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 analyzing 'recent trades' and 'sandwich attack patterns,' but doesn't describe what the tool returns (e.g., risk scores, transaction lists, or alerts), whether it requires specific permissions, rate limits, or how it handles errors. For a security analysis tool with zero annotation coverage, 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 ('Analyze') and includes all essential elements (resource, timeframe, target). There's zero waste or redundancy.

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 security analysis and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what the analysis yields (e.g., risk metrics, flagged transactions), how results are structured, or any behavioral traits like rate limits or data sources. For a tool that likely returns nuanced findings, this leaves the agent guessing about the output format and operational constraints.

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 both parameters ('token_address' and 'blocks_back') fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain token address format or block range implications). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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: 'Analyze a token's recent trades for sandwich attack patterns (bot activity).' It specifies the verb ('analyze'), resource ('token's recent trades'), and target pattern ('sandwich attack patterns'). However, it doesn't explicitly differentiate from sibling tools like 'detect_arb_opportunity' or 'scan_top_tokens' that might also involve trade 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. There's no mention of prerequisites, when this analysis is appropriate, or what distinguishes it from sibling tools like 'detect_arb_opportunity' or 'get_mempool_pending' that might relate to transaction monitoring. The agent must infer usage context solely from the purpose statement.

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 full burden. While it mentions the tool's core function, it lacks behavioral details such as rate limits, computational requirements, whether it performs live queries or uses cached data, error conditions, or what constitutes a 'profitable arbitrage route' in the response. The description doesn't contradict annotations (none exist).

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 extremely concise (two sentences) and front-loaded with the core purpose. Every word earns its place—no redundant phrases or unnecessary elaboration. It efficiently communicates the tool's function without verbosity.

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 arbitrage detection (involving multiple DEXes and profit calculations) and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain what the output looks like (e.g., route details, profit amounts), performance characteristics, or limitations. For a tool with no structured behavioral hints, more descriptive context is needed.

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 doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't clarify token_address format beyond 'contract address' or explain min_profit_bps implications). Baseline 3 is appropriate when 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 with specific verbs ('compare', 'find') and resources (token prices across Uniswap V2, Uniswap V3, and Aerodrome on Base). It distinguishes itself from siblings by focusing on arbitrage opportunity detection rather than risk assessment, profit estimation, or data retrieval.

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 context (finding arbitrage opportunities) but doesn't explicitly state when to use this tool versus alternatives like 'get_price_across_dexes' or 'estimate_flash_profit'. No guidance is provided about prerequisites, exclusions, or specific scenarios where this tool is preferred.

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 'estimate' and 'after gas', which implies a calculation without execution, but it doesn't clarify if this is a simulation, requires network access, has rate limits, or what the output format might be. For a tool with financial implications and no annotations, this leaves significant gaps in understanding its behavior and 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 front-loads the core purpose ('estimate flash loan profit after gas') and adds the key condition ('Given two pools with a price difference'). Every word earns its place with zero waste, making it highly concise and well-structured for quick understanding.

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 flash loan profit estimation (involving gas costs, price differences, and financial calculations), no annotations, and no output schema, the description is incomplete. It lacks details on how the estimation is performed, what the output includes (e.g., profit amount, gas cost breakdown), and any assumptions or limitations. This makes it inadequate for a tool with such operational and financial implications.

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 parameters thoroughly (e.g., 'buy cheap here' for pool_a_address). The description adds no additional parameter semantics beyond what's in the schema. According to guidelines, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 flash loan profit after gas' given 'two pools with a price difference'. It specifies the verb ('estimate'), resource ('flash loan profit'), and key condition ('after gas'). However, it doesn't explicitly differentiate from siblings like 'detect_arb_opportunity' or 'check_sandwich_risk', which might also involve profit estimation or arbitrage scenarios.

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: it implies usage when there's a price difference between two pools for flash loans. However, it doesn't specify when to use this tool versus alternatives (e.g., 'detect_arb_opportunity' for opportunity detection or 'get_price_across_dexes' for price checks), nor does it mention prerequisites like needing pool addresses or token details. No explicit exclusions or context for when-not-to-use are provided.

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 'front-run detection' and the note about 'minimal public mempool exposure,' which adds context about the tool's limitations and use case. However, it doesn't disclose other behavioral traits such as rate limits, authentication needs, or what the output might look like (e.g., transaction details, risk scores). This is a moderate gap given the lack of annotations.

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 concise and front-loaded, with the main purpose stated first and an additional note provided for context. Both sentences earn their place by clarifying the tool's function and its limitations. However, it could be slightly more structured by explicitly separating usage guidelines, but it's still efficient with zero waste.

