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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool compares prices across DEX pools for arbitrage, but doesn't detail key behaviors such as rate limits, authentication needs, whether it's read-only or has side effects, or how results are returned (e.g., format, pagination). This leaves significant gaps for an agent to understand operational constraints.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every part of the sentence contributes to understanding the tool's function.

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 of price comparison and arbitrage analysis, the description is incomplete. No annotations exist to cover behavioral aspects, and there's no output schema to explain return values. The description lacks details on what the comparison entails (e.g., which DEX pools, time frames, or metrics), making it insufficient for an agent to fully grasp the tool's functionality and limitations.

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 description doesn't add any parameter-specific information beyond what the input schema provides. The schema has 100% coverage with a clear description for 'token_address' as 'Token contract address on Base.' Since schema coverage is high, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.

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 purpose: 'Compare a token's price across multiple DEX pools to find arbitrage opportunities.' It specifies the verb (compare), resource (token's price), and context (across multiple DEX pools). However, it doesn't explicitly distinguish this from sibling tools like 'get_token_price' or 'get_price_history,' which reduces it from a perfect score.

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 minimal guidance on when to use this tool. It implies usage for arbitrage opportunities but doesn't specify when to choose this over alternatives like 'get_token_price' (which might fetch a single price) or 'get_price_history' (which tracks price over time). No explicit when/when-not instructions or prerequisites are mentioned.

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. It mentions checking across 'all DEXes', which adds some context about scope, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the output format might be. This leaves significant gaps for a tool that likely queries external data.

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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 of querying liquidity across multiple DEXes, the lack of annotations and output schema means the description should do more to explain behavioral traits, return values, or limitations. It currently provides only a high-level purpose, which is insufficient for an agent to fully understand how to use this tool effectively.

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 100% description coverage, with the single parameter 'token_address' documented as 'Token contract address on Base'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating value.

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 action ('Check') and the target resources ('pool reserves and liquidity depth for a token across all DEXes'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_market_summary' or 'get_token_price', which might also involve token data, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools such as 'compare_prices' or 'get_price_impact', leaving the agent without context for selection.

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 of behavioral disclosure. It mentions the report includes price, volume estimate, liquidity, and volatility, but does not specify data sources, update frequency, accuracy, rate limits, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operational 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 description is a single, efficient sentence that front-loads key information ('Combined report') and lists the metrics concisely. It avoids unnecessary words, though it could be slightly more structured (e.g., by clarifying the report's format or output).

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 of providing multiple financial metrics, lack of annotations, and no output schema, the description is incomplete. It does not explain the return format, units of measurement, or how metrics are calculated, which are critical for a combined report tool. This leaves the agent with insufficient context to use the tool effectively.

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 100% description coverage, with the parameter 'token_address' clearly documented. The description adds minimal value beyond the schema by implying the token is the target, but does not provide additional context such as token format examples or validation rules. Baseline 3 is appropriate given the high 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 clearly states the tool's purpose: to provide a combined report with specific metrics (price, volume estimate, liquidity, volatility) for a token. It uses a specific verb ('Combined report') and resource ('for a token'), but does not explicitly differentiate from sibling tools like get_token_price or get_liquidity_depth, which offer similar or overlapping functionality.

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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools like get_token_price or get_liquidity_depth, nor does it specify contexts where this combined report is preferable over individual metric tools. Usage is implied by the report's comprehensiveness, but no explicit when/when-not instructions are given.

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 of behavioral disclosure. It mentions querying swap events from DEX pools, which implies a read-only operation, but it doesn't clarify aspects like rate limits, authentication needs, data freshness, or potential errors. For a tool with no annotations, this leaves significant gaps in understanding its 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 description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to understand at a glance. Every part of the sentence contributes to clarifying the tool's function.

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 (fetching historical price data), lack of annotations, and no output schema, the description is moderately complete. It explains the data source (swap events from DEX pools) but doesn't cover return values, error handling, or performance considerations. For a tool with three parameters and no structured output, it should provide more context to be fully helpful.

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 description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description doesn't add any extra meaning beyond the schema, such as explaining how 'periods' and 'interval' interact to define the time range or providing examples of valid 'interval' values. With high schema coverage, the baseline score 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's purpose with a specific verb ('Get') and resource ('recent price data points'), and it explains the mechanism ('by querying swap events from DEX pools'). However, it doesn't explicitly differentiate this tool from sibling tools like 'get_token_price' or 'compare_prices', which likely serve related but distinct purposes in price analysis.

