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 data source ('Wormholescan API') and output format ('pandas DataFrame'), but lacks critical details: it doesn't specify if this is a read-only operation, potential rate limits, authentication needs, error handling, or what 'activity data' entails beyond the parameters. For a tool fetching external API data, 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 appropriately sized and front-loaded, starting with the core purpose. The Args and Returns sections are structured clearly, though the 'Returns' could be more precise (e.g., noting it's a string representation). No extraneous information is included, making it efficient.

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 3 parameters with 0% schema coverage and no output schema, the description does well on parameters but lacks completeness in other areas. It doesn't explain the return structure beyond 'pandas DataFrame', and with no annotations, it misses behavioral aspects like data freshness or API constraints. For a tool with external dependencies, more 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 0%, so the description must fully compensate. It successfully adds meaning beyond the bare schema by explaining all three parameters: 'timeSpan' with allowed values and default, 'by' with options and default, and 'app' with format and default. This provides essential context not present in the schema's minimal titles.

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 ('fetch cross-chain activity data') and the resource ('from Wormholescan API'), and specifies the output format ('return as a pandas DataFrame'). It distinguishes from siblings by focusing on cross-chain activity, unlike tools for KPIs, money flow, or top assets/chains. However, it doesn't explicitly contrast with similar-sounding siblings like 'get_top_chain_pairs_by_num_transfers'.

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, context for selection among siblings, or exclusions. The sibling tools include related metrics (e.g., 'get_top_chain_pairs_by_num_transfers'), but no comparison is offered.

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 the return format ('String representation of a pandas DataFrame'), which adds some behavioral context, but lacks details on error handling, rate limits, authentication needs, or data freshness. The description doesn't contradict annotations (none provided).

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 with two sentences: one stating the purpose and source, and another specifying the return format. It's front-loaded with the main action. However, the second sentence could be integrated more smoothly, and there's minor redundancy in specifying 'Wormhole' twice.

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 0 parameters and no output schema, the description is moderately complete. It covers the purpose and return format, but as a tool with no annotations, it should ideally include more behavioral context (e.g., data scope, limitations). The lack of usage guidelines and minimal transparency details leaves gaps for an AI agent to 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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details beyond the schema, but this is acceptable as there are no parameters. Baseline is 4 for 0 parameters, as the description doesn't need to compensate for missing schema info.

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 ('Fetch') and resource ('list of KPIs for Wormhole'), specifying the source ('from Wormholescan API'). It distinguishes from siblings by focusing on KPIs rather than activity, money flow, corridors, assets, chain pairs, or symbols. However, it doesn't explicitly contrast with siblings like 'get_top_chain_pairs_by_num_transfers' which might also involve KPIs.

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 fetching KPIs but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this is for general KPI overview versus detailed metrics available in other 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 the full burden of behavioral disclosure. It mentions fetching data from an API but doesn't cover critical aspects like rate limits, authentication requirements, error handling, or whether this is a read-only operation. The description lacks behavioral traits beyond the basic action, leaving 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 well-structured and appropriately sized, with a clear purpose statement followed by organized sections for 'Args' and 'Returns.' Each sentence earns its place by providing essential information without redundancy. However, the 'Returns' section could be more concise by avoiding the phrase 'String representation of' if not necessary.

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 (6 parameters, no annotations, no output schema), the description is moderately complete. It covers parameters well but lacks behavioral context (e.g., API constraints) and doesn't fully explain the return value beyond mentioning a pandas DataFrame. For a data-fetching tool with multiple filters, more guidance on usage and error cases would improve completeness.

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 0%, so the description must compensate. It provides detailed semantics for all 6 parameters, including their purposes (e.g., 'Time span for data'), formats (e.g., 'ISO 8601 format'), and defaults. This adds substantial value beyond the bare schema, though it doesn't fully explain interactions between parameters like 'timespan' and date ranges.

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 'Fetch transaction count and volume data from Wormholescan API for a specific period,' which specifies the verb (fetch), resource (transaction count and volume data), and source (Wormholescan API). However, it doesn't explicitly differentiate this tool from its siblings like 'get_cross_chain_activity' or 'get_top_chain_pairs_by_num_transfers,' which may also involve transaction data.

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 its siblings. It doesn't mention alternatives, exclusions, or specific contexts where this tool is preferred over others like 'get_top_assets_by_volume' or 'get_top_symbols_by_volume,' which could be related. Usage is implied only through the tool name and description.

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 fetches data from an API but doesn't mention rate limits, authentication needs, error handling, or whether it's a read-only operation. The return format is described as a string representation of a pandas DataFrame, which is useful but lacks details on structure or potential side effects.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured clearly, though the second sentence could be slightly more concise. Every sentence adds value, with no wasted words.

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 (1 parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the purpose and parameters well but lacks usage guidelines, behavioral context like API constraints, and details on the DataFrame format. Without annotations or output schema, more completeness is needed for optimal 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?

