WagerX Crypto Casinos

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

With no annotations provided, the description carries the transparency burden. It discloses that 'fine print' is decoded from T&Cs and that expired promos are excluded, giving useful behavioral context. However, it doesn't mention whether the operation is read-only, any rate limits, or how the returns are structured.

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 two sentences, front-loaded with the core purpose, and packs useful detail (fields covered, exclusion of expired promos, intended use). 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?

The description provides enough context for a simple tool with only two optional params and no output schema. It covers what data is returned (key bonus fields) and the filtering behavior (expired excluded). A minor gap is lack of explicitly stating an array return type or default ordering, but with such a focused tool this is adequate.

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 only 50% (casino has a description, limit does not). The description does not explain the 'limit' parameter at all, nor does it add any meaningful behavior beyond the schema. The tool's output fields are mentioned but not the parameters, so the description fails to compensate for the low 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: it returns current best crypto casino bonuses. It names specific attributes (wagering requirement, max bonus, free spins, min deposit, value score) which distinguishes it from sibling tools that focus on casinos (e.g., list_casinos, top_casinos).

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 explicitly says to use this for 'best crypto casino bonus' questions, which is clear context. It doesn't explicitly mention when not to use it or list alternatives, but sibling tool names suggest alternatives, and the scope ('bonuses' vs 'casinos') implicitly differentiates.

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. It adds valuable behavioral context by stating that "Data comes from hand-run real-money tests" and enumerating the included fields (trust score, audit status, live test, KYC policy, license, review link). This goes beyond a simple fetch description, though it does not disclose potential failure modes or staleness of 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?

Two sentences, front-loaded with the core purpose followed by a colon list of outputs. The second sentence adds provenance data (hand-run tests) without fluff. Every word earns its place; no 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?

For a single-parameter tool with no output schema, the description sufficiently enumerates the expected return contents (trust score, audit status, live test details, KYC, license, review link) and explains how the data is sourced. It is complete for an agent to understand what it will receive and what the tool is for.

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 already fully describes the single parameter 'name' with a clear example ("Duelbits" or "Rolly"), achieving 100% coverage. The description merely reiterates that it is for one casino, adding no new syntactic or format details beyond what the schema provides. Baseline 3 applies.

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 uses a specific verb+resource pattern: "Get the WagerX forensic audit verdict for one crypto casino." It clearly scopes to a single casino, distinguishing it from sibling tools like list_casinos, top_casinos, and compare_casinos which operate across multiple casinos. The colon-delimited list of report contents further clarifies the exact deliverable.

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 phrase "for one crypto casino" clearly implies this tool is for investigating a specific casino's audit data, not for browsing or comparing. However, it does not explicitly state when to prefer this over siblings like latest_audits or compare_casinos, nor does it mention any exclusions. Context is clear, but exclusions are missing.

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 transparency burden. It discloses the data source (WagerX audit data) and comparison criteria (trust score, live tests, KYC, withdrawal speed), which is useful. However, it does not state whether the operation is read-only, how results are presented, or any potential side effects. Since the verb 'Compare' implies read-only, some transparency is provided, but it is not exhaustive.

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, front-loaded sentence that states the action, resource, count range, and specific comparison criteria. It is concise and efficiently structured 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?

For a simple one-parameter tool with no output schema, the description covers the essential context: what is compared, how many, and on what data. It lacks details about output format or error handling, but these are less critical given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It implies that the 'names' parameter holds casino names by stating 'Compare 2-5 crypto casinos', but it does not explicitly define the parameter format or expected values beyond that. This adds some meaning over the raw schema but leaves room for ambiguity.

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 uses the specific verb 'Compare' with a clear resource ('crypto casinos') and scope ('2-5', 'on WagerX audit data', including trust score, live tests, KYC, withdrawal speed). It clearly distinguishes from sibling tools like check_casino (single) and list_casinos (listing).

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 clearly implies when to use it: when comparing 2-5 crypto casinos side by side. However, it does not explicitly mention when not to use it or name alternatives, though the sibling tools suggest 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, the description must carry full transparency weight. It reveals the sorting behavior and scope, but omits important behavioral details such as authentication needs, pagination behavior, or whether only certain audit statuses or types are included.

