Company Intelligence Tools — Zinin M2M Hub

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

Annotations already provide readOnlyHint=true and destructiveHint=false, indicating safe read-only access. The description adds useful context about API usage and cost ($0.01/call, x402 USDC) but does not explain pricing model details, rate limits, or pagination behavior for larger company batches. The description is consistent 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?

The description is a single, dense sentence with no filler. It front-loads the action and resource, then enumerates signals, then sets boundaries. Every clause adds value, and the brevity does not sacrifice clarity.

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 4 parameters with 100% schema coverage, no output schema, and simple structure, the description is complete. It covers what inputs produce, how results are categorized (matched roles, remote share, new by window), and caveats (no login/scraping needed). An agent can confidently invoke the tool without needing additional context.

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 good descriptions for each parameter. The description goes further by explaining how these parameters combine to produce the signal, e.g., 'newWindowDays' is used to filter 'new' postings. It also clarifies the provider:token format for companies, which is essential for correct invocation. However, there is no description of default behavior when 'keywords' or 'newWindowDays' are omitted beyond schema defaults.

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 specific verbs like 'Pull every open role' and clearly identifies the resource as 'a company's public job board from Greenhouse, Lever, Ashby' with concrete signals like 'role count, which functions are growing, remote share, and what's new.' It is clearly distinguishable from siblings like 'layoff-tracker' or 'hiring-trend-index' which cover different hiring-related concepts.

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 tells the agent when to use this tool: to turn job postings into a 'buying/expansion signal.' It also states what it does NOT require ('No login, no scraping, no proxies'), which helps agents exclude this tool for scenarios requiring authentication or custom scraping. The emphasis on public job boards further clarifies when the tool is appropriate.

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, openWorldHint=true, and destructiveHint=false. The description adds value beyond these by specifying 'Keyless, no login – built for AI agents and sales' (ease of access), '$0.01/call, x402 (USDC on base)' (pricing and payment method), and 'live GLEIF registry match' (real-time data freshness). No contradictions with annotations. A small deduction for not mentioning rate limits or latency, but the added context is strong.

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 a pricing note – very compact. It is front-loaded with the core purpose and then details the output. The pricing note is helpful but slightly disrupts flow; could be placed in a separate field. No fluff, every sentence adds value. A 5 would require even tighter structuring or separation of concerns, but this is 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?

Given no output schema, the description effectively conveys what the tool returns: a 'company card' with website tech stack (CMS, ecommerce, key tech) for domains and a GLEIF registry match (legal name, jurisdiction, status, LEI). This covers the core output. The two parameters are well-documented in the schema. The description could be improved by mentioning the response format (e.g., JSON structure) but is still complete enough for an AI agent to select and invoke 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% (both companies and maxConcurrency have descriptions in the schema). The description does not add significant new parameter-level detail beyond the schema; it mentions the pipe syntax for country hints (e.g., 'stripe.com | US') but that is already present in the schema description for companies. The description's main value is at the tool level, not per-parameter. 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 clearly states the specific action: 'Turn a domain or company name into one unified company card' with two explicit output components: website tech stack and live GLEIF registry match. This uniquely distinguishes the tool from siblings like company-registry-enricher (which focuses only on registry data) or website-contact-extractor (which extracts contacts). The verb 'turn' and resource 'domain/company name' are specific and 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?

The description mentions it is 'built for AI agents and sales' but provides no explicit guidance on when to use this tool vs. alternatives. It does not state when not to use it, nor does it reference any sibling tools. For example, it could have noted that company-registry-enricher provides deeper registry data or that counterparty-risk-rollup covers risk, but it does not. This lack of comparative guidance leaves the agent to infer usage context.

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 already declare readOnlyHint: true and destructiveHint: false, so no contradiction. The description adds behavioral context: it uses GLEIF (free, no key), charges $0.01/call in USDC, and optionally needs a Companies House API key for extra data. This moves beyond the annotations by disclosing the cost, third-party data sources, and optional enrichment behavior.

