SupplyMaven MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It comprehensively enumerates all 31 tracked commodities by category, specifies return values (current price and 24-hour change percentage), and discloses tier-based access constraints. Minor gap on rate limits or caching 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?

Four well-structured sentences: purpose statement, comprehensive commodity enumeration (necessary given zero parameters), return value specification, and tier constraints. The commodity list is lengthy but essential for scope clarity. No redundant information.

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

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

No output schema exists, and the description compensates by specifying return values (current price and 24-hour change). The exhaustive list of 31 commodities provides necessary coverage context. Minor gap: does not specify which 5 commodities are included in the free tier.

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?

Input schema contains 0 parameters (empty object), establishing the baseline score of 4. The description appropriately omits parameter discussion since none exist, focusing instead on the tool's data coverage and return behavior.

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?

Description clearly states the tool monitors real-time commodity prices and volatility for supply chain cost management. It distinguishes from sibling get_commodity_volatility_alerts by emphasizing current price tracking and 24-hour change metrics rather than threshold-based alerts.

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?

Provides explicit usage context ('for supply chain cost management') and critical tier limitations (free tier: 5 commodities, paid: 31). Lacks explicit comparison to get_commodity_volatility_alerts for when to choose one over the other, though the return value description implies the distinction.

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 carries the full disclosure burden and successfully specifies the statistical methodology ('Granger-causal relationship (p≤0.01)'), data freshness ('live'), return values ('ACTIVE, WATCH, or CLEAR'), and access restrictions ('Paid tier only').

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 each earn their place: sentence 1 defines the action and data source, sentence 2 details statistical methodology, sentence 3 specifies return statuses, and sentence 4 states access restrictions. No redundant or filler 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?

Despite lacking an output schema, the description compensates by enumerating return statuses (ACTIVE/WATCH/CLEAR) and explaining signal properties (lag time, directional accuracy), providing sufficient context for a parameter-less data retrieval 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 input schema contains zero parameters with 100% description coverage trivially satisfied. The description appropriately requires no parameter explanation, meeting the baseline expectation for zero-argument tools.

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 action ('Get statistically validated leading indicator signals') and domain resources ('live GDI and SMI data'), distinguishing it from siblings like get_commodity_prices or get_port_congestion which provide raw data rather than statistically processed 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 context through 'leading indicator signals' (suggesting predictive analysis) and 'Paid tier only' (access constraint), but provides no explicit when-to-use guidance or comparison against alternatives like get_disruption_alerts or get_supply_chain_risk.

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 empty annotations, description carries full disclosure burden. Compensates effectively by detailing returned data (FAA ground delays, ground stops, arrival/departure delays with minutes, closure status, disruption score, traffic collapse detection) and noting 'real-time' nature. Lacks rate limits or caching details, but covers essential behavioral traits.

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 well-structured sentences with zero waste: sentence 1 defines action/resource, sentence 2 specifies return data and coverage, sentence 3 establishes use case. Every sentence earns its place; specific airport list adds value without verbosity.

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 description appropriately compensates by comprehensively listing return data types (delays, scores, closures, traffic collapse). Covers domain scope (major US/international cargo hubs) and target users, making it complete for a zero-parameter read 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?

Zero input parameters present, triggering baseline score of 4. Description implicitly documents scope constraints by enumerating covered airports (Memphis, Louisville, etc.), effectively substituting for location parameters that might otherwise exist.

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?

Opens with specific verb+resource ('Get real-time air cargo disruption status') and explicitly distinguishes from siblings by specifying 'air cargo' and listing specific freight hub airports (Memphis, Louisville, Anchorage, etc.), clearly differentiating from port congestion, rail freight, and border delay tools in the sibling list.

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?

Provides clear contextual signals on when to use ('reroute time-sensitive shipments around airport disruptions') and identifies user personas (freight forwarders, express carriers), establishing clear domain boundaries. Does not explicitly name sibling alternatives to avoid, but strong transport-mode specificity makes intent clear.

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?

Strong disclosure given zero annotations: specifies update frequency ('every 30 minutes'), data source ('US Customs and Border Protection'), and return payload structure ('delay in minutes', 'lanes open', 'port status'). Minor gap: doesn't explicitly declare read-only nature or error behaviors, but 'Get' prefix and source attribution provide implicit safety signals.

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?

Five sentences covering purpose, return values, data provenance, coverage scope, and use case. Each sentence earns its place. Slightly verbose with the specific city list (could be implied by 'major commercial crossings'), but information density is high and structure follows logical priority order.

