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

With no annotations, the description carries the transparency burden and usefully discloses that the return is an array matching check_domain fields and that missing domains are included with a 'not_found' flag. It does not state explicitly that the operation is read-only, nor does it cover error handling for invalid entries, leaving some behavioral 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?

Three short sentences, front-loaded with the batch purpose and limit, then return shape and missing-domain behavior. No filler or repeated schema details beyond what is useful for orientation.

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 explains the return structure by reference to check_domain and covers the important missing-domain case. Minor gaps such as result ordering and duplicate handling remain, but the definition is sufficient to call the tool correctly.

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%, and the schema already documents the domains array and maxItems=100. The description repeats the max-100 limit but adds no new parameter semantics such as expected domain format or duplicate handling, so the baseline 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 states a specific batch operation ('Check multiple domains at once') and distinguishes it from the single-domain sibling by naming check_domain. However, 'check' does not explain what is being checked beyond implied dataset membership, so the purpose is clear at the batch-vs-single level but not fully specified.

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?

'Check multiple domains at once' and the max-100 limit give clear guidance on when to use this batch tool rather than a single-domain call. It does not explicitly say to use check_domain for one domain or search_domains for discovery, so alternatives are implied rather than fully stated.

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 behavioral burden. It does well on output semantics — score scale with direction (0.0–1.0, lower = less credible), plus category, traffic-light level and metadata — but says nothing about what happens when a domain is absent from the dataset, or about rate limits or data freshness.

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, zero waste, with the action and its scope front-loaded before the return-value detail. Every clause carries 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 one-parameter read lookup with no output schema, the description covers purpose, data source and return shape well. The notable gap is not-found / no-data behavior, which an agent calling with an arbitrary domain would want to know.

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% and the single parameter already documents examples and the automatic stripping of 'www.' and protocols. The description adds no parameter-level meaning beyond the schema, so the 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?

States a specific verb and resource ('Check a single domain's credibility score and category') plus the underlying dataset (CRED-1). The word 'single' implicitly separates it from batch_check, and the return fields listed make the scope fully 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?

Usage is only implied: 'single domain' hints that batch_check is the right tool for multiple lookups, but no alternative is named and no when-not condition is stated. An agent can infer the routing but must do the work itself.

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 burden; it does convey that this is a static reference enumeration with no side effects and tells what fields come back. It says nothing about result size, ordering, caching, or whether the category set is fixed, which matters for an agent planning a loop.

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 front-loaded sentence with no filler; every clause names a concrete returned field rather than restating the tool name.

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 no-param, no-output-schema, no-annotation read tool, the description gives enough to call it correctly and to know what comes back. The only shortfall is the missing hint about how it relates to get_stats.

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 there is nothing to disambiguate and the baseline of 4 applies. The description correctly implies no input is needed by describing only return content.

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?

Specific verb ('List') plus resource ('credibility categories') with an enumeration of the fields returned (descriptions, score ranges, domain counts). It is clearly distinct from check_domain / search_domains / batch_check, though it does not explicitly contrast itself with the potentially confusable sibling get_stats.

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?

Usage is only implied: an agent can infer this is the reference-lookup call to make before filtering by category, but the description never states when to call it or when to prefer get_stats instead. No exclusions or prerequisites are given.

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 exist, so the description carries the full burden; 'Get' implies a read-only, zero-side-effect operation and the mention of 'dataset version' hints at data vintage. However, it says nothing about whether figures are cached or live, how often they refresh, or the cost/latency of the call.

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 that front-loads the verb and resource and then enumerates returns with a colon list. No filler or redundancy.

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

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

With no output schema, the description usefully enumerates the returned statistics (domain count, category breakdown, version), which is what an agent most needs. The only gap is behavioral context around freshness and whether the summary is cached.

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 no parameters and the schema is an empty object, so baseline 4 applies. The description correctly adds no parameter discussion, since there is nothing to parameterize.

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?

States a concrete verb ('Get') and resource ('summary statistics') and enumerates the three returned fields, so an agent knows exactly what comes back. It does not explicitly distinguish itself from get_categories, which is the closest sibling and could plausibly be confused with the 'per-category breakdown' portion.

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?

There is no statement of when to call this versus alternatives such as get_categories or search_domains, and no prerequisites or exclusions are given. Usage is only inferable from the fact that it returns an overview.

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 behavioral burden, yet it discloses almost nothing beyond what the schema already says. It does not say whether matching is anchored, how results are ordered, whether the search is exhaustive or truncated, or what an 'entry' contains — only the regex support is behavioral, and that is duplicated in the schema.

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, zero filler, with the core purpose front-loaded before the usage hint. 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?

With no output schema and no annotations, the description should hint at what a match returns (domain entries, metadata) and how results are bounded, but it stops at the input side. Adequate for a simple two-parameter search, but there are clear gaps for an agent interpreting 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?

Schema description coverage is 100%, with both 'pattern' and 'limit' fully documented including the default of 20 and max of 200. The description adds no parameter detail beyond the schema, so the 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?

States a specific verb (search) and resource (domains) with the matching mode (substring or regex). The distinction from check_domain is implied by 'search' vs 'check' but never made explicit, so sibling differentiation is left to inference.

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?

Gives two concrete use cases: discovering entries related to a topic and checking variations of a domain name. It never explicitly excludes or routes to check_domain/batch_check, which risks overlap with 'checking variations,' but the context is clear enough to act on.

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