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 lists the types of information returned (stats, parts, etc.), which adds some context, but it doesn't cover critical aspects like error handling (e.g., what happens if the ID is invalid), rate limits, authentication needs, or data freshness. For a read operation with zero annotation coverage, this is a significant gap.

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 ('Get detailed information about a single Axie by its ID') and then enumerates the included data types without unnecessary elaboration. Every word earns its place, 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 tool's low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate for basic use. However, with no annotations and no output schema, it lacks details on behavioral traits (e.g., error cases) and return format, which could hinder an agent's ability to handle edge cases effectively. It meets minimum viability but has clear 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?

The input schema has 100% description coverage, with the parameter 'axieId' clearly documented as a numeric ID. The description adds no additional semantic details beyond what the schema provides, such as format constraints or examples beyond the schema's 'e.g. '1234567''. Since the schema does the heavy lifting, 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 verb 'Get' and the resource 'detailed information about a single Axie by its ID', specifying exactly what the tool does. It distinguishes itself from siblings like 'search_axies' (which returns multiple results) and 'get_axie_children' (which focuses on offspring), making the purpose specific and well-differentiated.

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 when detailed information about a specific Axie is needed, but it doesn't explicitly state when to use this tool versus alternatives like 'search_axies' for broader queries or 'get_axie_children' for related data. No exclusions or prerequisites are mentioned, leaving some ambiguity in context.

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 it 'Get[s]' data, implying a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or response format (e.g., JSON structure, pagination). For a tool with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence that front-loads the core purpose. There is no wasted wording, and it directly communicates the tool's function without redundancy or fluff, 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 tool's moderate complexity (retrieving relational data), lack of annotations, and no output schema, the description is minimally complete. It specifies what data is fetched but omits details on behavior, response format, and usage context. It meets a basic threshold but leaves gaps that could hinder effective agent 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%, with the parameter 'axieId' documented as 'The numeric ID of the parent Axie.' The description adds no additional meaning beyond this, such as format examples (e.g., integer vs. string) or validation rules. Baseline 3 is appropriate since the schema handles parameter documentation adequately.

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 verb 'Get' and the resource 'children (bred Axies) and parentage information', specifying what data is retrieved. It distinguishes from siblings like get_axie (general info) and get_axie_transfer_history (transaction data), though not explicitly. However, it could be more precise about the scope (e.g., all children vs. recent).

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 (e.g., needing a valid Axie ID), exclusions, or comparisons to siblings like get_axie (which might include some parentage) or search_axies (which could filter by parent). Usage is implied but not articulated.

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 the tool retrieves equipment, implying a read-only operation, but doesn't disclose any behavioral traits such as rate limits, authentication needs, data freshness, error handling, or whether it supports pagination for large lists of Axie IDs. 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, clear sentence that efficiently conveys the tool's purpose without any wasted words. It is front-loaded with the core action and resource, making it easy to parse. This is an excellent example of conciseness, as every part of the sentence earns its place in defining what the tool does.

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 required parameter) and lack of annotations and output schema, the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, and return values. Without an output schema, the description doesn't explain what the equipment data looks like (e.g., format, fields), leaving gaps that could hinder an agent's ability 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 'axieIds' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't explain format constraints, valid ID ranges, or handling of invalid IDs). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Get the equipment and accessories currently equipped on one or more Axies.' It specifies the verb ('Get'), resource ('equipment and accessories'), and scope ('on one or more Axies'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_axie' (which might return general Axie data) or 'get_erc1155_tokens' (which might include equipment tokens), 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 specific contexts for usage. For example, it doesn't clarify if this is for real-time data or cached information, or how it relates to tools like 'get_axie' or 'get_erc1155_tokens'. This lack of usage context leaves the agent to infer when this tool is appropriate.

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 'on-chain' history, implying blockchain data, but doesn't specify rate limits, authentication needs, error conditions, or what the output looks like (e.g., format, pagination details beyond schema). 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. It directly states what the tool does ('Get...history') and for what ('specific Axie'), making it easy to parse and understand 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 no annotations and no output schema, the description is minimally adequate for a read-only tool. It specifies the resource and scope ('on-chain transfer and sale history'), but lacks details on output format, error handling, or behavioral constraints. For a tool with 3 parameters and no structured safety hints, it should do more to be fully 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?

The schema description coverage is 100%, so the schema already documents all parameters (axieId, from, size) with clear descriptions. The description adds no additional meaning beyond implying 'axieId' is for a 'specific Axie,' which is redundant. Baseline 3 is appropriate as the schema does the heavy lifting, but no extra value is provided.

