WagerX Crypto Casinos

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

The description explicitly states that expired promos are excluded, and that the headline percentage is a marketing metric while the decoded terms matter. It also reveals the tool decodes T&Cs. With no annotations provided, the description carries full burden and reasonably discloses key behavioral traits. A slight deduction for not mentioning if the list is sorted or if there are rate limits.

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 three sentences long, front-loads the core purpose, and each sentence adds distinct value: what the tool returns, what to use it for, and example calls. 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 simple tool (2 optional parameters, no output schema, no annotations), the description is nearly complete. It explains what the tool returns, what it excludes, and provides usage examples. It could be slightly more complete by mentioning the default sort order or whether the result is limited by default, but it's 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 50% — the description does not explicitly detail the 'limit' parameter's meaning, but the schema already defines it with default, min, max. The 'casino' parameter is described in the schema as 'Optional: only bonuses from this casino'. The real world example calls in the description add value by showing typical usage patterns. The description compensates well for missing parameter details.

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 returns current best crypto casino bonuses with decoded fine print from T&Cs. It explicitly lists decoded fields (wagering requirement, max bonus, max cashout, free spins, min deposit, real-value score) and distinguishes from the headline marketing percentage. This is a specific verb+resource with clear scope.

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 example queries like 'best crypto casino bonus' and 'deposit match with low wagering', and an example call with {'limit': 5} or {'casino': 'BetUS'}. However, it does not explicitly state when NOT to use this tool or compare it to siblings like compare_casinos or top_casinos, which would help clarify differentiation.

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 carries full burden. It transparently discloses that data comes from hand-run tests (not scraped) and outlines the return fields. It does not mention failure modes, rate limits, or authentication, but the core behavior is well-communicated.

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 long, front-loaded with the primary purpose, and contains no redundant information. Every sentence earns its place: the first details outputs, the second explains data source and provides usage examples.

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 is a single-parameter lookup with no output schema, the description fully covers the returned data structure, data provenance, and example usage. It includes sibling tool names for context but does not need to reference them explicitly.

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 single parameter 'name' is well-described in the schema (fuzzy-matched, case-insensitive). The description adds value by providing an example call with a concrete mapping of input to output, helping the agent understand expected values and result format.

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 retrieves a forensic audit verdict for a single crypto casino, listing specific outputs (trust score, audit status, live test, license, review link). It distinguishes itself from siblings by emphasizing data from hand-run real-money tests, not scraped reviews.

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 concrete usage examples and question contexts (e.g., 'Is Duelbits legit?'). It implies single-casino lookups without explicit when-not-to-use or sibling alternatives, but the context is clear enough for an agent to infer appropriate use.

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 metrics compared (trust score, KYC, withdrawal speed, license) but does not specify behavior for invalid names, exact name matching requirements, output format, or rate limits. Some transparency is present, but gaps remain for a read/comparison tool.

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 plus an example call, with no filler. Every sentence adds value: first states scope and data fields, second gives use cases, third shows exact JSON format. Highly efficient and front-loaded.

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 one parameter, no output schema, and no annotations, the description adequately explains the input but lacks details about the output (e.g., format, what 'side by side' means) and error handling for missing casino names. It is complete enough for basic use but leaves open questions for an AI agent.

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

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

The input schema has zero description coverage for the 'names' parameter. The description adds meaning by specifying it is an array of 2-5 casino names, listing example queries, and providing a JSON example call. This adds clear semantic value beyond the bare schema.

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 'compare' and the resource 'crypto casinos' on specific WagerX audit data (trust score, live test, KYC, etc.). It distinguishes the tool from siblings like 'check_casino' (single casino) and 'top_casinos' (ranking) by specifying side-by-side comparison of 2-5 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 provides explicit example queries ('Duelbits vs BC.Game?', 'Which is safer, Stake or Rollbit?') that tell the agent exactly when to use this tool. While it does not list exclusions or alternatives, the usage context is clear and directly tied to comparative questions.

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 supplied, the description carries the behavioral burden. It explicitly states 'This lookup never executes remote tools' and enumerates output categories like endpoints, provenance, evidence status, access model, and limitations. It does not discuss auth or rate limits, but for a read-only directory lookup this is adequate.

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?

Three dense sentences with no filler: the main action is front-loaded, the return payload is summarized, and use cases plus a safety guarantee are packed efficiently. Every sentence earns its place.

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

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

Given one free-text parameter and no output schema, the description is largely complete: it names the data returned, the directory scope, example queries, and the non-execution guarantee. It does not specify exact response formatting or error behavior, but those are minor for this lookup tool.

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 documents the single `query` parameter at 100% coverage, describing it as a provider, product, domain, protocol, or directory question. The description adds concrete example queries and clarifies the style of questions that work, going slightly beyond the schema without being redundant.

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 opens with a specific verb ('Look up') and names the exact resource: official and community MCP/A2A products in the WagerX Agentic Gambling Index. It also clarifies what the tool returns and that it never executes remote tools, distinguishing it clearly from the casino-focused siblings.