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?

Comprehensive for a zero-parameter data-retrieval tool. Compensates for missing output schema by explicitly documenting return values (delays, lanes, status), data freshness (30min), and geographic coverage. No additional structured metadata (annotations) to conflict with or supplement.

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?

Input schema has zero parameters. Per rubric baseline, 0 params = baseline 4. Description appropriately requires no parameter clarification since the tool appears to return a comprehensive dataset of all major crossings without filtering.

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?

Excellent specificity: verb 'Get' + resource 'border crossing wait times', geographic scope 'US-Mexico and US-Canada', and vehicle type 'commercial'. Clearly distinguishes from siblings like get_port_congestion_trends (seaports), get_rail_freight_status (rail), and get_air_cargo_disruptions (air) by explicitly listing land border crossings (Laredo, El Paso, etc.).

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?

Provides clear usage context: 'Used by logistics companies... to route cross-border shipments through the fastest crossing points.' This signals when to invoke the tool (for land-based cross-border freight routing). Stops short of explicitly naming sibling alternatives to avoid, but the context is sufficient for selection among the logistics toolset.

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, description carries full burden and successfully discloses return values (vessel count, speed, congestion score) and real-time nature. Explains business impact (route rerouting detection). Missing explicit read-only declaration or rate limit warnings, though 'Monitor' implies safe 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 sentences with zero waste: (1) Core function + scope, (2) Return value specification, (3) Business value/context. Front-loaded with actionable verb and strategically organized.

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 description appropriately documents return structure (congestion scores, vessel counts). No annotations exist, so description explains behavioral value (real-time rerouting detection). Complete for a zero-parameter monitoring 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?

Input schema has zero parameters. Description correctly omits parameter discussion (none exist). Baseline 4 achieved as no compensation 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?

Specific verb 'Monitor' paired with exact resource 'vessel traffic and congestion at critical maritime chokepoints'. Lists concrete examples (Suez, Panama, Malacca) that distinguish it from sibling port/air/rail tools. The scope is precisely defined.

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?

Provides clear contextual trigger: 'When chokepoints congest or close...'. Implicitly distinguishes from siblings like get_port_congestion_trends and get_air_cargo_disruptions by emphasizing 'maritime chokepoints' vs ports or air. Lacks explicit 'when not to use' or named alternative references.

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 empty annotations, the description carries the full disclosure burden and succeeds in documenting return structure ('current price, 24-hour change percentage, trend direction, and risk assessment') and detection logic. However, it omits critical behavioral context such as data refresh frequency, rate limits, coverage limitations (which commodities), or whether alerts are realtime vs cached.

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 well-structured sentences with zero redundancy: (1) purpose, (2) detection criteria, (3) return values, (4) use cases/question framing. Every sentence earns its place with specific technical or business context. Front-loaded with the core value proposition.

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 lacking an output schema, the description enumerates specific return fields (price, change %, trend, risk). For a zero-parameter alert tool, it adequately covers the algorithmic logic (24-hour ranges, extreme levels) and stakeholder use cases. Minor gap: does not specify commodity coverage universe or data latency.

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?

Input schema contains zero parameters, triggering the baseline score of 4. The description correctly omits parameter discussion since none exist, avoiding confusion. It would be impossible to add param semantics here, so this is appropriately handled.

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

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

The description opens with a specific action ('Get alerts') and resource ('commodities experiencing abnormal price volatility'), defines the specific criteria triggering alerts ('24-hour price change exceeds normal ranges,' 'extreme levels'), and distinguishes this from siblings like commodity_price_monitor or supply_chain_disruption_alerts by focusing specifically on volatility anomalies rather than general pricing or disruptions.

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?

Provides clear use cases ('Used by procurement teams to time purchases...') and frames the business question it answers ('which commodities are behaving unusually right now?'). However, it lacks explicit guidance on when to choose this over related tools like commodity_price_monitor or get_predictive_signals, or prerequisites like data coverage requirements.

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 provided, so description carries full burden. It comprehensively discloses what data is returned (specific Federal Reserve metrics, PPI categories, GSCPI, EIA forecasts), which is essential behavioral context for a data retrieval tool. Does not mention rate limits or data freshness, but covers the primary behavioral trait (return payload) well.

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 well-structured sentences: purpose statement, specific data enumeration, and target audience/use case. Every sentence earns its place with no repetition of structured metadata. Front-loaded with the core action.