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 a single, dense sentence with key information front-loaded (purpose, outputs, free source) and optional details later (UK API key, cost). It is concise and wastes no words, though it could be split into two sentences for easier scanning.

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 moderately complex input (array with optional country hints, optional API key) and no output schema, the description covers inputs, optional enrichment, data source, and cost well. It lacks details on the output format or error cases (e.g., what happens if a name is not found), but the annotations and schema provide a solid foundation. Completeness is good but not exhaustive.

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%, but the description still adds meaning: it explains the pipe syntax for country hints, that LEI or UK company numbers bypass name search, and how the optional API key unlocks directors/SIC codes. This exceeds the baseline of 3 by enriching the agent's understanding of input formatting and optional features.

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 turns a company name, LEI, or UK company number into an official registry card with specific fields (legal name, status, jurisdiction, registered address, LEI). It also mentions optional enrichment with UK directors and SIC codes. This purpose is distinct from siblings like 'company-lookup' or 'taiwan-company-kyb-lookup', which likely have different scopes or geographies.

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 implies when to use the tool (for identity and registration data enrichment) and what inputs are accepted (name, LEI, UK company number). It also explains how to use the optional API key for UK data and notes a cost of $0.01/call. However, it does not explicitly state when not to use it or compare it to siblings like 'company-lookup' or 'litigation-check', leaving room for interpretation.

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 description adds meaningful behavior beyond annotations: it discloses the cost ($0.08/call), the partial-card behavior, and the listing of missing signals. This is valuable operational context that annotations do not provide.

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 purpose, and includes pricing in the second sentence. Every clause earns its place with no fluff.

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 mentions 'several signals' but does not enumerate them or explain what 'changed' means in the output. Since there is no output schema, the description should compensate more, especially because the schema hints at hiring/funding signals that are not described. The tool's composition behavior is left implicit.

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 itself contains detailed parameter descriptions (e.g., domain, baseline_key, role_keywords, company_name_override). The description does not add parameter-level meaning, so a 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 clearly states the tool's verb ('summarising') and resource ('what changed across several signals in the past week'). The phrase 'One card per competitor' distinguishes it from sibling tools like counterparty-risk-rollup, which targets risk, not general competitor changes.

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 a clear usage context: weekly competitor monitoring (past week). However, it does not explicitly state when not to use it or point to alternatives, so it stops 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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds useful behavioral context: 'Keyless sources only' indicates no API keys are needed, 'combined into one row per company' clarifies aggregation behavior, and 'documented risk score' hints at how results are presented. It does not contradict annotations, and the extra details go beyond annotation coverage.

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 core description is one dense sentence that front-loads the tool's purpose and components, followed by a brief pricing note. Every word contributes meaningful information with no fluff or repetition of schema details.

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 four well-documented parameters and no output schema, the description covers the essential behavior: the composite nature of the rollup, the per-company row output, and the risk score. It could be more explicit about what the 'documented risk score' contains or how the row is structured, but for the tool's complexity, it is sufficiently complete.

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 provides 100% coverage with rich descriptions for all four parameters, including defaults, allowed values, and per-parameter cost implications. The tool description itself does not add new parameter-level insights beyond referencing the checks, 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 uses a specific verb 'Screen' and resource 'counterparty', clearly listing all four data sources (OFAC/EU sanctions, GLEIF registry, litigation history, hiring signal) and the output format ('one row per company with a documented risk score'). This distinguishes it from sibling tools like sanctions-screening or litigation-check, which are single-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 phrase 'Screen a counterparty in one call' implies this is the comprehensive, combined alternative to individual checks, giving clear context for when to use it. However, it does not explicitly state scenarios where a specialized sibling tool would be preferred, so the guidance is clear but not exhaustive.

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 and destructiveHint. The description adds important behavioral context: the null-when-uncertain policy for mailbox existence, which prevents false certainty, and the pricing model per call. This goes beyond what annotations provide.