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, front-loaded sentence with no wasted words. It immediately states the action, subject, and ordering.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter list tool, the purpose is clear, but without an output schema the description does not specify what fields an audit record contains, so an agent may not know the expected response structure.

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 only parameter, 'limit', is not mentioned in the description at all. The schema has 0% description coverage, and the description fails to compensate by explaining the limit's meaning, default, or effect on results, leaving the agent without added semantic 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 uses a specific verb ('List') with a clear resource ('most recent WagerX real-money casino audits') and ordering ('newest first'), which distinctly sets it apart from sibling tools about casino rankings and bonuses.

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 clearly implies the use case—when the most recent real-money casino audits are needed—but does not explicitly contrast with alternatives or state when not to use it. This is clear context without exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses the scope (all casinos with audit data) and the return contents (trust score, status), making the behavior transparent for a read-only list operation. It does not mention pagination or sorting, but these are less critical for a zero-parameter exhaustive list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence, 12 words, front-loaded with the verb and resource. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no annotations and no output schema, the description covers the core functionality: what is returned and to what extent ('every casino'). It lacks details like sorting order or data freshness, but the sibling tools suggest limited scope, and the description is adequate.

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 is empty (0 parameters), so there are no parameters to explain. Per rubric, a baseline of 4 applies; the description adds context about the data returned, which is useful.

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 explicitly states the action ('List'), the resource ('every casino WagerX holds forensic audit data on'), and the output fields ('trust score and status'). This clearly differentiates from sibling tools like top_casinos (ranked subset) or check_casino (singular lookup).

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 this is the exhaustive listing tool ('every casino'), giving clear context for when to use it. It does not explicitly mention alternatives or exclusions, but the word 'every' sets it apart from filtered/ranked siblings.

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?

The description adds important behavioral context beyond any annotations by noting that some entries are 'still mid-audit' and that unscored entries are 'awaiting a hand-run real-money test.' This sets expectations about data quality, though it doesn't detail output format or ordering.

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 tightly written in three short sentences, each adding distinct information: what it is, when to use it, and the data caveat. No fluff.

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 (no parameters, no output schema), the description covers the core purpose, usage, and potential data caveats. It doesn't mention the return structure but that's likely obvious for a list tool. Overall it's sufficiently 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 tool has zero parameters, so the description has no need to explain param semantics. The baseline score of 4 applies.

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 identifies the tool's function as surfacing the newest crypto casinos on WagerX's radar, with an explicit usage note for 'new crypto casinos' questions. It distinguishes from sibling tools like list_casinos by emphasizing 'newest' and the mid-audit inclusion.

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 explicit guidance on when to use it ('Use this for "new crypto casinos" questions'), giving a clear context. It does not explicitly mention alternatives or when not to use it, but the context is sufficient.

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 takes on the full burden and delivers meaningful context by explaining the ranking is based on an audit trust score built from real-money tests, payout speed, KYC, support, and license. It doesn't describe the return format, but for a read-only list tool this is sufficient.

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 two concise sentences: the first explains what the tool does and the second gives usage direction. Every word earns its place, with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only one optional parameter, no output schema, and a simple list-returning purpose, the description provides complete context: it explains the ranking basis, gives usage guidance, and implies the output is a ranked list. It doesn't need additional detail to be actionable.

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 documents the single optional 'limit' parameter with default, minimum, and maximum, providing complete semantics. The description doesn't mention limit at all, and since schema description coverage is 0%, it doesn't compensate, but the parameter is simple enough that no additional explanation is strictly necessary.

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 ranks crypto casinos by WagerX audit trust score, mentioning specific criteria like real-money tests and payout speed. This distinctly differentiates it from siblings such as list_casinos, which would simply list casinos, and best_bonuses, which focuses on bonuses.

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?

Explicitly directs usage for "best/safest/most trusted crypto casino" questions, giving clear context for when to select this tool. However, it doesn't explicitly mention when not to use it or name alternatives, so it misses the full 'when-not' guidance.

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