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?

It provides concrete example queries such as 'What is the official Crypto.com MCP?' and 'Is ahel.io in the directory?', making the intended use explicit. It does not explicitly list exclusions or alternatives, but no sibling tool appears to cover this directory lookup, so the context is clear enough.

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, and it does so thoroughly. It states results are deterministic and aggregate-only, clarifies that activity counts are requests rather than unique agents, and promises privacy guarantees by never exposing raw queries, IP addresses, user agents, or geography.

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?

Three dense sentences with the core purpose front-loaded, followed by scope constraints and privacy guarantees. Every sentence earns its place; there is no filler or repetition of schema content.

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 four enum scopes and no output schema, the description helps an agent grasp the purpose and privacy posture but omits scope-by-scope return semantics and the behavior of 'period' when scope is not 'activity'. An agent could call the tool correctly at a high level but would still be uncertain about what shape each scope's statistics take.

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 'period' with a description but 'scope' has only enum values with no explanation. The description maps two scopes to their meanings ('endpoint observations' to endpoints, 'machine requests' to activity) and implies the Index maps to overview/catalog, but it does not fully disambiguate each scope or clarify how 'period' behaves for non-activity scopes.

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 ('Return') and names a precise resource ('selective WagerX Agentic Index', 'MCP/A2A endpoint observations', 'machine requests received by WagerX'), clearly communicating what the tool produces. It is specific enough to stand alone, though it does not name sibling tools for explicit differentiation.

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 gives clear context for when to use the tool: aggregate-only statistics, with explicit exclusions like 'Official provenance, editorial status and endpoint health remain separate.' It does offer an implicit when-not signal by declaring those categories are out of scope, though no alternative tools are named explicitly.

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 explains the tool is a read operation that returns a list sorted by recency and describes the fields in each audit entry. It does not discuss rate limits, required authentication, or data freshness, but for a simple list tool this is sufficient 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 two sentences plus example queries and an example call, all front-loaded. Every part adds value: purpose, content, and usage hints. There is no redundant or verbose language.

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 tool is simple (one optional parameter, no output schema). The description covers what the tool lists, the sort order, the fields in each result, and provides natural language queries. It is complete enough for an agent to understand the tool's functionality and typical use cases.

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% (the parameter 'limit' has no schema description), so the description must compensate. However, the description only shows an example call with limit:5 and does not explain the parameter's meaning, default value, range, or effect. The agent must infer or look at the schema for constraints, which is inadequate.

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 'List' and the resource 'most recent WagerX real-money casino audits' with explicit sort order 'newest first'. It also describes the content of each audit (deposit/withdrawal amounts, payout timing, support response), making the tool's purpose distinct from sibling tools like best_bonuses or list_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 provides concrete example queries ('What are the latest casino audits?', 'Which casinos were tested this week?') and an example call, guiding the agent when to use the tool. It does not explicitly state when not to use it or list alternatives, but the purpose is narrow enough that confusion with siblings is unlikely.

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 present, so the description must disclose behavioral traits. It states the tool returns a full list with trust score and audit status and takes no arguments, which is adequate for a read-only listing. However, it does not specify authentication requirements, return format (e.g., list of objects), or performance implications, leaving moderate gaps.

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 exceptionally concise with two sentences and 35 words. It front-loads the purpose, then immediately gives usage guidance, and ends with the parameter note. Every sentence earns its place with no redundancy or extraneous detail.

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 tool with no parameters and no output schema, the description covers the essential: what is listed (all casinos with trust score and audit status) and how to use it. It mentions relevant output fields, which compensates for the missing output schema. Minor omission: it could explicitly state that the output is a list of objects, but the context is sufficient.

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 and schema coverage is 100% (empty schema). The description reinforces this with 'Takes no arguments', which adds clarity but does not add new semantic meaning beyond what the schema provides. Per guidelines, baseline for 0 params is 4, and the description meets that.

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 'List' and the resource 'every casino WagerX holds forensic audit data on (40+ operators)', specifying the output includes trust score and audit status. It differentiates from siblings by explicitly naming check_casino as a follow-up tool and implying coverage discovery, which distinguishes it from other list/filter tools.

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 usage scenarios: 'Use to discover coverage before calling check_casino' and 'for which casinos does WagerX cover?'. It notes it takes no arguments, simplifying invocation. However, it does not mention when not to use or alternatives beyond check_casino, leaving some inference to the agent.

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

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

With no annotations, the description carries the full transparency burden. It discloses the sort order, the included evidence types, and the interpretive caveat that this is operating history, not a safety ranking. It does not detail output format or pagination, but for a no-argument list tool this is adequate.

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 compact, front-loaded with the core purpose, and every sentence earns its place: purpose, caveat, usage triggers, and argument confirmation. There is 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 simple zero-parameter, no-output-schema tool, the description is complete. It tells the agent what the tool returns, how it is ordered, what caveats apply, and when to use it. Nothing essential for invoking it correctly is missing.

