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

With no annotations provided, the description bears the full burden of behavioral disclosure. It adds important context by stating that full detail is returned for each URL and that the tool 'counts one scan per URL against the caller's plan,' which is a meaningful quota/billing behavior. It doesn't cover error handling or auth, but those are less critical for this read-style 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?

Four sentences, all functional: capability, use case, alternative, and quota impact. Key information is front-loaded, with zero 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?

For a tool with one well-documented parameter and no output schema, the description covers purpose, usage, routing, and plan impact. Nothing essential for selecting or invoking the tool 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 already documents the single parameter well, including min/max items and that bare domains are fine, so schema coverage is 100%. The description reinforces the batch aspect and return detail but adds no new parameter-level semantics 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 states a specific verb and resource: 'Detect the technology stacks of up to 25 websites in one call.' It clearly distinguishes itself from the compare_stacks sibling by noting batch detection versus side-by-side comparison, and from lookup_tech_stack by explicitly framing this as the multi-URL version.

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 when-to-use guidance: 'when working through a list of prospects, competitors, or portfolio sites.' It also names an alternative and the condition for choosing it: 'If you want a side-by-side view of who uses what, use compare_stacks instead.'

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 burden of behavioral disclosure. It discloses the output format ('side-by-side table'), the quota impact ('Counts one scan per URL against the caller's plan'), and the allowed URL count ('2-25'). It doesn't mention failure handling or authorization, but the essential behavioral traits are present and it correctly implies a read-only scanning 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?

Three concise sentences: the first states the core function, the second gives examples, and the third provides the sibling alternative and quota note. It is front-loaded and economical, though the example questions add length without new operational 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 2-parameter tool with 100% schema coverage and no output schema, the description covers the essential pieces: what it does, the output shape, the quota, and the alternative tool. It doesn't address edge cases like invalid URLs, but that is beyond what an agent needs to make a correct call decision. The description is complete enough for safe and effective selection.

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 adds minor context by mentioning '2-25' websites and that bare domains are fine, but this largely repeats the schema's parameter descriptions. No new meaning is added beyond what the schema already documents, so the score stays at the baseline.

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 clear verb ('Scan') and resource ('2-25 websites'), and specifies the exact output ('side-by-side table of which technologies each one uses'). It explicitly distinguishes itself from the sibling batch_lookup by naming the alternative, so an agent can immediately tell them apart without opening either schema.

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 concrete example queries ('which of these prospects use HubSpot vs Salesforce?') that illustrate when to use this tool. It also states the exclusion case ('For the full per-site detail rather than a table, use batch_lookup'), providing explicit routing 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 fully carries the behavioral burden and excels: it discloses the detection method (HTML and server headers), explains the client-side JavaScript limitation, and interprets sparse results as 'little detectable, not that the site runs nothing.' It even notes plan quota impact. This is exemplary behavioral disclosure.

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 dense and front-loaded with the core purpose, followed by targeted use cases and critical caveats. Every sentence contributes: the category list specifies scope, the usage sentence gives context, the JavaScript limitation is vital, and the quota note is actionable. 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?

Despite having no output schema, the description fully prepares an agent to use the tool correctly: what it detects, how detection works, its limitations, how to interpret results, and a billing/quota consequence. Nothing essential for invoking this single-parameter tool 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 schema already documents the url parameter at 100% coverage, including the bare-domain note, so the description adds little parameter-specific meaning. Baseline 3 is appropriate because the schema does the heavy lifting and the description complements it without needing to repeat 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 a specific verb and resource ('Detect the technologies a website runs') and enumerates the technology categories covered, which is unambiguous. However, it does not explicitly differentiate itself from the sibling tools batch_lookup and compare_stacks, so it falls short of a 5.

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 use-case context: 'research a competitor, qualify a sales prospect, or check what a site is built with.' It does not, however, mention when to prefer this tool over the sibling tools or when to avoid it, so it lacks explicit exclusions or alternative routing.

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