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, but description compensates by enumerating specific datasets returned (Fed data, PPI, GSCPI, EIA forecasts). Appropriate for a zero-parameter retrieval tool, though could enhance with data frequency or update cadence details.

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?

Input schema has zero parameters. Per rubric, 0 params = baseline 4. Description correctly does not invent parameters that don't exist in 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?

Description uses specific verb 'Get' with clear resource 'economic indicators' and scopes it to supply chain costs/conditions. It distinguishes from operational siblings (e.g., port_congestion_monitor, get_border_delays) by specifying macroeconomic data sources (Federal Reserve, NY Fed, EIA) rather than tactical logistics data.

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?

Provides implied usage context ('Used by supply chain strategists... who need the macro backdrop'), indicating when to use it. However, lacks explicit when-not guidance or named alternatives despite overlapping siblings like get_freight_transportation_index, manufacturing_output_indicator, and get_energy_forecast.

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 empty annotations, the description carries full burden. It comprehensively lists return data types (WTI, Brent, Henry Hub, etc.) explaining what the tool yields, but omits operational traits like data freshness, caching behavior, rate limits, or explicit safety declarations beyond the 'Get' verb.

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 efficiently structured: purpose/use case, detailed return specification, sibling differentiation, and target audience. No redundancy; every sentence adds value beyond structured fields.

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?

Excellent compensation for missing output schema by enumerating all returned data points (oil prices, storage levels, refinery rates, etc.). Lacks only operational metadata (update frequency, geographic granularity beyond 'US') to achieve completeness for a complex data retrieval 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?

Input schema has 0 parameters, establishing baseline 4 per rubric. The description appropriately requires no parameter clarification since the tool takes no inputs.

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 ('Get') and resources ('US energy market status') and explicitly distinguishes from siblings by contrasting the 'disaggregated view' with the 'single risk number' from GDI Energy pillar tools like risk_pillar_breakdown. It clearly scopes the tool to supply chain cost analysis.

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?

Identifies target users ('supply chain cost analysts, transportation managers') and use case ('supply chain cost analysis'). It differentiates from aggregated risk views but does not explicitly map when to use this versus get_energy_forecast or commodity_price_monitor, though the 'status' vs implied forecast distinction is clear from naming.

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 carries full disclosure burden. It successfully explains the dual nature of returned data (historical actuals vs forward projections), the isActual flag mechanism for distinguishing them, and the specific data categories included. Does not mention rate limits or update frequency.

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?

Five sentences, zero waste. Front-loaded with the specific data source (STEO). Each subsequent sentence adds distinct value: data categories, credibility claim, data structure flag (isActual), and use cases. Logical flow from what → content → credibility → structure → usage.

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?

Comprehensive for a parameter-less data retrieval tool. Explains return content sufficiently even without output schema. Only minor gap is absence of temporal context (update frequency, forecast horizons) which would be useful for a forecasting 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?

Input schema has 0 parameters and 100% description coverage. As per rules, 0 parameters warrants baseline score of 4. Description appropriately does not fabricate parameter details where none exist.

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?

Description explicitly identifies the specific resource (EIA Short-Term Energy Outlook) and action ('Get'). It clearly distinguishes from siblings like commodity_price_monitor (real-time prices) and get_energy_breakdown (composition analysis) by emphasizing 'forecasts' and the specific STEO data product.

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?

Provides specific use cases ('energy traders, logistics companies budgeting fuel costs, and macro analysts') that clearly signal when to use this tool. However, it lacks explicit contrast with siblings (e.g., when to use commodity_price_monitor instead) or prerequisites.

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 provided, so description carries full disclosure burden. Successfully identifies data source (Bureau of Transportation Statistics), enumerates specific return data fields (TSI freight/passenger, truck tonnage, rail volumes, etc.), and provides interpretation context (declining volumes as recession signal). Missing operational details like update frequency, caching, or rate limits.

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 well-structured sentences: definition with source, return value enumeration, analytical interpretation, and user personas. Every sentence earns its place with no redundancy or filler. Front-loaded with essential action and resource identification.

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?

Strong compensation for missing output schema by explicitly listing all returned indices (TSI components, tonnage, carloadings, intermodal, waterborne, inventory ratios). Appropriate scope for a zero-parameter data retrieval tool. Could benefit from mentioning data frequency or time range coverage.

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?

Input schema contains zero parameters. Per scoring rules, 0 params establishes baseline 4. Description appropriately reflects this by not mentioning parameters that don't exist.