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the timeSpan parameter's purpose ('Time span for data'), provides allowed values ('2d, 7d'), and specifies a default ('Default: 2d'). This compensates well for the schema's lack of documentation, though it doesn't detail what '2d' or '7d' mean precisely.

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 ('Fetch'), resource ('top 100 token corridors by number of transactions'), and source ('from Wormholescan API'). It distinguishes from siblings by focusing on corridors rather than assets, symbols, chain pairs, or other metrics like activity, KPIs, or money flow.

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 like get_top_chain_pairs_by_num_transfers or get_top_assets_by_volume. It mentions a timeSpan parameter but doesn't explain when to choose different values or why this tool is preferred over others for corridor analysis.

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 data source (Wormholescan API) and return format (string representation of a pandas DataFrame), but lacks details on rate limits, authentication needs, error handling, or whether this is a read-only operation. For a tool with zero annotation coverage, this is insufficient.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by clear sections for Args and Returns. Every sentence earns its place by providing essential information without redundancy, making it efficient 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 output schema, no annotations), the description is moderately complete. It covers the purpose, parameter details, and return format, but lacks behavioral context like rate limits or error handling. Without annotations or output schema, it should do more to be fully comprehensive.

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 adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'timeSpan' parameter's purpose, valid values (7d, 15d, 30d), and default (7d), compensating fully for the schema's lack of documentation. With only one parameter well-explained, this earns a high score.

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 ('Fetch top assets by volume') and resource ('from Wormholescan API'), distinguishing it from sibling tools like get_top_symbols_by_volume or get_top_chain_pairs_by_num_transfers by focusing on assets rather than symbols or chain pairs. The verb 'fetch' is precise and the scope is well-defined.

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 it implicitly suggests fetching top assets by volume, it doesn't mention when-not scenarios or compare it to siblings like get_top100_corridors or get_money_flow, leaving the agent without explicit usage 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches data and returns a string representation of a pandas DataFrame, which implies a read-only operation. However, it doesn't mention potential limitations like rate limits, authentication needs, error handling, or data freshness, which are critical for an API-based tool. The description adds minimal behavioral context beyond the basic operation.

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 efficiently structured and front-loaded, with the core purpose stated first, followed by clear sections for arguments and returns. Each sentence adds value without redundancy, and the total length is appropriate for the tool's complexity. There's no wasted text, 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 tool's moderate complexity (API fetch with one parameter) and lack of annotations or output schema, the description is partially complete. It covers the purpose and parameter semantics adequately but lacks usage guidelines, detailed behavioral traits, and output specifics (e.g., DataFrame structure). This leaves gaps that could hinder effective tool invocation in varied contexts.

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 adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'timeSpan' parameter's purpose ('Time span for data'), provides allowed values ('7d, 15d, 30d'), and notes the default ('Default: 7d'). This compensates well for the schema's lack of documentation, though it doesn't detail format constraints or validation rules.

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: 'Fetch top chain pairs by number of transfers from Wormholescan API.' It specifies the verb ('fetch'), resource ('top chain pairs'), and metric ('number of transfers'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_top100_corridors' or 'get_top_assets_by_volume', which likely involve similar data but different metrics or scopes.

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 the data source ('Wormholescan API') but doesn't specify use cases, prerequisites, or comparisons to sibling tools such as 'get_cross_chain_activity' or 'get_top_symbols_by_volume'. This lack of context 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 the full burden of behavioral disclosure. It mentions the API source ('Wormholescan API') and the return format ('String representation of a pandas DataFrame'), but fails to disclose critical traits like rate limits, authentication needs, error handling, or whether this is a read-only operation. 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 appropriately sized and front-loaded, starting with the core purpose. The use of sections ('Args:', 'Returns:') adds structure, though the 'Args' section is slightly redundant since it only covers one parameter. Overall, it avoids unnecessary verbosity while conveying essential information efficiently.

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 lack of annotations or output schema, the description is minimally adequate. It covers the purpose, parameter details, and return format, but misses behavioral aspects like error handling or API constraints. Without an output schema, it should ideally explain the DataFrame structure more, but the current level is acceptable for basic functionality.

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 adds meaningful context for the single parameter 'timeSpan' by specifying allowed values ('7d, 15d, 30d') and the default ('7d'), which the input schema lacks (0% coverage). This compensates well for the schema's deficiency, making the parameter usage clear without needing to detail syntax or format extensively.

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 ('Fetch') and resource ('top symbols by volume from Wormholescan API'), making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from its sibling 'get_top_assets_by_volume' or 'get_top_chain_pairs_by_num_transfers', which might cause confusion about when to use each tool for similar volume-related queries.

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, such as the sibling tools 'get_top_assets_by_volume' or 'get_top_chain_pairs_by_num_transfers'. It lacks context about specific use cases, exclusions, or prerequisites, leaving the agent to infer usage based on the tool name alone.

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