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: 'Get the on-chain transfer and sale history for a specific Axie.' It specifies the verb ('Get') and resource ('transfer and sale history'), and distinguishes it from siblings like 'get_axie' (general info) or 'get_user_activities' (user-focused). However, it doesn't explicitly differentiate from all siblings (e.g., 'get_top_sales' might overlap in concept), keeping it from a perfect 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 doesn't mention prerequisites, exclusions, or comparisons to siblings like 'get_user_activities' (which might include transfers) or 'get_top_sales' (which might involve sale data). Without such context, an agent must infer usage from the name and description alone.

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 mentions filtering by owner but does not disclose behavioral traits such as pagination behavior (implied by 'from' and 'size' parameters), rate limits, authentication needs, or what the response format looks like. This leaves significant gaps for a tool with multiple parameters.

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 and includes optional filtering. There is no wasted verbiage, and 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 (4 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and filtering but lacks details on behavior, response format, and usage context relative to siblings. This is adequate as a minimum but has clear gaps for effective tool 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?

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds minimal value by mentioning the optional owner filter and token examples, but it does not provide additional semantics beyond what the schema specifies. This meets the baseline for 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 verb 'Get' and resource 'ERC1155 tokens', with specific examples (runes, charms, accessories, etc.) that distinguish it from sibling tools like get_axie or get_land. It precisely defines the scope as in-game items, 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 provides clear context for usage with the optional owner filter, but it does not explicitly state when to use this tool versus alternatives like get_axie_equipment or search_axies. It implies usage for ERC1155 tokens but lacks explicit exclusions or comparisons to sibling tools.

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. It states what the tool does but lacks critical behavioral details: whether it requires authentication, rate limits, data freshness (e.g., real-time vs cached), error handling, or output format. The description is minimal and doesn't compensate for missing 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 a single, efficient sentence that front-loads the core purpose. Every word contributes value by specifying the action, resource scope, and token list without redundancy or fluff. It's appropriately sized for a simple 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?

Given the tool's simplicity (0 parameters, no output schema) and lack of annotations, the description is incomplete. It omits behavioral context like data source, update frequency, or return structure. While minimalism is acceptable for simple tools, the absence of annotations means the description should provide more operational guidance 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 tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist. It earns a baseline 4 for not needing to compensate for parameter documentation gaps, though it could theoretically mention why no parameters are needed (e.g., always returns all listed tokens).

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') and resource ('current USD exchange rates for Axie Infinity tokens'), listing the exact tokens covered (ETH, AXS, SLP, RON, USDC). It distinguishes itself from sibling tools that focus on assets, profiles, or market statistics rather than exchange rates.

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. While it implicitly suggests use for exchange rate queries, it doesn't mention prerequisites, timing considerations, or how it differs from other market-related tools like 'get_market_stats' or 'get_overall_market_stats'.

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 the tool retrieves details but does not specify whether it's a read-only operation, potential error conditions, rate limits, or authentication needs. For a 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 is front-loaded and wastes no space, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete. It does not explain what details are returned, error handling, or usage constraints. For a tool with no structured behavioral data, more context is needed to fully understand its operation and 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?

The input schema has 100% description coverage, with clear documentation for 'col' and 'row' parameters. The description adds minimal value beyond the schema by implying these coordinates are used to identify a land plot, but it does not provide additional context like coordinate ranges or examples. 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 tool's purpose: 'Get details for a specific land plot by its grid coordinates.' It specifies the verb ('Get details'), resource ('land plot'), and method ('by its grid coordinates'). However, it does not explicitly differentiate from sibling tools like 'search_lands', which might be used for broader queries, leaving room for improvement.

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 lacks any mention of prerequisites, exclusions, or comparisons to sibling tools such as 'search_lands', which could be used for different types of land queries. This omission reduces its effectiveness in helping an agent choose correctly.

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 mentions retrieving rankings but doesn't disclose behavioral traits like rate limits, authentication requirements, pagination behavior beyond parameters, error conditions, or response format. The description is minimal and lacks operational 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and domain context concisely.

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 doesn't explain what the return values look like (e.g., structure of rankings), error handling, or operational constraints. For a tool with 3 parameters and competitive data, more context is needed for effective 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 parameters are fully documented in the schema. The description adds no additional meaning beyond implying categories exist, which is already evident from the enum. Baseline score of 3 applies as the schema handles parameter semantics adequately.