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 specific action (Get) and resource (US freight transportation health index/TSI from Bureau of Transportation Statistics). Distinguishes from siblings like get_rail_freight_status by enumerating comprehensive multi-modal coverage (truck, rail, water, inventory ratios) rather than single-mode data, though explicit comparison to siblings is missing.

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?

Describes use cases via user personas (logistics companies, freight brokers, economic analysts) and contextual utility (leading indicator of economic slowdown), but lacks explicit guidance on when to choose this over similar economic indicators like get_economic_indicators or commodity-specific monitors.

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, description carries full disclosure burden and comprehensively details return structure: current score plus multi-day comparisons, direction, velocity, and pillar-level momentum. Explains analytical capabilities (identifies driving pillar, acceleration vs deceleration). Minor gap: no mention of data freshness or caching 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?

Four sentences with clear information hierarchy: action (get trend analysis), data structure (returns score/comparisons), analysis logic (identifies drivers), and use cases. Every sentence adds distinct value. Slightly dense but avoids redundancy relative to 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?

Extensive output description compensates for missing output schema, detailing scalar values (current score), temporal comparisons (7/14/30-day), and derived metrics (velocity, momentum). Adequate for a parameter-less tool, though auth requirements or rate limiting context would elevate to 5.

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 contains zero parameters (empty object), meeting baseline threshold per scoring rules. Description appropriately focuses on behavioral semantics rather than inventing parameter documentation where none exist.

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?

Description opens with specific verb 'Get trend analysis' and resource 'Global Disruption Index over time', clearly distinguishing from siblings like get_port_congestion_trends or supply_chain_disruption_alerts. The scope is precisely defined through the 7/14/30-day comparison framework and pillar-level momentum detail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly identifies two distinct use cases—'supply chain executives for weekly status briefings' and 'traders to time entry/exit decisions'—which implicitly distinguish this from real-time alert tools (e.g., supply_chain_disruption_alerts) and static assessment tools (e.g., risk_pillar_breakdown). Lacks explicit 'when not to use' 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?

With no annotations provided, the description carries the full disclosure burden and succeeds reasonably well. It reveals key behavioral traits: content is 'generated every hour from live data' (temporal/caching behavior), provides 'narrative analysis' versus structured data points (output format), and returns content suitable for specific downstream channels (integration 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 well-structured and front-loaded with the core action and scope in the first sentence. Each subsequent sentence adds distinct value: content details (sentence 2), behavioral constraints (sentence 3), target audience (sentence 4), and integration/use-case guidance (sentence 5). Minor verbosity in 'Designed for decision-makers...' slightly overlaps with the opening, preventing a perfect score.

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 absence of an output schema, the description adequately compensates by detailing what is returned ('structured briefs', 'narrative analysis of current conditions, key drivers, emerging risks'). It covers generation frequency, data synthesis methodology, and consumption channels. A slight gap remains regarding error conditions or rate limits, but overall sufficient for a zero-parameter retrieval 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 input schema contains zero parameters (empty object), which establishes a baseline of 4. The description appropriately makes no mention of parameters since none exist, and does not need to compensate for undocumented inputs. No deduction warranted given the schema coverage is 100% and no user input is required.

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 ('Get AI-generated intelligence briefs') and clearly identifies the resource scope (supply chain dimensions: energy, materials, transportation, macro, manufacturing). It effectively distinguishes from siblings by emphasizing these are 'synthesized analytical summaries' versus raw data, and explicitly contrasts with operational data tools by targeting 'decision-makers who need a quick read'.

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?

Provides clear context on when to use versus alternatives by stating 'These are not raw data' and characterizing the output as suitable for 'executive dashboards, email digests, or Slack channels.' This implies usage for high-level synthesis rather than detailed operational analysis. However, it does not explicitly name specific sibling tools (e.g., 'use get_energy_breakdown for raw data 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 provided, the description carries full burden and successfully discloses return structure (current SMI score, regional breakdown, 7-day anomaly history), methodology (weather normalization), and interpretation logic (drops=shutdowns, surges=ramp-ups). Lacks operational details like rate limits or caching behavior, but adequately covers the read-only data retrieval 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?

Five sentences with zero waste: purpose (detect anomalies), mechanism (8 grid regions), output (SMI score + breakdown), methodology (weather normalization), and use cases (traders/procurement). Information density is high and logically sequenced from what to how to why.

