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

No annotations are provided, so the description carries the full burden, and it discharges much of it: failures are isolated per URL ('a single failure does not abort the batch'), results carry an 'error' field, and empty/incomplete batches still return a valid object. It omits auth/credential requirements, rate limits, and timeout behavior, which keeps it short of a 5.

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?

Front-loaded one-line summary followed by labelled 'What it does / How to use / Returns' sections; every bullet adds operational value. The section headings cost a little space but the content is dense and non-repetitive, so it stays efficient.

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 must cover both behavior and returns, and it does explain the return shape (per-URL markdown/tokens/error plus aggregate totals). It is complete enough to call correctly, though it leaves prerequisite/auth and rate-limit concerns unstated.

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%, so the baseline is 3, but the description adds cross-parameter semantics the schema cannot express: mode/css_selector/headers/cookies/proxy apply to every URL in the array, concurrency (1-10) caps parallel workers with advice to keep it modest, and each URL is independent. That is real meaning beyond the per-parameter docs.

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+resource+scope: 'Batch extract token-optimized Markdown from multiple website URLs concurrently, with aggregate token statistics.' It explicitly relates itself to the sibling extract_web_content as the same pipeline applied to an array, so an agent can tell the batch tool from the single-URL tool without opening a 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?

Gives clear context: 'Use when you need many pages at once — docs, a list of product pages, a set of sources to compare,' and explains how global options (mode, css_selector, headers/cookies/proxy) apply across all URLs, plus concurrency guidance. It does not state when NOT to use it (e.g., 'for a single URL use extract_web_content'), so routing is implied rather than explicit.

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 discloses output artifacts (verified emails, social links, founders, a <300-token Markdown summary) and the token-optimized engine, which is useful behavioral context. But it omits cost/latency implications, rate limits, auth requirements, or what happens when the crawl fails.

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 tightly written sentences that front-load the action and then the outputs. No filler, no restatement of 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 an 8-parameter, no-output-schema tool, the description covers the essential return content (emails, socials, decision makers, markdown summary) so an agent knows what comes back. Missing only failure/edge-case behavior and any alternative-tool routing.

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 8 documented parameters and three enums, so the schema already explains every field. The description adds no parameter-level detail beyond what is there, so 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 (enrich) and resource (business lead) plus the mechanism (crawling the official website) and enumerates the concrete outputs. It is clearly distinct from generic extract_contacts/crawl_website siblings, though it never names an alternative tool.

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?

No when-to-use guidance, no prerequisites, and no mention of when to prefer this over crawl_website, extract_contacts, or batch_extract_web_content. The only selection hint is the 'deeply' qualifier and the mode enum, which lives in the schema, not the description.

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 disclosure burden. It usefully reveals that results come from Google Maps and enumerates the extracted fields, but says nothing about rate limits, quotas, permissions, or whether the operation is idempotent/read-only, and the schema's 1-100 'limit' cap is never mentioned in prose.

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 tight sentences: the first front-loads what the tool does and its engine, the second enumerates return fields. With no output schema present, that second sentence earns its place rather than being 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?

Given no output schema, the field list is a valuable completeness contribution, and the required query parameter is covered by the schema. Gaps remain around sibling differentiation and result-volume expectations, but the core contract is conveyed.

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%, so all four parameters (query, location, lang, limit) plus defaults and bounds are already documented. The description adds no syntax, format, or interplay guidance (e.g. how location combines with query), so it sits 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?

Names a specific verb ('Discover') and resource (local businesses/establishments/B2B prospects) and even names the underlying engine (Google Maps), so the capability is unambiguous. It does not, however, distinguish itself from the very similar sibling 'search_places', which appears to overlap heavily.

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 'B2B prospects' hints at the lead-generation use case, so usage is implied rather than stated. There is no explicit when-to-use, when-not-to-use, or reference to the near-identical 'search_places' sibling that an agent would need to choose between them.

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 discloses meaningful behavior: results are 'verified', extraction follows links into contact/about subpages, and it returns compact structured data rather than raw DOM. It omits failure modes, JS-rendered page handling, timeouts, and whether any auth or rate limits apply.

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 verb and extracted resource before any justification. No filler; the second sentence functions as a routing signal rather than padding.

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?

There is no output schema and no annotations, so the description does well by naming the returned field categories. It is nearly complete for a two-parameter read tool, with only minor gaps around output shape and behavior on dynamic sites.