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 packs the core check scope, the null behavior caveat, and pricing into a compact format, every sentence earning 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 simple tool with no output schema, the description provides enough context: what is checked and the null policy. However, it doesn't describe the output shape (e.g., per-email result objects), but the null behavior hints at it. Still, it is sufficiently complete for an agent to invoke 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 coverage is 100% with descriptions for both 'emails' and 'maxConcurrency'. The description does not add parameter-specific semantics beyond the schema, 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 clearly states the verb 'checks' and specifies the resource and scope: syntax, domain, and mail exchangers. It distinguishes this tool from siblings focused on companies and signals, making its purpose unmistakable.

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 implies usage context by listing what it checks and the null behavior, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. This is clear context without explicit 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?

Beyond the annotations (readOnly=false, destructive=false), the description discloses key behavioral traits: it is stateful (remembers previous checks), the first run creates a baseline, and it only returns deltas. It also adds cost information. This significantly enhances understanding of side effects and expected behavior.

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: the first states the core function in a compact, front-loaded manner; the second adds essential cost/mechanism details. 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?

The description covers the purpose, statefulness, first-run behavior, and cost. There is no output schema, but the description does not specify the exact structure of returned data. However, given the schema's rich parameter descriptions and the simplicity of the tool, it is mostly complete.

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 covers all parameters with detailed descriptions (100% coverage), including baseline_key and sinceDays. The tool description itself does not add parameter-level meaning beyond what the schema already 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 states a specific verb ('watches') and resource ('funding announcements for a saved filter') and clearly distinguishes from siblings by noting it returns only changes since the previous check. It also mentions the baseline behavior, setting it apart from a plain tracker.

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 clearly implies when to use this tool: for repeated monitoring of saved filters to get only new/changed announcements. It also explains first-run behavior (baseline creation). However, it does not explicitly name alternatives or say when not to use it, though the differentiation from 'funding-round-tracker' is implicit.

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 non-destructive. The description adds value by disclosing it uses official SEC EDGAR full-text search, is free, and includes a per-call cost ($0.02). It does not contradict annotations and provides useful behavioral context beyond the structured fields.

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 concise sentences plus pricing information. It front-loads the core purpose, sources, and outputs, with no wasted words. Every sentence serves a distinct purpose, making it easy for an agent to quickly understand the tool.

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 description adequately explains return values (company, filing date, document URL). It covers all four parameters via schema descriptions and contextualizes the queries. Minor gaps exist (e.g., no mention of pagination or empty result behavior), but the tool is simple enough that this 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?

Schema coverage is 100%, so the description does not need to repeat parameter details. It adds meaning by linking the 'queries' parameter to company names or sector keywords and clarifying outputs (company, filing date, document URL) in the absence of an output schema. This contextualizes the parameters without redundancy.

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 tracks SEC Form D filings by company name or sector keyword. It specifies the data source (SEC EDGAR), the outputs (company, filing date, document URL), and distinguishes itself from a sibling like 'funding-alert' by focusing on regulatory filings rather than general funding news.

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 context on when to use this tool (tracking recent private capital raises via Form D) and highlights its free, no-login nature. However, it does not explicitly contrast with sibling tools like 'funding-alert' or state when not to use it, leaving some ambiguity for 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?

The description discloses that partial results are marked as partial and list what is missing, which is a behavior not covered by annotations. It also mentions the cost per call. However, it does not explicitly mention the stateful memory behavior (tracking changes across runs), which is only implied in the schema parameter descriptions. With annotations already covering idempotence/destructiveness, 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 two sentences, front-loads the core purpose, and adds only essential notes (partial results and pricing). The pricing detail including 'x402 (USDC on base)' is slightly cryptic and arguably unnecessary for an AI agent, but it does not detract significantly. Overall, 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 description conveys the core concept and partial-result handling but does not describe the return format or fields (e.g., how role and geography are presented). Since there is no output schema, this is a gap. The rich parameter schema partially compensates, but an agent might still be uncertain about what the result object looks like.