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 takes zero parameters, so the description only needs to confirm that, which it does with 'Takes no arguments'. Per the baseline for zero-parameter tools, no additional parameter documentation is 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 states a specific action (List) with a clear resource (crypto casino brands) and specifies ordering (documented launch year, oldest first) and associated content (audit evidence and ownership/licensing caveats). The explicit 'not a safety ranking' caveat separates it from audit or ranking-oriented siblings.

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?

It gives explicit natural-language trigger phrases ('oldest crypto casinos', 'established bitcoin casinos', 'long-running gambling sites') that tell an agent exactly when to select this tool. It also provides a negative scope marker ('not a safety ranking') to prevent misuse.

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 discloses a key behavioral trait: casinos may be mid-audit and are never scored before withdrawal test completion. It also states the tool takes no arguments. This adds value beyond the empty schema, though rate limits or auth requirements are not mentioned (not critical for a read-only 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?

The description is three sentences, front-loaded with the primary purpose, followed by usage examples and a parameter note. Every sentence earns its place; there is no fluff or repetition. It is 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?

The tool is simple (no parameters, no output schema) but the description does not explain what the returned casino entries contain (e.g., name, audit status, score). Given the lack of output schema and the presence of sibling tools like check_casino, more detail on the return format would improve completeness. As it stands, it is adequate but has a clear gap.

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 no properties and schema description coverage is 100%, so baseline is 3. The description explicitly states 'Takes no arguments', which confirms the absence of parameters and removes any ambiguity. This adds clarity beyond the schema alone, justifying a score of 4.

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 returns 'newest crypto casinos on the WagerX radar', which is a specific verb+resource pair. It also notes inclusion of mid-audit casinos, distinguishing this from sibling tools like list_casinos (all casinos) and top_casinos (ranked). The example queries reinforce the unique purpose.

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 example queries ('new crypto casinos 2026', 'newest bitcoin casinos worth trying') that indicate when to use this tool. It does not explicitly exclude use cases or mention alternatives, but the context is clear enough for an agent to select it over 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?

Because annotations are absent, the description carries the full disclosure burden. It reveals the registry-based resolution, URL-domain filtering, explicit missing/unavailable states, and the research-not-legal-advice caveat, which are genuinely non-obvious behaviors. This is strong transparency for a read-style research tool.

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 information-dense sentences plus a short JSON example. It front-loads purpose, then filtering and behavior, with the example last; no sentence is wasted.

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 two-parameter tool with no required fields and no output schema, the description covers the core invocation details: jurisdiction semantics, limit via example/default, registry-based resolution, filtering, and the global-list fallback. It does not enumerate the shape of the returned regulatory context or enforcement records, but that is not necessary for correct invocation; the missing/unavailable-state note helps set expectations.

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 coverage is only 50%: limit has no schema description. The description partially compensates with a concrete example (including limit: 5) and by noting that jurisdiction is optional, but it never states that limit controls the number of records returned. Since the schema doesn't document limit and the description doesn't fully disambiguate it, this dimension lands at a 3.

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 opens with a specific verb and resource ('Get gambling regulatory context ... plus recent source-linked enforcement records'), which immediately identifies the tool's function. It further clarifies scope through the approved regulator registry and exclusion rule, making it clearly distinct from the casino/bonus sibling tools.

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: jurisdictions resolve through an approved registry, records are filtered by approved domains, and omitting jurisdiction yields a bounded global list. It does not explicitly name sibling alternatives or state when not to use the tool, so it stops short of full exclusion guidance.

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 explains the ranking criteria (payout speed, KYC, support, license) and the audit/testing requirement, which adds transparency about the data source. However, it does not discuss whether results are cached, how frequently updated, or any rate limits, leaving some behavioral aspects opaque.

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

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

The description is extremely concise: three sentences. The first sentence defines the tool's purpose and criteria, the second provides example search queries, and the third shows a code example. Every sentence earns its place, 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 has only one parameter and no output schema, the description covers the core use cases well. It explains what ranks casinos, provides example queries, and shows usage. It could optionally mention that results are sorted by trust score or that the response is a list of casino objects, but this is not essential for a simple ranked list tool.

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 for the single 'limit' parameter. It explicitly explains that limit controls how many casinos to return, with a default of 10 and max of 25 (as per schema). The example call {'limit': 10} reinforces usage. This adds meaning beyond the schema's basic type/numeric constraints.

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 returns ranked crypto casinos based on a specific audit trust score, with a clear filter for only tested casinos. It distinguishes itself from sibling tools like 'list_casinos' or 'new_casinos' by focusing on trust-ranked, verified 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 provides specific query examples (e.g., 'best crypto casino 2026', 'safest bitcoin casino') and an example call {'limit': 10}, giving the agent clear context for when to use this tool. However, it does not explicitly state when NOT to use this tool vs siblings like 'compare_casinos' or 'check_casino', missing a small gap in exclusion guidance.

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