scope ('predicted cheapest window'). The 'forward-looking' framing cleanly separates it from get_current_gas and get_gas_history without needing to name 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?

Gives concrete user-intent triggers ('when will gas be cheaper', 'should I wait until tomorrow', planning a transaction), which is strong context. It stops short of naming the overlapping sibling best_time_to_transact or stating when this forecast is the wrong choice, so it is clear but not fully disambiguating.

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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return contents (min/max/average) and the fact that it is a look-back query, but says nothing about data granularity, freshness/lag, rate limits, or whether an empty window returns an error.

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?

Two sentences, zero filler. The capability statement is front-loaded and the use cases follow in one compact clause.

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 single-parameter read tool with no output schema, the description usefully names the aggregate values returned and frames the query as a baseline comparison. Missing only operational details such as update cadence and the resolution of the returned series.

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 'range' parameter is fully documented with an enum and default in the schema. The description's phrase 'over a chosen window' adds only light framing beyond that; 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?

States a specific verb and resource ('Get recent historical Ethereum mainnet gas prices') plus the aggregate returned (min/max/average). The word 'historical' and 'mainnet' implicitly separates it from get_current_gas and get_l2_gas, but no sibling is named explicitly.

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 concrete usage contexts — 'what has gas been doing', 'was gas cheaper yesterday', 'compare the current price against a recent baseline' — which maps well to the agent's likely questions. It stops short of naming alternatives (e.g. use get_current_gas for the live price) or stating exclusions.

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 the full behavioral burden, and it does disclose the response shape (Gwei plus estimated wallet fee in USD) and the underlying assumption (a standard transfer) that determines the USD estimate. It omits refresh cadence, caching, and any failure/error behavior, which keeps it out of the top band.

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?

Two sentences, zero filler. The scope and returned metrics are front-loaded, with the usage trigger trailing, which is the right ordering for a no-arg lookup tool.

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?

Without an output schema, the description must imply return content, and it names both metrics and the transfer scenario assumed for the USD figure. It does not say how many chains are excluded, how fresh the data is, or that the comparison is point-in-time only, leaving minor gaps for a no-param tool.

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 takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. The description correctly implies a no-argument, fire-and-forget call without adding spurious parameter guidance.

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 concrete verb (compare) and resource (live Layer-2 gas fees) and enumerates the exact chains covered (Arbitrum, Base, Optimism, Polygon), plus the derived output (cheapest right now). This clearly separates it from siblings like get_current_gas or get_gas_forecast, which are not L2-comparison tools.

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?

Provides explicit trigger phrasing: "Use for 'cheapest L2' / 'L2 gas fees' questions." That is strong context, but it names no exclusion or alternative sibling (e.g. when to prefer get_current_gas for L1), so it stops short of full when/when-not guidance.

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