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

The annotations already declare readOnlyHint=true, which the description reinforces. It adds valuable behavioral information by stating 'Free - does not consume a credit', a detail not captured in the annotations. This goes beyond the annotation's simple read-only hint.

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 with no redundant information. It leads with the core action, then provides the usage context and the cost-saving behavior. 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?

For a tool with zero parameters and no output schema, the description fully covers what it does, when to use it, and the critical behavior of not consuming credits. Nothing needed to call 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 input schema has no parameters, so there is no parameter description to provide. Per the rubric, a zero-parameter tool receives a baseline score of 4 because no parameter explanations are needed.

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 'check' and a clear resource 'credit balance', making the tool's purpose unmistakable. It is clearly distinguished from the sibling lookup tools, which all involve looking up data rather than managing credits.

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 states when to use the tool ('before a batch of lookups') and explains the benefit ('so you know how many you can afford'). While it does not mention alternatives, none are relevant for checking credit balance, so the guidance 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?

The annotations only indicate read-only and open-world hints. The description adds valuable behavioral detail: results come as a compact table, duplicates and www variants collapse and are charged once, and misses/malformed domains are free. Pricing behavior is disclosed clearly and does not contradict the annotations.

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

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

The description is compact and front-loaded with the core purpose followed by usage guidance and cost details. Every sentence earns its place, and the key differentiator (one call for up to 20 domains) appears immediately.

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 read-only tool with no output schema, the description covers purpose, scope, batching behavior, deduplication, pricing, and free cases. Nothing essential for an agent to decide whether and how to call it 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?

Schema coverage is 100%, and the schema already documents that domains are an array of strings, bare domains, with maxItems 20. The description reinforces this but adds no new parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Look up'), a clear resource (companies behind domains), and a distinguishing scope (up to 20 domains in one call). It also differentiates from the singular sibling by emphasizing the batch capability, so an agent can immediately tell it apart.

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 explicitly says to prefer this over repeated lookup_company calls when several domains are already known, and explains the efficiency and cost equivalence. This provides direct routing guidance versus the main alternative.

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?

Annotations already provide readOnlyHint and openWorldHint, so the description does not need to re-state safety. It adds useful behavioral context beyond annotations, including the one-credit cost and the specific firmographic fields returned, which are not present in the schema or annotations.

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

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

The description is three short, purposeful sentences with no filler. It front-loads the core behavior and output, then states the use case and cost, all in under 40 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 low-complexity, single-parameter, read-only lookup, the description is complete: it explains input, output fields, when to use it, and cost. The schema fully covers the parameter format, and the annotations cover safety, so no critical information 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?

Schema coverage is 100%, with the single 'domain' parameter already documented as a bare domain with no scheme or path. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb-resource pair: given a domain, return the company behind it. It lists the exact output fields (name, industry, city, country, address, LinkedIn URL), making its purpose unmistakable and distinguishing it from domain-technology and technology lookups.

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 when to use it: when asked who owns or operates a website, or to enrich a domain into firmographics. It does not explicitly name alternatives or exclusions, but the stated use cases are clear enough for an agent to select this tool.

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

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

Beyond the readOnlyHint annotation, the description adds meaningful behavioral details: duplicate and www. variants collapse, credits are charged per domain with data, malformed domains are free, and raising per_domain does not cost extra credits. These are non-obvious traits an agent would not otherwise know.

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 longer than average but every sentence carries actionable information. It is front-loaded with purpose and usage guidance, followed by edge-case behavior and pricing, with no filler 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?

The description covers the tool's purpose, main alternative, parameter nuances, batching behavior, deduplication, and credit cost. The only mild gap is that the 'compact table' return format is not specified in more detail, but the examples and references to lookup_domain_technologies provide enough orientation.

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 100%, so the baseline is 3. The description goes further by clarifying that category means the part before the slash in lookup_domain_technologies output and that the sub-category after the slash matches nothing, plus that per_domain affects only context, not cost. This adds real value beyond the 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 opens with a specific verb and resource: 'Look up the technologies on up to 20 domains in ONE call', and immediately names the sibling tool it should not be confused with. It clearly distinguishes itself from lookup_domain_technologies by scope and level of detail.

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 prefer this tool over repeated lookup_domain_technologies calls whenever several domains are in hand, and tells the agent to switch to lookup_domain_technologies when full detail on a single domain is needed. It also explains when category should be used and when it should be omitted.

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?

Annotations already declare readOnlyHint=true, covering the safe read-only nature. The description adds valuable context beyond annotations by disclosing the cost ('Costs 1 credit') and the output structure (category and usage count). This provides useful operational insight not present in the structured annotations.

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

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

The description is two sentences with no wasted words. It front-loads the core purpose, then adds the use case and cost information. Everything earns its place and is easy to parse.

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?

Even though there is no output schema, the description explains what is returned (technologies with category and usage count). It also covers the required input (domain), the optional limit is in the schema, and the cost is disclosed. For a simple read-only lookup tool, nothing critical 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 input schema has 100% description coverage for both parameters (domain and limit), so the schema already documents them thoroughly. The description does not add any additional parameter-specific meaning beyond what the schema provides, which is the baseline for a high-coverage 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 tool's function: given a domain, list the technologies detected on it, including category and usage count. This distinguishes it from siblings like lookup_technology (which looks up a specific technology) and lookup_domains_technologies (plural, likely for multiple domains) by explicitly referencing 'a domain' and the technologies detected on it.

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 an explicit use case: 'Use this to answer what is this site built with?'. This tells the agent when to use it, but it does not mention alternatives or when not to use it, so it falls short of a 5.

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?

Annotations already mark the tool as read-only and open-world, so the description only needs to add context beyond that. It usefully discloses the 1-credit cost and clarifies the output content (adoption breadth plus top countries). No contradiction with annotations.

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

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

Three short sentences with no filler: the core behavior is front-loaded, a practical use case follows, and the credit cost is a useful closing note. 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?

For a one-parameter read-only lookup with no output schema, the description covers input, expected output, example usage, and cost. It could be slightly more precise about what 'how widely it is used' means (e.g., site count, percentage), but the description is sufficient for an agent 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 explains that 'technology' is a StackScan-specific name with examples. The tool description adds an example use case but no additional format, normalization, or edge-case guidance, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Given a technology name, return how widely it is used and its top countries by adoption.' The example 'how many sites run Shopify, and where?' makes the purpose concrete, and the technology focus clearly distinguishes it from sibling tools that target companies or domains.

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 two explicit use cases — 'size a market or compare platforms' — and provides an illustrative query. It does not explicitly state when not to use this tool or contrast it with sibling alternatives, but the usage context is strong enough for most cases.

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