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 no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where this tool is preferred over siblings like 'get_token_price' (which might fetch a single price) or 'compare_prices' (which might compare multiple tokens), nor does it specify prerequisites or exclusions for usage.

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. It states the tool 'estimates' price impact, implying a read-only, non-destructive operation, but does not address potential limitations like accuracy, assumptions (e.g., constant product model), rate limits, or error handling. For a tool with zero annotation coverage, this is insufficient to inform the agent adequately about its 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 description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and uses specific terminology ('constant product formula'), making it highly concise and well-structured for quick comprehension.

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 of price impact estimation and the lack of annotations and output schema, the description is incomplete. It does not explain what the output represents (e.g., percentage impact, slippage), potential errors, or dependencies on external factors like liquidity pools. For a tool with no structured behavioral or output information, more detail is needed to ensure the agent can use it effectively.

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 100% description coverage, with clear parameter descriptions: 'token_address' as a contract address on Base and 'trade_size_eth' in ETH format. The description adds minimal value beyond this, mentioning 'trade size' and 'constant product formula' but not elaborating on parameter interactions or constraints. Given the high schema coverage, a baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding.

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 purpose: 'Estimate price impact for a given trade size using constant product formula.' It specifies the verb ('estimate'), resource ('price impact'), and method ('constant product formula'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'get_liquidity_depth' or 'compare_prices', which might also relate to pricing or liquidity analysis, so it falls short of a perfect score.

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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as 'get_liquidity_depth' or 'get_token_price', nor does it specify contexts or exclusions for its use. This lack of comparative information leaves the agent without clear direction on tool selection.

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 full burden for behavioral disclosure. While it mentions the data sources (Uniswap V2/V3, Aerodrome), it doesn't disclose important behavioral traits like rate limits, error conditions (e.g., invalid token addresses), response format, whether it requires authentication, or how it handles tokens not listed on these DEXs. The description is insufficient for a tool that queries on-chain data.

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, efficient sentence with zero wasted words. It's appropriately sized for a straightforward price lookup tool and front-loads the core functionality. Every element (action, resource, data sources) 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?

Given the complexity of on-chain price queries and the absence of both annotations and output schema, the description is incomplete. It doesn't explain what the price output looks like (USD equivalent? raw token amount?), how recent 'current' means, or important constraints like which DEX pools are prioritized. For a financial data tool with no structured output documentation, this leaves significant gaps.

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 fully documents both parameters (token_address and quote_currency). The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain token address format requirements, valid quote currency options beyond the default, or how the tool resolves price calculations.

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 specific action ('Get current price'), target resource ('any Base token'), and data sources ('on-chain DEX pools (Uniswap V2/V3, Aerodrome)'). It distinguishes itself from siblings like get_price_history (historical data) and get_market_summary (aggregate metrics) by focusing on real-time price retrieval from specific DEX sources.

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 usage for real-time price queries on Base tokens from DEX pools, but provides no explicit guidance on when to use this tool versus alternatives like compare_prices (multiple token comparison) or get_price_impact (slippage analysis). It lacks explicit when-not-to-use criteria or prerequisite information about token availability.

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 of behavioral disclosure. It mentions 'recent swaps' but doesn't specify data sources, rate limits, authentication needs, or error conditions. For a calculation tool with no annotation coverage, this leaves significant gaps in understanding how it behaves operationally.

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, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying what is calculated and from what data.

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 (calculating volatility from swap data), lack of annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or error handling. This leaves the agent with gaps in understanding the full context of use.

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 both parameters thoroughly. The description adds minimal value beyond the schema by implying the parameters relate to 'recent swaps,' but it doesn't provide additional context like token address validation or lookback_hours constraints. Baseline 3 is appropriate 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?

The description clearly states the tool's purpose: 'Calculate price volatility (standard deviation of returns) from recent swaps.' It specifies the verb ('calculate'), resource ('price volatility'), and method ('standard deviation of returns from recent swaps'). However, it doesn't explicitly differentiate from sibling tools like 'get_price_history' or 'get_market_summary' that might provide related metrics.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where volatility calculation is preferred over other metrics like price history or market summary. The agent must infer usage from the purpose alone.

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