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 absence of an output schema, the description effectively compensates by detailing the return payload structure (SMI score, regional breakdown, 7-day ranked anomalies) and domain context necessary to interpret results. Sufficient for the tool's complexity, though explicit data format (JSON structure) would elevate this further.

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 contains zero parameters. Per the baseline rules for parameter-less tools, this receives a default score of 4. No parameters require semantic clarification.

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 explicitly states the tool detects 'unusual electricity demand patterns' to signal manufacturing disruptions, using specific infrastructure (8 US power grid regions) and a named metric (SMI). It clearly distinguishes from siblings like manufacturing_output_indicator by emphasizing electricity-based detection 'before they appear in official reports.'

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?

Provides clear use-case context for 'commodity traders' and 'procurement teams' and positions the tool as an early warning system (before official reports). However, it does not explicitly name sibling alternatives or state exclusion criteria (e.g., 'do not use for real-time factory floor monitoring').

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 empty annotations, description carries full burden. Discloses analytical behavior: historical baseline comparison, acceleration identification, critical threshold flagging, and trajectory analysis. Does not mention rate limits or data freshness, but covers core analytical transparency well.

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 sentences, each earning its place: 1) Purpose with immediate differentiation, 2) Behavioral output details, 3) Concrete use cases. No redundancy. Front-loaded value.

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?

Adequately compensates for lack of output schema by describing return value semantics: congestion changes, acceleration flags, and threshold warnings. For a 0-parameter analytical tool, description covers conceptual scope completely.

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?

Zero parameters present (empty schema). Per rubric, 0 params establishes baseline of 4. Description appropriately focuses on tool behavior rather than non-existent 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?

Excellent specific verb ('Get') + resource ('port congestion trend analysis'). Critically distinguishes from sibling 'port_congestion_monitor' via explicit contrast 'not just current congestion, but direction and trajectory' and temporal focus ('historical baselines', 'accelerating').

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?

Strong implicit differentiation via 'not just current congestion' framing. Provides concrete use cases: 'logistics planners to reroute shipments before congestion peaks' and 'importers to anticipate lead time extensions'. Lacks explicit naming of sibling alternative.

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

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

Rich disclosure beyond empty annotations: specifies statistical methodology (Granger-causal, directional accuracy >=55%), return value taxonomy (ACTIVE/WATCH/CLEAR with definitions), data scope (58 signals across 3 tiers), and organizational structure (GDI pillars, SMI regions, cross-index spreads).

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?

Dense but well-structured: progresses logically from methodology → prediction targets → timeframe → output format → organization → use cases. Every sentence conveys unique technical information (statistical thresholds, signal counts, status definitions) 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?

Comprehensive despite missing output schema: documents return values (ACTIVE/WATCH/CLEAR), explains the 3-tier signal organization, and specifies the 58-signal scope. Adequately compensates for lack of structured output definition.

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 contains 0 parameters, triggering baseline score of 4 per evaluation rules. Description appropriately contains no parameter exposition since none exist.

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?

Excellent specificity: defines the resource as 'Statistically validated leading indicator signals' using 'Granger-causal relationships' (p<=0.01) and distinguishes from siblings by emphasizing predictive scope (1 week to 6 months ahead) vs real-time monitoring tools like commodity_price_monitor or port_congestion_monitor.

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?

Clear positive guidance identifying three specific personas/use cases (commodity traders for forward-looking positioning, procurement teams for buy/defer timing, hedge funds for alternative data). Lacks explicit 'when not to use' warnings or direct sibling comparisons, but use case descriptions imply appropriate contexts.

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?

Strong disclosure despite no annotations: lists data sources (Surface Transportation Board, Association of American Railroads, USDA), explains business impact/interpretation of the data, and details what metrics are returned. Lacks operational details like update frequency or caching, but compensates well for missing output schema by enumerating return values.

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 dense sentences with zero waste: first enumerates capabilities/metrics, second establishes data provenance/credibility, third provides business context. Every sentence earns its place; appropriately front-loaded with the core function.

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?

Comprehensive for a zero-parameter retrieval tool: compensates for missing output schema by listing all metrics, cites authoritative sources for credibility, and explains business significance. Minor gap: does not describe response structure or format.

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?

Zero parameters present, which establishes a baseline of 4 per scoring rules. No parameter-related content needed or expected in 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?

Excellent clarity: 'Get US freight rail performance metrics' provides specific verb and resource, and enumerates exact metrics (train speed, dwell time, carloadings, etc.). Unambiguously distinguishes from siblings like get_air_cargo_disruptions and get_port_congestion_trends through specific rail-focused vocabulary.