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. The tool-level description does not add parameter-specific meaning, but the schema already thoroughly documents all three parameters with examples, defaults, and edge cases. No additional explanation is 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 clearly states the tool provides a cross-section of hiring demand by role and geography, assembled from several job sources in one call. It does not explicitly contrast with siblings like company-hiring-radar, but the aggregation wording implies a higher-level view. A more explicit verb like 'retrieves' or 'returns' would make it 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?

The description implies the tool is used for cross-sectional hiring demand analysis, but it never explicitly states when to use it versus sibling tools like company-hiring-radar or layoff-tracker. The parameter descriptions provide operational context (e.g., re-checking same slices), but the tool-level description lacks clear 'use this when' or 'not when' 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds value by disclosing pricing ($0.01/call), the lack of authentication requirements, and the data sources used. This helps the agent understand cost and access prerequisites beyond what annotations provide.

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, using two sentences and a pricing line to convey purpose, data sources, and ease of use. It is front-loaded with a question that hooks the use case, and every sentence adds essential information without 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?

Given the tool has no output schema, the description should clarify the return format. It mentions an 'intent score per company' but does not specify whether it's a number, label, or structured object. For a simple tool with 2 parameters and no nested objects, the description is somewhat complete but lacks output details needed for an agent to interpret 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 parameters (companies and maxConcurrency) already well-documented in the schema (e.g., company format options, concurrency limits). The description does not add new meaning or additional usage details for the parameters, 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 the tool's purpose: it computes an intent score by combining public hiring activity and recent news for a company, mentioning specific sources (Greenhouse, Lever, Ashby) and news categories. However, it does not explicitly differentiate from sibling tools like company-hiring-radar or funding-alert, which could provide individual signals.

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 implies the tool is for assessing market intent ('Is this company in-market right now?') and emphasizes ease of use (no login, no API keys), but it does not provide explicit guidance on when to use this tool versus alternatives. There is no mention of exclusions or when-not-to-use, leaving the agent to infer context from the tool's name and 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: 'No login, no scraping, no proxies,' cost ($0.01/call), and return format (headlines, mention count, summary). It goes beyond annotations without contradicting them, though rate limits or update frequency could add more depth.

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 concise at roughly three sentences, front-loading the core purpose and resource. It includes necessary details without excess, though the cost and payment info, while transparent, could be seen as slightly tangential to tool selection. Still, it's efficient 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?

For a simple three-parameter tool with no output schema, the description covers the main aspects: purpose, source, output format, cost, and lack of authentication requirements. Missing details like example output or error scenarios are minor given the low complexity. The description adequately complements annotations and schema.

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 each parameter having a clear description. The tool description restates some parameter info but does not add new semantic meaning beyond what is already in the schema. Baseline 3 is appropriate since the schema already 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?

The description clearly states it tracks recent tech layoffs by company or sector from Google News, with a specific purpose as a demand and recruiting signal. This distinctively separates it from sibling tools like hiring-trend-index or company-hiring-radar, which focus on positive hiring signals.

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 implies usage for layoff-related signal gathering but does not explicitly state when to use this tool versus alternatives or when not to use it. No direct comparisons to sibling tools are provided, so the agent must infer the context from the tool's purpose alone.

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 description adds useful behavioral context beyond annotations: it costs $0.05/call and reports identity confidence honestly as guessed when no override is supplied. However, it does not mention side effects (e.g., parallel checks, rate limits) which are only found in the schema descriptions. Annotations already indicate it's not read-only and not idempotent, so no contradiction.

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 a price/capability line, tightly packed with the core purpose and key caveat (identity reporting). It is front-loaded with the most important information and contains no 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 the tool's moderate complexity (3 parameters, nested objects) and no output schema, the description provides sufficient context about the tool's cost and identity handling. The rich schema fills in the parameter details. However, the description alone might not fully convey the parallel checks and cap, but that lives in the schema, so overall it's complete.

