Trustbase Lab · rCB & Chemical Recycling Data

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

Annotations already declare read-only and idempotent behavior, and the description adds useful context: it is pure arithmetic, performs no market data access, and is not a price recommendation. This is consistent with the annotations and clarifies side-effect expectations 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?

The description is well structured with Purpose, Guidelines, Limits, and an Example. Every section adds useful information and the essential behavior is front-loaded before the example.

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 arithmetic tool with a complete input schema, output schema, and clear annotations, the description covers purpose, inputs, workflow, limits, and gives a concrete example. An agent has enough context to invoke it correctly and confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning by identifying which price is the native price, which is the rCB price, and how the replacement ratio and annual volume fit into the calculation. The example with 'N330 12000 RMB/t vs rCB 8000 RMB/t, ratio 0.3, annual 5000t' clarifies units and usage beyond the schema.

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

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

The description clearly states a specific verb and resource: it computes per-ton saving and optional annualized value from native and rCB prices plus replacement ratio. It also distinguishes itself from data-fetching siblings by emphasizing 'pure arithmetic - no market data access'.

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

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

The description gives concrete guidance: supply your own quotes, or fetch market anchors via get_rcb_index first. It also states what the tool is not for ('not a price recommendation'), though it could more explicitly name sibling comparison/boundary tools as 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?

The description discloses behavioral details beyond the annotations, including the classification into pass / light deviation / significant deviation and the dependency on measured values. It also clarifies that thresholds follow the site's own anchored-benchmark methodology, not customer specifications, which is important for correct interpretation of results.

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 with labeled Purpose, Guidelines, Limits, and Example segments, making it highly scannable and front-loaded. Each sentence contributes meaningful operational guidance, and the example clarifies usage without unnecessary 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?

The description fully covers the tool's purpose, input requirements, methodology, limitations, and a usage example. Combined with complete schema descriptions and an output schema, there is sufficient information for an agent to select and correctly invoke the 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, but the description adds semantic value by giving concrete examples for rcb_params such as iodine_absorption, OAN, and ash. It also grounds target_grade with the N550 example, providing context beyond the schema's short parameter descriptions.

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's purpose: compare measured rCB sample parameters against a target native carbon black grade spec range and classify each parameter as pass, light deviation, or significant deviation. It is clearly differentiated from sibling get_* tools that only retrieve grade or spec data without performing a benchmark comparison.

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 usage context: ideal for grading incoming rCB lots against a native anchor before substitution decisions, and it specifies required inputs including target_grade and rcb_params. It also gives limits such as needing measured values and following the site's anchored-benchmark methodology rather than customer specs, but it does not explicitly name alternative sibling tools for cases where different input or methodology is needed.

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

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

The description adds useful caveats (claims are not independently confirmed, full press-release text is not served, coverage depends on public announcements), but it says 'absence is not evidence of inactivity,' which contradicts the annotation openWorldHint=false. That annotation indicates a closed-world assumption where absence should be meaningful, while the description asserts the open-world interpretation; per the rubric this contradiction forces a 1.

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 tightly organized into Purpose, Guidelines, Limits, and Examples, with every clause contributing information. It front-loads the core behavior and immediately supplies the date format and two minimal calling examples.

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 read-only tool with an output schema and only optional parameters, the description covers purpose, usage, limits, and examples, which is nearly complete. The open-world contradiction prevents a 5 because it leaves the agent with conflicting instructions about whether missing announcements are meaningful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by specifying the date format (YYYY-MM-DD), demonstrating region values ('欧洲'), and showing how the limit parameter is used in an example, which goes beyond the schema's brief field descriptions.

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 names a specific verb-resource pair: 'return a chronological timeline of company announcements across the rCB industry' and enumerates what the announcements cover (results, capacity, certification, delivery approvals). It also clarifies that items are 'announcement-derived items only,' which helps distinguish it from article or policy query 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?

The 'Guidelines' section tells the agent to filter by region and date range and explicitly names sibling tools with conditions: use with query_companies when tracking capacity build-out and with query_policies when a regulatory date drives an announcement. It lacks an explicit when-not-to-use/fallback statement, 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 readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds valuable behavioral constraints beyond annotations: it returns metadata and summary only, never the full body, and handles exactly one record per call. This is useful context that the annotations do not 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 labeled structure (Purpose, Guidelines, Limits, Ex) is clear, front-loaded, and free of fluff. Minor typographical issues like 'titlekeyword', 'achronological', and 'URL,confidence' slightly reduce polish but do not harm comprehension.