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?

Provides clear contextual guidance: 'When rail slows down, inland supply chains back up within days' establishes when to use this tool (for early warning of freight bottlenecks). Includes value proposition but does not explicitly name sibling alternatives or exclusion criteria.

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 empty annotations, the description carries the full burden and succeeds in disclosing return structure ('trigger context, predictor value, direction, and a narrative paragraph') and content type (mechanism explanations, economic logic). It does not cover operational traits like caching or rate limits, but the core behavioral contract is clear.

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 well-structured sentences with zero waste: sentence 1 defines the operation, sentence 2 details the content/mechanism, and sentence 3 identifies the audience and return format. Every clause earns its place.

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

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

Given the absence of an output schema, the description explicitly documents the return values (trigger context, predictor value, direction, narrative paragraph). For a zero-parameter read tool, this level of detail on the output format and target audience provides complete 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?

The tool has zero parameters (schema coverage 100%), establishing a baseline of 4. No parameter semantics are needed in the description, and none are provided, which is appropriate for a parameter-free tool that presumably returns all active signal narratives.

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 ('Get plain-language explanations') and clearly identifies the resource ('active predictive signals'). It distinguishes from quantitative siblings by emphasizing 'plain-language' and 'without reading F-statistics', positioning it against statistical tools like get_predictive_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?

It provides clear usage context ('Designed for non-quantitative users', 'suitable for reports and briefings') and implicitly contrasts with statistical approaches via the F-statistics reference. However, it does not explicitly name the quantitative alternative tool (e.g., get_predictive_signals) for users who need raw statistics.

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 carries full burden. It comprehensively lists return contents (GDI scores, disruptions, manufacturing output, etc.) but omits operational characteristics: no disclosure on data freshness (when 'this week' updates), computational cost, rate limits, or response size/pagination 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?

Four sentences with zero waste: (1) overview, (2) detailed contents list, (3) design intent/question answered, (4) target audience. Logical flow from what it is → what's included → why use it → who uses it. Every sentence earns its place.

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

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

Given no output schema exists, the description compensates well by enumerating specific data points returned (GDI score breakdown, 8 regions, commodity movements, etc.). Only minor gap is lack of format disclosure (structured vs narrative) or size expectations for this comprehensive aggregation.

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?

Input schema has zero parameters. Per scoring rules, 0 params establishes a baseline of 4. The description appropriately requires no parameter clarification, though it implicitly confirms no filtering is available by stating it returns 'comprehensive' data combining all sources.

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?

Excellent specificity: states it 'combines all SupplyMaven data sources into an executive-level brief' and answers 'what happened this week in supply chains?' This clearly distinguishes it from specific siblings like commodity_price_monitor or get_air_cargo_disruptions which cover individual domains, positioning this as the aggregated weekly rollup.

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?

Strong context provided: identifies target users (executives, procurement leaders) and specific scenarios (weekly risk reviews, stakeholder briefings). The phrase 'in a single call' implies efficiency for broad overviews. However, lacks explicit 'when not to use' guidance contrasting with specific sibling tools for drill-down analysis.

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 carries the full disclosure burden and succeeds well: it explains persistence behavior ('persist as long as the policy is in effect'), side effects ('modify GDI risk scores'), and return structure ('affected GDI pillar, score modifier, effective date'). Lacks only rate limit or pagination details.

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 well-structured sentences with zero waste: (1) core function and examples, (2) key behavioral differentiator from siblings, (3) return data structure, (4) target user personas. Information density is high with 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?

Despite having no output schema defined, the description compensates by detailing the returned policy objects (pillars, modifiers, dates). Adequately explains the domain for a zero-parameter retrieval tool, though time range limits or result volume caps would enhance completeness.

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 zero parameters, triggering the baseline score of 4 per evaluation rules. No parameters require semantic clarification.

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+resource ('Get active trade policy actions') and clearly enumerates covered policy types (tariffs, sanctions, export controls, etc.). It explicitly distinguishes itself from transient 'news alerts' mentioned in the sibling context, establishing clear scope boundaries.

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?

Provides strong contextual guidance by contrasting persistent policy data with 'news alerts that expire after 72 hours' and identifies specific user personas (procurement teams, compliance officers). Could be improved by explicitly naming the sibling alert tool to use for transient events rather than implying it.