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. The description itself does not explain any parameters; it relies on the schema's detailed descriptions for domains, role_keywords, and company_name_overrides. No additional semantic meaning is added by the description.

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 ('Scores') and resource ('domains') for a clear outcome ('buying readiness'), and notes it draws from 'several of our own signal sources in one call.' This makes the core purpose clear, though it doesn't explicitly distinguish from sibling tools like intent-signal-aggregator or company-lookup.

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 implies this tool is for consolidated lead qualification but provides no explicit guidance on when to use it versus alternatives. No when-to-use/when-not-to-use conditions or alternative tool names are given. The schema references company-hiring-radar but the description itself lacks comparative 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?

The description goes beyond the readOnly/openWorld annotations by disclosing that the operation is keyless, produces one row per company x jurisdiction, includes true case counts and source links, and carries a $0.03/call cost. It does not mention rate limits or error handling, but the annotations already cover safety, so this is sufficient.

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 sentences, front-loaded with the core purpose, and each additional sentence supplies actionable details (keyless access, output shape, cost). There is 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 tool has a moderately complex I/O profile, but the description covers the purpose, jurisdictions, output format, security model, and pricing. Since the schema already documents all four parameters and annotations cover safety, the description provides sufficient context for a caller to understand what will happen, including the one-row-per-company-jurisdiction behavior.

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?

All four parameters are fully described in the schema (100% coverage), so the description needs no additional parameter details. The description's 'one row per company x jurisdiction' mirrors the schema's explanation for the companies parameter, adding no new semantic value beyond what the schema already provides.

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 ('Screen') and a specific resource ('a company for litigation history') and names three concrete jurisdictions (US federal dockets, UK case law, Polish judgments), which clearly distinguishes it from sibling tools like sanctions-screening. It also notes output structure (one row per company x jurisdiction) and keyless access, leaving no ambiguity about what the tool does.

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?

While it doesn't name alternative tools explicitly, the description gives clear context that this tool is for litigation-history screening across specific jurisdictions, which implies it's not for sanctions checks or general company lookups. The inclusion of jurisdictions and court types makes the applicable use case obvious, but it lacks explicit when-not-to-use 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds valuable context: it is a public data scraper ('no API key, no login, no proxies'), and mentions cost ($0.02/call). No contradictions are present, and the description reinforces the safe, read-only nature.

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 concise (two sentences) with no wasted words. Every sentence provides essential information: purpose, source, time window, constraints, and cost. Highly 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?

Given the low complexity of this tool (3 parameters, no output schema, safe annotations), the description covers the most important aspects: what it does, where it gets data, cost, and limitations. It does not explain the return format, but with no output schema, the agent might need to infer structure. Still, for a simple search tool, this 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?

Schema coverage is 100%, so baseline is 3. The description does not add much beyond the schema: it mentions keyword and time window but doesn't elaborate on parameter formatting or behavior beyond what the schema already specifies. No significant added value for parameters.

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 finds newly-incorporated UK companies matching a keyword. It specifies the data source (Companies House), the time window (last ~60 days by default), and key constraints (no API key needed). This clearly distinguishes it from siblings like company-hiring-radar or company-lookup which have different scopes.

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: it is for finding recent UK companies via keyword, with no authentication needed. It does not explicitly state when not to use it or name alternatives, but the behavior is well-defined enough that the agent can infer appropriate use cases (e.g., not for non-UK companies or old registrations).

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 and destructiveHint=false. The description adds value beyond these by disclosing cost ($0.02/call), payment method (x402 USDC), data source (PatentsView API, USPTO-backed), and the need for an API key. No contradictions 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?

The description is three sentences, front-loaded with the core purpose. It includes relevant details (API key, output fields, cost) but the pricing and payment method could be considered extraneous for core functionality. Minimal waste, but not perfectly concise.

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 should clarify the return structure. It lists fields but does not specify the format (e.g., array of objects) or mention pagination, error handling, or rate limits. Adequate but not fully complete given the lack of an output schema.