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 read-only, single-record lookup with a rich schema and output schema, this description is complete: it covers what the tool returns, how to find the id, the one-of parameter behavior, the body limitation, and a pairing suggestion. There is no additional information an agent needs to call it 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%, so the baseline is 3, but the description adds meaning: it explains that id comes from query_articles with the rcb-art-xxx format, shows concrete examples for both id and name, and clarifies the one-record limit. Language and response_format are already well documented 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?

The description names a specific verb and resource (fetch one article record) and enumerates the exact fields returned, making its purpose unmistakable. It also differentiates from sibling tools by noting it takes an id from query_articles and returns one record, so an agent can distinguish it from other get_* and query_* 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?

The description gives explicit routing guidance: run query_articles first when the id is unknown, use a title keyword as an alternative, and pair with get_announcements_timeline for chronological views. It also states an exclusion—full article body is not served—so an agent knows not to use this tool for that purpose.

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 read-only, idempotent, and non-destructive, and the description adds meaningful behavioral context beyond them: no batch mode, gated fields may be withheld for low-confidence records, and name lookup is a first-match service rather than a disambiguation service. This accurately discloses important limitations.

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 organized into Purpose, Guidelines, Limits, and Examples, with every sentence carrying operational value. Minor typos do not undermine its clarity, and the practical examples make it immediately actionable for an agent.

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 output schema exists, the description does not need to explain return values. It covers purpose, parameter selection, source of valid ids, output-language/format switches, record-count limits, gated-field caveats, and disambiguation limitations – everything an agent needs to call this 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 coverage is 100%, but the description adds substantial value by explaining the id/name relationship, that ids come from query_companies, that name is a fuzzy lookup, and by providing concrete examples. It also clarifies the purpose of language='en' and response_format='json' for agent consumption, going beyond the schema's enum descriptions.

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

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

The description opens with a specific verb ('fetch') and a precise resource ('one company or project record'), scoping the tool to rCB producers, international players, or equipment vendors and listing the detail fields returned. It is clearly differentiated from siblings like query_companies by emphasizing single-record retrieval by entityid or name.

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 Guidelines section explicitly tells the agent where ids come from (query_companies), when to use fuzzy name lookup, and how to set language and response_format for agent parsing. It also states limits: one record per call, no batch mode, and name lookup returns only the first match and is not disambiguation – effectively defining when not to rely on this tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: metadata-only access (no record content) and counts refreshing per data release (schema 1.8), which helps the agent set expectations correctly.

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

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

The description is compact yet information-dense, with clear labels (Purpose, Guidelines, Limits, Ex) and no filler. Every sentence contributes actionable information, and the key guidance is front-loaded.

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 metadata-overview tool with an output schema and strong annotations, the description covers everything an agent needs: what the tool returns, when to call it, how to configure parameters, what it excludes, and example use cases. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline of 3 applies. The description repeats and reinforces the parameter guidance (language='en', response_format='json') but does not add meaning beyond what the schema already documents.

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

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

States a specific purpose: dataset overview with version, record counts, entity-ID rules, confidence grading, verified-supplier counts, tool index, and module map. It clearly differentiates itself from the many query_* and get_* siblings by being the server-level discovery tool to call first.

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 instructs the agent to call this tool FIRST when discovering the server and then route to specific query_* tools. It also gives concrete setting guidance (language='en', response_format='json') and example queries, leaving no ambiguity about when and how to use 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?

Beyond read-only/idempotent annotations, the description discloses important behavioral limits: only index values and ratios are returned, absolute prices and source platforms are never disclosed, and some regions are excluded when product caliber is incomparable. It also reveals the 1000-point benchmark basis, which is meaningful context an agent would not otherwise know.

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

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

Longer than necessary for a simple tool, but it is clearly organized under Purpose, Guidelines, Limits, and Ex labels and front-loaded with the core purpose. The additional details about category context and data limitations justify most of the length.

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 definition covers default behavior, parameter combinations, category codes, regional limitations, output restrictions, and related tools, while an output schema exists for return value structure. For a multi-tier, multi-region index tool, this is sufficient for correct invocation.