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 carries the full burden and succeeds in disclosing key behavioral traits: content is generated every Monday, every number traces to live data sources, and outputs are platform-specific formatted strings. It lacks operational details like caching duration or error states if called outside generation windows.

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 dense sentences efficiently cover: (1) the core resource and source, (2) specific output formats with length constraints, (3) data verification methodology, and (4) intended automation context and included metadata. Every clause adds value 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 absence of an output schema, the description compensates effectively by detailing the three platform-specific content pieces and additional metadata fields (GDI, SMI, commodities). It would benefit from specifying the response data structure (e.g., JSON object keys) or format, but adequately describes the payload contents.

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 contains zero parameters, establishing a baseline score of 4. The description correctly implies no user input is needed by characterizing the package as an automated weekly generation, making the parameter-less nature explicit through context.

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 explicitly states the tool retrieves a 'pre-written, data-verified marketing bundle' and distinguishes it from data-focused siblings by specifying platform-ready outputs (Substack, LinkedIn, Twitter). It identifies the resource clearly as the weekly 'Signal of the Week' package generated from SupplyMaven data.

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 for when to use the tool—'automated content distribution via Claude Desktop + platform MCP servers'—indicating it's for marketing automation rather than raw data analysis. However, it does not explicitly contrast with similar narrative or brief tools like get_signal_narratives or name specific alternatives.

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 carries full behavioral disclosure burden and excels: it discloses the INVERTED scale interpretation (lower = stronger), specific numeric thresholds (0-35 STRONG, 66+ WEAK), the weather-normalized electricity demand methodology, 8 specific grid regions covered, and the 24-hour lead time over official reports. Critical behavioral traits for correct interpretation are fully documented.

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?

Five dense sentences with zero waste. Logical flow: value proposition (sentence 1), methodology (sentence 2), return values (sentence 3), interpretation guide with inverted scale thresholds (sentence 4), and target audience (sentence 5). Every sentence provides essential information not available in structured fields.

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 lacking an output schema, the description fully compensates by detailing what is returned ('regional and national manufacturing activity scores, trend direction, and comparison to...INDPRO'). Given the complexity of the proprietary SMI index and the critical need to understand the inverted scale, the description provides complete contextual information necessary for correct invocation and interpretation.

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?

Input schema contains zero parameters. Per scoring rules, 0 parameters warrants a baseline score of 4. The description correctly implies no filtering is needed (national/regional coverage is automatic) and does not invent non-existent 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?

Description explicitly states the tool 'Detect[s] US manufacturing output changes' using the 'Supply Manufacturing Index (SMI)' up to 24 hours before official reports. It clearly distinguishes itself from siblings like get_economic_indicators by positioning itself as a 'leading manufacturing indicator' versus official Federal Reserve Industrial Production (INDPRO) data, and specifies the unique electricity-demand methodology across 8 named grid regions.

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?

Provides clear usage context by identifying target users ('commodity traders, economic analysts, and hedge funds') and contrasting with 'official government reports' to signal when to use this leading indicator versus lagging official data. Lacks explicit 'when not to use' or direct sibling comparisons (e.g., vs get_manufacturing_anomalies), but the positioning as a predictive electricity-based indicator provides sufficient implicit 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?

No annotations provided, so description carries full burden. Compensates well by detailing return values (vessel counts at berth/anchor, congestion score vs baseline, port status) and scope limitations (26 specific ports). Missing operational details like rate limits or cache behavior, but 'real-time' implies freshness expectations.

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 well-structured sentences covering: (1) core function, (2) return data specifics, (3) geographic coverage, and (4) use case context. No redundancy or filler; front-loaded with critical 'real-time' distinguisher.

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 zero parameters and lack of output schema, description provides complete compensation by exhaustively listing return metrics and port coverage. For a simple read-only monitoring tool, this level of detail is sufficient to invoke correctly without additional structured metadata.

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?

Input schema has zero parameters, which per scoring rules establishes a baseline of 4. Description appropriately focuses on behavior and outputs rather than inventing parameter documentation where none exist.

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?

Description uses specific verb 'Monitor' with clear resource 'real-time port congestion and vessel traffic'. Explicitly distinguishes from sibling 'get_port_congestion_trends' by emphasizing 'real-time' versus historical trends, and enumerates the 26 specific ports covered.

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?

Provides clear usage context identifying target users (freight forwarders, logistics teams) and use cases (monitor delays, plan routing, anticipate lead time changes). Lacks explicit 'when not to use' guidance or naming of alternative tools like get_port_congestion_trends.