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') and resource ('leaderboard rankings') with specific domain context ('for various competitive categories in Axie Infinity'). It distinguishes from most siblings that focus on individual assets or market data, though it doesn't explicitly contrast with similar ranking tools like 'get_top_sales'.

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. The description mentions 'various competitive categories' but doesn't specify which scenarios warrant this tool over sibling tools like 'get_top_sales' or 'get_market_stats' for ranking-related queries.

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 describes what data is retrieved but does not cover critical aspects like rate limits, authentication needs, data freshness, or error handling. For a tool with zero annotation coverage, 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 the key action and details without any wasted words. It directly communicates the tool's function and scope, making it easy to parse and understand 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 tool's complexity (retrieving statistical data over multiple timeframes) and lack of annotations and output schema, the description is minimally adequate. It specifies what data is fetched but does not explain the return format, structure, or units (e.g., currency for volumes). For a data-fetching tool with no structured output documentation, more detail would be 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's output semantics (e.g., timeframes and data types). This meets the baseline for tools with no parameters.

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 ('marketplace settlement statistics'), including the types of data (transaction counts, Axie counts, volumes) and timeframes (24 hours, 7 days, 30 days). It distinguishes from siblings like 'get_overall_market_stats' by specifying 'settlement' statistics, though the distinction could be more explicit.

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. It does not mention prerequisites, compare with sibling tools like 'get_overall_market_stats', or specify scenarios where this tool is preferred. The description implies usage for historical settlement data but lacks explicit when/when-not instructions.

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. While it clearly describes what data is returned, it doesn't address important behavioral aspects like whether this is a real-time or cached data source, rate limits, authentication requirements, or potential data freshness issues. For a data retrieval tool with zero annotation coverage, this represents a significant gap in behavioral 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 a single, efficient sentence that packs substantial information. It front-loads the core purpose ('Get overall marketplace statistics') and then provides specific examples of what's included. There's zero wasted language or redundancy, making it maximally concise while still informative.

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 parameterless tool with no output schema, the description provides adequate information about what data is returned. However, without annotations covering behavioral aspects and with sibling tools that might overlap in functionality, the description doesn't provide complete context for optimal tool selection and usage. It tells what you get but not important contextual details about data freshness, limitations, or differentiation from similar tools.

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 0 parameters with 100% schema description coverage, so the baseline score would be 3. However, the description appropriately acknowledges this by not attempting to describe non-existent parameters while clearly indicating what data will be returned. The description adds value by specifying the types of statistics included, which helps the agent understand what to expect from this parameterless query.

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: 'Get overall marketplace statistics' with specific examples of what statistics are included (new Axies minted, marketplace volumes in RON and USD, total transactions, and ascended Axies). It distinguishes itself from sibling tools like 'get_market_stats' by specifying 'overall' statistics rather than more granular market data. However, it doesn't explicitly contrast with 'get_market_stats' in the description text itself.

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. With sibling tools like 'get_market_stats' and 'get_top_sales' that might provide overlapping or related market data, there's no indication of when this 'overall' statistics tool is appropriate versus more specific market tools. The description simply states what the tool does without contextual usage information.

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 the return values (account ID, name, linked addresses) but doesn't cover critical aspects like error handling (e.g., invalid addresses), rate limits, authentication needs, or whether it's read-only. This leaves significant gaps for a tool that likely interacts with 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 front-loads the core action and resource. Every word earns its place, with no redundant or vague phrasing, 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 tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return values, but lacks details on behavior, errors, or usage context. For a simple read operation, this is passable but could be more informative to fully guide 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?

Schema description coverage is 100%, so the input schema fully documents the single parameter 'roninAddress' with its format. The description adds no additional parameter semantics beyond implying it's used to fetch the profile, which is already clear from the schema. This meets the baseline for 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 action ('Get'), resource ('public player profile'), and key identifier ('by Ronin address'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_user_activities' or 'search_axies', which might also involve user data, so it misses full sibling 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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, such as needing a valid Ronin address, or exclusions, like not working for private profiles. It simply states what it does without usage instructions.

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 but offers minimal behavioral insight. It implies a read-only operation but doesn't disclose rate limits, authentication needs, data freshness, pagination, error conditions, or what 'top sales' means (e.g., by volume, price, count). This is inadequate for a tool with potential complexity in sales data retrieval.

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. It avoids redundancy and wastes no words, though it could be slightly more structured by separating key elements like token types and period.