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 for parameters is 100%, but the description adds value by explaining the practical effect of omitting arguments, listing region codes, supplying category codes (G-TPO/G-CRB/G-WIRE/G-RCB), and giving query examples. The tier parameter is only indirectly explained through the Tier 1/Tier 2 category descriptions, but the schema handles its basic definition.

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 identifies a specific resource ('rCB feedstock (end-of-life tyre) price index'), defines its global caliber, lists Tier 1 and Tier 2 categories, and clarifies the benchmark (global weighted average = 1000). It also states the primary use case for the scrap tyre collection price, making the tool's scope unmistakable against siblings like get_rcb_index.

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 Guidelines section explicitly covers no-argument, region, and category invocation modes, and names two sibling tools for complementary use. It stops short of explicitly saying when not to use this tool versus alternatives, but the invocation conditions and examples provide clear selection 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 readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: official sources are labelled per record, market-size figures are public-information compilations and labelled as such, and the tool deliberately excludes prices. This goes beyond the annotation safety profile and helps the agent set expectations about data provenance and 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 description is well-structured with labelled sections (Purpose, Guidelines, Limits, Ex). Each sentence contributes either scope, usage direction, limitations, or examples. Despite being longer than average, there is no redundancy, and the most important purpose and usage information is front-loaded.

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

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

For a tool with 2 optional parameters, an output schema, and clear annotations, the description fully covers what the tool returns, how to filter it, what it excludes, and how it relates to sibling tools. The examples illustrate realistic queries. Nothing an agent needs to select and invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents both parameters. The description adds value by explaining that no arguments returns the full topic, that topic narrows to one section, and by giving concrete query examples for topic values. This clarifies parameter usage beyond the schema's terse value list.

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 precise statement of the resource: the end-of-life tyre (ELT) collection topic for the rCB supply chain, enumerating specific content areas (policy targets, 2026 standard system, collection network, value ladder, market sizes). It also names the sibling tools it should be paired with and clarifies it does not return prices, effectively distinguishing it from get_elt_price_index and get_feedstock_overview.

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 Guidelines section is explicit: call with no arguments for the full topic, or pass a topic value to focus one section. It also names alternatives and complements: pair with get_elt_price_index for collection prices and get_feedstock_overview for arisings. Limits explicitly state what this tool does not do (returns prices), so an agent knows when not to use 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?

Annotations already declare the tool read-only, idempotent, and non-destructive, so the bar is lower. The description still adds valuable behavioral context: figures come from official and industry-association sources, each record is labelled with its statistical body, and data from different bodies must not be merged due to scope differences. It also clarifies that the tool does not return prices. This goes well beyond the annotations.

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

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

The description is well-structured with labeled sections: Purpose, Guidelines, Limits, and Ex. It front-loads the scope and the price-tool alternative, then packs source caveats and examples into a compact format. Every sentence earns its place, and there is 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?

For a read-only overview tool with an output schema, no required parameters, and strong annotations, the description is complete. It covers what the module includes, the source caveats, when to use it, what it does not return, and example queries. Nothing an agent needs to invoke and interpret it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for both parameters, so the baseline is 3. The description adds meaning to the region parameter by explaining it focuses the arisings table. However, it lists region values as CN/EU/US without mentioning GLOBAL, despite GLOBAL being valid in the schema enum; this is a minor gap. Language is optional and self-explanatory, so no additional description 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 states a precise purpose: an overview of the rCB feedstock module covering waste tyre arisings, recovery volumes, the processing-path value ladder, and module categories. It explicitly separates itself from pricing tools by stating it does not return prices and naming get_elt_price_index as the price tool. The title 'Feedstock Price Overview' is slightly misleading, but the description resolves this immediately.

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

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

The description gives explicit when-to-use guidance: use this tool before quoting feedstock availability or recovery-rate figures, then use get_elt_price_index for prices. It also explains the effect of the region parameter on focusing the arisings table. This is strong, actionable guidance with an explicit alternative tool.

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

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds meaningful behavioral caveats beyond the annotations: the ranges are compiled from public specifications and are not a supplier certificate, and the gap analysis is relative only to N550. These constraints materially affect how an agent should interpret results.

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 with Purpose, Guidelines, Limits, and Ex sections, front-loading the core purpose before usage details. Every sentence carries information; there is no filler or repetition of the schema's enum descriptions. Length is justified by the richness of the tool's output.