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 schema already documents all parameters. The description adds marginal value by mentioning the Google Patents link in output, but does not add new meaning to the parameters beyond what the schema provides. 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 clearly states the tool watches keywords for newly granted US patents, lists specific fields returned (patent id, title, grant date, assignee, Google Patents link), and is distinct from all sibling tools (none are patent-specific). This meets the 'specific verb+resource, distinguishes from siblings' criterion.

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 implies usage for patent monitoring by keywords, but it does not explicitly state when to use this tool versus alternatives (e.g., other alert tools). There are no sibling patent tools, so the context is implicit, but no exclusions or when-not-to-use guidance is provided.

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:true, destructiveHint:false, and openWorldHint:false, so the safety profile is clear. The description adds behavioral context by indicating that the tool is free (overriding a potential assumption that pricing tools incur costs), and by specifying it lists pricing for paid tools—implying it discloses what remains proprietary. However, it does not describe what happens if no paid tools exist in the bundle or whether the response is a list or single object; these are minor gaps given the tool's simplicity.

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 a single sentence of 20 words, front-loading the most important information ('Free') and then immediately stating the action and output. Every word earns its place with no fluff or repetition. It is highly efficient while being complete.

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 no parameters, no output schema, and a simple purpose, the description covers the essential facts: what data it returns (prices, payTo, network), for which bundle (company-intel), and how it relates to the user's wallet readiness. A minor gap is that it does not explain whether the tool lists all paid tools once or can be filtered, but with zero params, the expectation is low. It is complete enough for an agent to correctly invoke this 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 tool has zero parameters and schema description coverage is 100% (empty schema). The description adds value beyond the schema by clarifying that the pricing is specifically for the 'company-intel' bundle, which is not stated in the structured input. It also mentions the details returned: price, payTo address, and network. This effectively compensates for the lack of parameters.

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 starts with 'Free' to quickly communicate the cost, then specifies the action 'list every paid tool in the 'company-intel' bundle with its price, payTo address and network.' This clearly describes what the tool does (lists pricing info) and what resource it operates on (the company-intel bundle). It also distinguishes itself from siblings by being the only tool dedicated to pricing; other tools like company-lookup or funding-alert serve different purposes.

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 advises the agent to 'Call this first if you don't have a wallet ready yet.' This gives clear when-to-use guidance, implying that the tool is useful for discovery before payment actions. It also contrasts with sibling tools that require a wallet or other setup, effectively guiding the agent's decision-making.

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, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral traits beyond annotations: 'Keyless, no login' (authentication model), 'live' lists (real-time nature), 'match score' (result granularity), and pricing ($0.01/call, x402 USDC). It does not contradict annotations and enriches the agent's understanding of side effects and constraints.

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, each earning its place: first sentence for core action, second for access and purpose, third for pricing. It is front-loaded with the primary verb and resource, uses no filler, and is immediately scannable. Perfectly sized for the tool's simplicity.

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 has no output schema, so the description should compensate by outlining the return format. It mentions 'flags potential hits with a match score' but does not specify the structure (e.g., list of matched names, scores, match details). For a screening tool, an agent would benefit from knowing what to expect in the response. Given the moderate complexity and well-covered parameters, the description is adequate but not fully complete.

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, with each parameter (names, minScore, maxConcurrency) already well-documented. The description adds high-level context ('live lists,' 'match score') but does not provide additional parameter-specific details beyond what the schema offers. The baseline of 3 is appropriate, as the description adds marginal value to parameter semantics.

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 action ('Screen names'), the resource ('live OFAC SDN and EU consolidated sanctions lists'), and the outcome ('flags potential hits with a match score'). It distinguishes itself from sibling tools like 'sanctions-update-alert' and 'litigation-check' by focusing on live screening for onboarding, making its purpose 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?