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 carries full burden and successfully discloses return structure: 'individual scores for each GDI pillar,' 'trend direction,' and 'specific data points driving the current reading.' Missing only operational details like caching, rate limits, or data freshness semantics.

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 well-structured sentences where each earns its place: (1) purpose, (2) pillar enumeration with examples, (3) return value details, (4) target audience/use case. Front-loaded action verb. Slightly dense but information-rich 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 zero parameters, no annotations, and no output schema, the description adequately compensates by detailing the four pillar categories and specific metrics returned (scores, trends, drivers). Addresses the complexity of 31 commodity prices and multiple economic indicators without requiring external schema reference.

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?

Input schema contains zero parameters. Description appropriately does not invent parameter documentation. Baseline score of 4 applies as there are no parameters requiring semantic clarification beyond the empty schema.

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

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

The description clearly states the tool 'Get[s] detailed breakdown of supply chain disruption risk by category' with specific scope (GDI pillars: Transportation, Energy, Materials, Macro). It distinguishes from siblings like get_energy_breakdown or commodity_price_monitor by positioning this as the comprehensive multi-pillar aggregation versus single-category 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?

Provides implied usage context ('Essential for supply chain managers who need to diagnose which risk category is elevated'), indicating diagnostic use cases. However, lacks explicit guidance on when to use specific sibling tools (e.g., get_energy_breakdown) versus this aggregate view, and no '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?

With no annotations provided, the description carries full behavioral disclosure burden effectively. It specifies the data source ('global news intelligence and event detection'), return structure ('severity level, affected supply chain stage, and risk score'), and tier-based behavioral differences. It does not mention rate limits or authentication requirements, preventing a perfect score.

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 well-structured sentences with zero waste: sentence 1 states purpose, sentence 2 lists disruption categories, sentence 3 details return fields, and sentence 4 explains tier constraints. Information is front-loaded with the primary action and progressively details specifics.

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 lack of output schema, the description appropriately compensates by detailing return values ('Each alert includes severity level, affected supply chain stage...'). It adequately covers the complex supply chain domain and differentiates from 20+ siblings, though it could enhance completeness by mentioning pagination or rate limiting behaviors.

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 contains zero parameters, which per guidelines establishes a baseline of 4. The description correctly implies no filtering parameters are available by describing the global nature of the alerts ('Free tier returns critical-severity alerts only'), confirming the parameter-less design is intentional.

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 ('Get') and resources ('supply chain disruption alerts') and explicitly distinguishes from specialized siblings by listing comprehensive coverage areas including port closures, trade policy changes, natural disasters, labor strikes, sanctions, and weather disruptions, contrasting with narrower tools like get_air_cargo_disruptions or get_border_delays.

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?

Provides clear usage constraints by specifying the free tier limitation ('returns critical-severity alerts only') versus paid tier capabilities. While it doesn't explicitly state when to choose this over specialized siblings (e.g., 'use this for multi-category monitoring, use get_port_congestion_trends for port-specific data'), the enumerated categories implicitly guide selection.

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 carries the full disclosure burden and succeeds in explaining the return value semantics (GDI 0-100 scale, directionality where higher = greater risk), methodology (200+ live variables), and scope (transportation, energy, materials, macroeconomic). It omits operational details like rate limits or data latency.

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 three-sentence structure is front-loaded with the core action ('Assess current global supply chain disruption risk'), followed by methodology specifics and user context. Every sentence delivers distinct value without redundancy or 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 absence of an output schema, the description adequately compensates by detailing the return format (composite score, range, pillars). It sufficiently positions the tool within the extensive sibling toolset as the high-level aggregation entry point, though mentioning data refresh frequency would strengthen completeness.

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 contains zero parameters (empty properties object). Per evaluation guidelines, this establishes a baseline score of 4, as there are no parameter semantics to clarify beyond the schema itself.

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 explicitly states the tool 'Assess[es] current global supply chain disruption risk' and distinguishes it from siblings by specifying it returns the 'Global Disruption Index (GDI)'—a composite 0-100 score across four pillars, contrasting with the server's granular monitoring tools (e.g., get_port_congestion_trends, get_energy_breakdown).

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 identifies user personas (procurement teams, logistics planners) and use cases (real-time visibility, risk monitoring), providing implied context. However, it lacks explicit guidance on when to use this composite index versus siblings like get_gdi_trend_analysis or risk_pillar_breakdown.

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