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 parameter count, 100% schema coverage, existing output schema, and strong sibling context, the description is complete. It covers invocation alternatives, data provenance, range semantics, a key comparator limitation, and realistic examples. Nothing an agent needs to correctly select and call this tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying the id/name alternatives and giving concrete examples such as get_grade(id='rcb-grade-n550') and get_grade(name='Super P', response_format='json'), which demonstrate the relationship between parameters. It does not add much beyond the schema for language and response_format, but the examples are genuinely useful.

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

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

The description opens with a specific verb and resource: 'fetch one carbon black grade in full', then enumerates exactly what is included: six ASTM parameters as min/max ranges, test-method standards, application list, process route, brand owner, and the N550-anchored gap analysis for rCB types. This level of detail clearly differentiates it from siblings like get_grade_spec and query_grades.

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?

Guidelines explicitly state how to call the tool by semantic id or grade name, and direct the agent to query_grades for id discovery and get_grade_spec for specification detail. Limits are also stated: one grade per call, ranges are from public specs not a guaranteed certificate, and gap analysis is only against N550. This is exemplary usage routing.

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 readOnly, idempotent, non-destructive. The description adds valuable behavioral context beyond that: it specifies that output is limited to spec ranges, not lot-specific data, and provides example queries. This enriches the agent's understanding of the tool's behavior without contradicting 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 structured with Purpose, Guidelines, Limits, and Examples, making it easy to parse. It is slightly verbose but every section adds value; the front-loaded purpose is strong. Could be tightened, but it's well-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?

Given that an output schema exists, the description does not need to explain return format. It covers what the tool does, when to use it, what it does not cover, and provides examples. Nothing essential is missing for an agent to select and call it 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%, so baseline is 3. The description adds meaning by explaining that 'grade' is the primary input (with examples like n330, n550) and 'type' is optional, and provides query examples that demonstrate parameter combinations. This goes beyond the schema's simple descriptions, warranting a 4.

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: retrieving ASTM spec parameter ranges (min/max/unit/test method) for carbon black grades. It distinguishes itself from siblings by positioning as the 'standard-parameters reference' and explicitly contrasts with 'lot-specific CoA data', making its scope 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?

Provides explicit usage instructions: 'pass grade (e.g. n330, n550) plus optional type' and states when to use ('use to judge whether an rCB sample falls in spec'). It also defines limits ('spec ranges only - not lot-specific CoA data'), effectively telling the agent when NOT to use it, which is exactly what this dimension asks for.

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 key behavioral traits beyond the annotations: 'index points only - dimensionless, non-tradable, and never to be converted into an absolute price' and 'early periods may carry an anchor carried forward from an earlier month, and the output flags when this happens.' These are non-obvious caveats an agent needs and are not visible in the schema or annotations.

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

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

The description is front-loaded with the purpose, then guidelines, limits, and examples. Every sentence earns its place: purpose, defaults, date format, sibling routing, non-tradability caveat, anchor caveat, and examples are all packed into a compact labeled structure.

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 output schema exists, the description doesn't need to explain return fields in detail. It covers purpose, usage, limitations, caveats, defaults, and examples. Nothing essential is missing for an agent to select and invoke this tool correctly among 30 siblings.

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 four parameters. The description adds extra semantics by explaining defaults and giving examples such as get_grade_trend(grade='P2') and get_grade_trend(grade='P1', start_date='2025-12', end_date='2026-09'). It could go slightly further by clarifying that start_date and end_date are optional in practice despite being marked required, but the examples imply this.

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 begins with a specific verb-resource pair: 'return the historical series of the layered rCB index for one grade tier' and lists exact output components (assessed benchmark price, period-on-period change, anchor, adjustment factor, verification factor). This clearly differentiates get_grade_trend from siblings like get_rcb_index and get_price_trend by naming them explicitly.

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

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

The Guidelines section states the default grade/region, instructs date formatting as YYYY-MM, and explicitly says 'pair with get_rcb_index for the current cross-section and get_price_trend for category-level trends.' This gives an agent clear when-to-use and when-not-to-use context relative to 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?

Beyond the readOnly/idempotent/destructive annotations, the description adds important behavioral limits: 'one record per call' and 'no legal opinion and no applicability assessment for a given company, product or shipment.' This prevents an agent from overreaching the tool's scope, which is valuable context the annotations alone don't 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 organized into Purpose, Guidelines, Limits, and Ex sections, each earning its place. It is dense but not bloated, with examples at the end to reinforce correct invocation without unnecessary prose.