The description explicitly mentions a use case: 'before onboarding a client or counterparty,' which guides when to use the tool. However, it does not explicitly state when not to use it or point to alternatives (e.g., 'sanctions-update-alert' for updates). The context is clear enough for an agent to infer appropriate usage, but lacks direct exclusion cues.

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 annotations (readOnly=false, etc.), the description discloses statefulness ('The first run creates the baseline and says so'), the selective return of only changed entries, and pricing ('$0.05/call'). It also clarifies the 'saved watchlist' behavior, which adds meaningful context beyond what annotations convey.

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 and immediately delivers the core function, then adds essential pricing and state nuances. It is front-loaded with the verb and purpose, with zero 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 there is no output schema, the description explains the return semantics ('only the entries whose status changed') and the first-run baseline, which covers the essential workflow. However, it does not specify the output structure (e.g., fields like name, score, program) beyond what is implied by the input schema, leaving a minor gap for an 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?

Schema description coverage is 100%, so the input schema already provides rich descriptions for all four parameters. The tool description does not add parameter-level meaning beyond saying 'saved watchlist,' which ties to baseline_key. This meets the baseline for high schema coverage but does not go further.

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 ('Re-screens') and clearly defines the resource ('a saved watchlist against sanctions lists') and the exact scope ('returns only the entries whose status changed since the previous check'). It also distinguishes itself from the sibling tool 'sanctions-screening' by focusing on change detection rather than initial screening.

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 clearly implies this is for ongoing monitoring, noting the first run creates a baseline and that only changed entries are returned. However, it does not explicitly name alternatives or say 'use this instead of sanctions-screening when you need change alerts,' so it lacks explicit when-not 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?

The description adds valuable behavioral details beyond the sparse annotations: 'Free structural checks, free not-found/ambiguous/error outcomes; billed only for a complete, unambiguous company card' clarifies the billing/failure model. It also names the official source. However, it does not describe the output structure or side effects beyond cost.

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, front-loaded with the core purpose and followed by billing/free-outcome details. No fluff.

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 input types, source, and billing but omits the structure of the returned KYB card. Since there is no output schema, the agent is left without knowing what fields to expect, which is a notable gap for a 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?

Schema description coverage is 100%, so the baseline is 3. The description mentions '統一編號 or legal name' but the schema already has detailed parameter descriptions. No additional semantics are provided beyond what the schema states.

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 ('Resolve'), clearly identifies the resource ('Taiwan companies'), and specifies the output ('evidence-backed KYB card'). It distinguishes itself from generic sibling tools like company-lookup by being Taiwan-specific and focused on KYB.

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 does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or conditions. The Taiwan/KYB scope is implied by the name but not contrasted with generic company lookup tools.

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, openWorldHint, and destructiveHint, so the safety profile is covered. The description adds valuable context: it extracts only from the company's own site, flags third-party/placeholder addresses instead of presenting them as valid, and discloses a per-call cost with payment method. This goes beyond minimal transparency.

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: the first front-loads the core functionality and unique value proposition, while the second provides cost and payment details. Every word contributes—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?

With no output schema, the description could have been expected to outline the return format more explicitly (e.g., fields returned, honesty flag). However, it states the extraction types (email, phone, social links) and implies the honesty flag. Combined with rich schema documentation and annotations, it is largely complete for a read-only extraction tool, but a brief mention of output structure would elevate it.

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 (domains, checkPages, maxConcurrency, maxPagesPerDomain) are already well-documented in the schema. The description does not add additional parameter-specific meaning beyond the schema, but it does establish the overall purpose (extracting contacts) that gives context to the parameters. This meets 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 clearly states the tool extracts public contacts (email, phone, social links) a company posted on its own site, with a specific verb ('Extract') and resource. It distinguishes itself from siblings like email-verifier by emphasizing the source (company's own site) and the honesty check for third-party/placeholder addresses.

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 implies when to use this tool (to obtain contacts from a company's official website) and highlights a unique honesty-check feature that differentiates it from simple scrapers. However, it does not explicitly mention alternatives or state when not to use it, leaving some room for ambiguity.

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