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 (involving blockchain mempool analysis) and the lack of annotations and output schema, the description is moderately complete. It covers the purpose and a key limitation, but doesn't explain return values, error handling, or detailed behavioral aspects. This leaves gaps that could hinder an AI agent's ability to use the tool effectively, though it's adequate as a minimum viable description.

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 as 'Token contract address on Base.' The description doesn't add any extra meaning beyond this, as it doesn't elaborate on parameter usage or constraints. According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description, which fits here.

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: 'Check pending transactions for a token (front-run detection).' It specifies the verb ('Check'), resource ('pending transactions'), and context ('for a token'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'check_sandwich_risk' or 'detect_arb_opportunity', which might also involve transaction analysis, so it doesn't reach 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 implied usage context with 'front-run detection' and the note about 'Base L2 has minimal public mempool exposure,' suggesting this tool is for monitoring mempool activity on Base. However, it doesn't explicitly state when to use this tool versus alternatives like 'check_sandwich_risk' or 'detect_arb_opportunity,' nor does it provide exclusions or prerequisites, leaving some ambiguity for an AI agent.

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 states what the tool does but lacks critical details: it doesn't specify if this is a read-only operation, what the output format looks like (e.g., list of pools with reserves), whether it requires authentication, rate limits, or how it handles 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 front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.

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 lack of annotations and output schema, the description is incomplete for a tool that likely returns complex data (reserves across multiple DEX pools). It doesn't cover behavioral aspects like safety, output structure, or error handling, which are essential for an AI agent to use it correctly in a blockchain 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?

The description implies the parameter 'token_address' is used to identify the token, but adds no semantic details beyond what the schema provides (which has 100% coverage with a clear description). It doesn't explain format requirements (e.g., checksummed address), validation rules, or examples, so it meets the baseline for high schema coverage without adding extra 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 ('Get reserves/liquidity') and target resource ('for a token across all known DEX pools on Base'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_price_across_dexes' or 'scan_top_tokens', which might also involve token data across DEXes.

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 prerequisites, exclusions, or compare it to siblings such as 'get_price_across_dexes' (which might return price data rather than reserves) or 'scan_top_tokens' (which might scan multiple tokens rather than a specific one).

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 full burden. It states what the tool does but lacks behavioral details such as rate limits, authentication needs, whether it returns real-time or cached data, error handling, or what 'all available DEXes' entails. This is a significant gap for a tool with no annotation coverage.

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 waste. It is front-loaded with the core purpose and 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 no annotations and no output schema, the description is incomplete. It lacks details on return format (e.g., structured price data per DEX), behavioral traits, and usage context. For a tool that likely returns complex data across multiple DEXes, this is inadequate.

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 the single parameter 'token_address' well-documented in the schema. The description adds no additional parameter semantics beyond implying it's for a token on Base, which is already covered. Baseline 3 is appropriate as 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 specific action ('Get current price') and resource ('token on all available DEXes on Base'), with precise scope. It distinguishes from siblings like 'scan_top_tokens' (which scans tokens rather than getting prices) and 'get_pool_reserves' (which gets reserves rather than prices).

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?

No guidance is provided on when to use this tool versus alternatives. While the purpose is clear, there's no mention of when-not-to-use scenarios or explicit alternatives among the sibling tools (e.g., 'detect_arb_opportunity' might be related but not compared).

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 scanning for 'arb opportunities' but does not clarify what constitutes an opportunity, how results are returned, or any operational constraints (e.g., rate limits, data freshness). The description lacks details on the tool's behavior beyond the basic action, leaving significant gaps for an AI agent.

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. It directly communicates the tool's function and method, making it easy to parse and understand quickly. Every part of the sentence contributes meaning, earning a high score for conciseness.

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 scanning for arbitrage opportunities, the description is insufficient. With no annotations and no output schema, it fails to explain what the tool returns (e.g., list of tokens, opportunity details) or any behavioral traits. The description does not compensate for these gaps, making it incomplete for effective tool invocation by an AI 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 100% description coverage, with clear defaults and meanings for both parameters (min_liquidity_eth and limit). The description does not add any parameter-specific information beyond what the schema provides, such as typical values or usage context. Given the high schema coverage, a baseline score of 3 is appropriate, as the description neither compensates nor detracts.

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: 'Scan top traded tokens on Base for arb opportunities using DexScreener.' It specifies the verb (scan), resource (top traded tokens on Base), and goal (for arb opportunities), with the method (using DexScreener) adding useful context. However, it does not explicitly differentiate from sibling tools like 'detect_arb_opportunity' or 'get_price_across_dexes', which prevents a score of 5.

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 mentions 'arb opportunities' but does not specify scenarios, prerequisites, or exclusions, nor does it reference sibling tools like 'detect_arb_opportunity' for comparison. This lack of contextual direction limits its utility for an AI agent in selecting the appropriate tool.

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