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%, so both 'url' and 'deep_scan' are already documented, including the subpage examples and the default. The description only restates that subpages are scanned and adds no syntax or format detail beyond the schema, so the baseline of 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 (extract), a specific resource (contact information), and enumerates the exact output types (emails, phone numbers, WhatsApp links, social profiles). This enumeration clearly separates it from siblings like extract_web_content and extract_structured_data, which target generic or arbitrary content.

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 closing sentence implies a use case by contrasting the tool with 'feeding the entire raw DOM to the LLM', which hints at when to prefer it. However, no sibling tools are named, no prerequisites or exclusions are given, and nothing tells the agent when NOT to use it (e.g. when only page text is needed).

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, and it delivers real behavioral detail: missing selectors are omitted rather than failing the call, and non-http/private URLs return isError: true. It adds defaults for include_metadata but does not cover auth, rate limits, or cost/latency differences between fast and deep mode.

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?

Front-loads the one-line summary, then organizes detail under 'What it does', 'How to use', and 'Returns' headings, which makes scanning easy for a 9-parameter tool. It is slightly verbose with the section headers and parenthetical enumerations, but every line carries actionable 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?

No output schema exists, so the description explains the return shape, partial-failure behavior, and error signal, and documents the defaults for include_metadata and the schema parameter. An agent has everything needed to invoke and interpret results without consulting other tools.

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 baseline is 3, but the description adds genuine semantic value: schema keys become the JSON keys and values are CSS selectors (with a concrete example), include_metadata=true adds the OG/author/date block and false yields a lean result. mode/css_selector/headers/cookies/proxy are punted to a sibling tool's semantics, which is a mild indirection.

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: 'Extract structured JSON — page metadata ... and/or a custom CSS-selector schema — from a website.' It also implicitly distinguishes itself from the prose-returning sibling by framing output as 'fields instead of prose.' An agent can tell what it does and how it differs from extract_web_content without opening the 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?

Gives concrete use cases ('product listings, news/metadata, schema-driven extraction') and a selection rule ('any time you need fields instead of prose'), plus a clarifying case ('Leave schema empty to get metadata only'). It stops short of explicitly naming extract_web_content as the alternative for prose, so the routing is implied rather than spelled out.

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 discloses the return shape ('list of URLs with source and modification metadata'), which is useful, but says nothing about read-only nature, network/rate-limit behavior, robots.txt compliance, or failure modes for a network-fetching 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?

Two tight sentences: the purpose and mechanism lead, and the return/usage context follows. Every clause carries information with no padding or restatement of 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?

No output schema exists, and the description does cover the return contents, which is the main gap it needed to fill. Remaining omissions are behavioral (safety/rate/permission context) rather than structural, so it is largely complete for a 2-parameter discovery 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 coverage is 100%, so both parameters (url, max_links) are already fully documented in the schema, including the default and 1-500 range. The description adds no parameter-level detail beyond that, 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 ('Discover the architecture and page inventory of a website domain') plus the mechanisms used (sitemaps, robots.txt, link graphs). This clearly separates it from extraction siblings, though it does not name the closest sibling (crawl_website) explicitly.

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?

'Useful for exploring a site before targeted crawling' gives a concrete usage context that positions it ahead of crawl_website/extract_web_content. It stops short of naming an alternative or stating when not to use it, so it is strong 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 behavioral burden. It discloses the data source (Google Maps) and the returned entity fields, which is useful context, but it omits read-only confirmation, auth requirements, rate limits, pagination, and result-limit behavior beyond what the schema states.

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 wasted words. The verb and resource come first, and the extracted field list is compact enough to remain useful without bloating the definition.

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?

There is no output schema, so the description appropriately compensates by naming the returned fields (title, address, phone, website, coordinates, rating). For a simple two-parameter search tool, this is nearly complete, though it still leaves usage relative to local-lead siblings unaddressed.

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 both the query syntax examples and the limit default/max are already fully documented in the input schema. The description adds no additional parameter meaning, which meets the baseline expectation when the schema does the heavy lifting.

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 (local businesses, places, establishments on Google Maps) and lists the structured fields returned. It is clear enough to separate from web-content and crawling siblings, but it does not explicitly differentiate itself from the potentially overlapping discover_local_leads or extract_contacts 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?

Usage is implied by the verb 'Search' and the Google Maps/local-business scope, so an agent can infer when it applies. However, there is no explicit when-to-use guidance, no exclusions, and no named alternative among the sibling tools when local lead discovery or contact extraction might be preferred.

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