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 doesn't explain return values (e.g., format of sales data, fields included), error handling, or behavioral constraints. For a tool fetching potentially complex sales data, this leaves significant gaps for an AI agent to infer usage.

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 parameters are well-documented in the schema. The description adds marginal value by mentioning token types and time period, but doesn't explain semantics beyond what's in schema descriptions (e.g., what 'top' means, how 'size' interacts with defaults). Baseline 3 is appropriate given 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 action ('Get') and resource ('top sales for a given token type'), specifying the scope ('over a specified time period'). It distinguishes from most siblings by focusing on sales data rather than individual items or profiles, though it doesn't explicitly differentiate from 'get_market_stats' or 'get_overall_market_stats' which might have 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 doesn't mention sibling tools like 'get_market_stats' or 'get_overall_market_stats' that might serve similar purposes, nor does it specify prerequisites, exclusions, or contextual triggers for selecting this tool.

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 on-chain activity history' and lists example activity types, but does not cover critical behaviors such as rate limits, authentication requirements, pagination, error handling, or the format of returned data. For a tool with no annotation coverage, this leaves significant gaps in understanding how it operates.

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. It avoids unnecessary words and directly states what the tool does, though it could be slightly more structured by separating usage hints or behavioral details.

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 a tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines. While the purpose is clear, the overall context for effective tool invocation is insufficient, especially for an AI agent needing to understand how to handle results or errors.

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 input schema fully documents parameters like 'userAddress', 'activityTypes', and 'size'. The description adds minimal value beyond the schema by implying filtering with 'such as buys, sells, transfers, and breeds' for activity types, but does not provide additional syntax or usage details. This meets the baseline for 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: 'Get the recent on-chain activity history for a user's Ronin address, such as buys, sells, transfers, and breeds.' It specifies the verb ('Get'), resource ('activity history'), and scope ('user's Ronin address'), but does not explicitly differentiate it from sibling tools like 'get_axie_transfer_history' or 'search_axies', which might also involve user activities or transactions.

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 or contexts where other tools might be more appropriate, such as using 'get_axie_transfer_history' for specific transfer details or 'search_axies' for broader queries. Usage is implied by the purpose but lacks explicit direction.

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 but offers minimal behavioral insight. It mentions 'optional filters' and 'supports filtering' but doesn't disclose pagination behavior (beyond schema), rate limits, authentication requirements, error conditions, or what happens when no results match. For a search tool with 10 parameters, this leaves significant gaps in understanding how it behaves.

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 efficiently communicates the core functionality. Every word earns its place - it states the action, target, and key filter categories without redundancy or unnecessary elaboration. It's front-loaded with the essential 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 search tool with 10 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (list of Axies? metadata? paginated results?), doesn't mention performance characteristics or limitations, and provides no context about the marketplace environment. The agent would need to guess about the response 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%, so the schema already documents all 10 parameters thoroughly. The description adds marginal value by listing filter categories (class, parts, breed count, auction type, owner) but doesn't provide additional syntax, format details, or usage examples beyond what's in the schema descriptions. This meets the baseline for 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 verb ('Search') and resource ('Axies on the marketplace'), specifies the domain (marketplace search), and distinguishes from siblings like get_axie (single Axie retrieval) or search_lands (different resource type). It's specific and immediately tells what the tool does.

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 when to choose search_axies over get_axie for single Axie retrieval, or when to use it versus get_market_stats for aggregated data. There's no context about prerequisites, typical use cases, or limitations.

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. It mentions searching with filters but doesn't describe what the search returns (e.g., list of lands with details), pagination behavior (implied by 'from' and 'size' but not explained), rate limits, authentication needs, or error conditions. This is inadequate for a search tool with multiple parameters.

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 ('Search for land plots') and adds necessary context ('on the Axie Infinity marketplace with optional filters'). There's zero wasted verbiage, 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?

For a search tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., structure of land plot data), how results are presented, or behavioral aspects like pagination limits. The agent would lack sufficient context to use this tool effectively without trial and error.

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 6 parameters thoroughly with descriptions and enums. The description adds no additional parameter semantics beyond mentioning 'optional filters', which is already implied by the schema. 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 action ('Search for land plots') and the target resource ('on the Axie Infinity marketplace'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'get_land' or 'search_axies', which would be needed for 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 mentions 'optional filters' but provides no guidance on when to use this tool versus alternatives like 'get_land' or 'search_axies'. There's no context about use cases, prerequisites, or comparisons with sibling tools, leaving the agent without usage direction.

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