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

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

For a single-record read tool with an output schema, the description is complete: it explains what the tool returns, how to get the id, what it cannot do, and provides invocation examples. The annotations cover safety, and the output schema covers return structure, so nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description enriches the parameter semantics with concrete examples ('cbam', '2023/956'), the relationship between id and name, and the source of valid IDs (query_policies). It also adds practical guidance on response_format for agent pipelines, going beyond the schema's default-only descriptions.

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

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

The description states a specific verb and resource: 'fetch one regulation record in full' and enumerates the exact fields returned (issuing body, policy type, scope, impact, compliance deadline, source link, summary). It clearly distinguishes itself from the sibling query_policies by focusing on retrieving a single record detail rather than listing/searching policies.

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

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

The description explicitly says when to use the tool: accept an id from query_policies or a name/identifier keyword, and instructs agents to run query_policies first when the id is unknown. It also provides concrete usage guidance for response_format='json' in agent pipelines, making the alternative workflow 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is covered. The description adds valuable behavioral context: 'reference bands and relative structure only - no exchange quote and no trading recommendation; ELT feedstock absolute prices are deliberately not published; every figure is a range, never a single transactable value.' This informs the agent about the nature of the data returned beyond what annotations state.

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 with labeled sections (Purpose, Guidelines, Limits, Ex) and is front-loaded with the core purpose. Every sentence adds value: purpose, usage rules, limitations, and examples. It is appropriately detailed for the tool's complexity and does not contain 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 tool has an output schema (as indicated in context signals), so return values need not be explained. The description covers all necessary aspects: what it returns, how to narrow results, which sibling tools to pair with, and critical limitations. Nothing essential for an agent to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds extra semantics by explaining that leaving product/section empty returns the full set, and provides concrete examples (get_price_benchmark(section='rCB'), etc.) that clarify how parameters combine. This goes beyond the schema's per-parameter descriptions, though not drastically.

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 purpose: returning the rCB and virgin carbon black price benchmark structure, detailing the anchor, rCB band, feedstock prices, and more. It clearly distinguishes from siblings by mentioning pairing with get_elt_price_index and get_rcb_index, and explicitly says it provides no exchange quote or trading recommendation, which differentiates it from other price 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?

The description provides explicit usage guidance: 'leave product and section empty for the full set; narrow with section=\'rCB\' / \'feedstock\' / \'international\' / \'cost\'; pair with get_elt_price_index for feedstock indices and get_rcb_index for the layered index series.' It also gives limits (no exchange quote, no trading recommendation) and examples. This is clear when-to-use and 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 readOnly, idempotent, and non-destructive behavior, so the description adds context beyond those: it discloses return constraints such as 'official anchors only', 'no raw quote tables', and 'trend direction and anchors only'. This clarifies what the agent will receive and what it will not, which is valuable for setting 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?

The description is well-organized with Purpose, Guidelines, Limits, and Ex labels, making it easy to scan. Every sentence contributes meaningful information without redundancy, and the most important purpose constraint is front-loaded. It is dense yet 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?

For a simple two-optional-parameter read-only tool with an output schema and strong annotations, the description covers purpose, parameters, usage context, limitations, and a sibling alternative. No critical information appears missing for an agent to correctly select and invoke the 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 schema already covers both parameters at 100%, but the description adds practical value by enumerating segment values in English (domestic/export/import/feedstock/rCB), giving a concrete period example (2026), and emphasizing both are optional. The example 'rCB price trend 2026 CN' makes parameter usage more concrete than the schema alone.

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

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

The description clearly states the tool's function: returning native carbon black / rCB price trend direction with official anchors, not raw quote tables. It also distinguishes itself from get_rcb_index by noting that tool provides numbers while this one provides direction. This gives an agent a precise picture of what the tool does and what it does not do.

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

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

Usage is explicitly guided: pass optional segment and period, use for directional questions like 'is rCB price rising', and pair with get_rcb_index for numeric values. It also states limits ('no absolute quotes'), making it clear when not to rely on this tool. The example query further anchors correct invocation.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context beyond this: it states the reference range is computed via the anchor method and is not a transaction price, and that the VF factor is forward-only (no backfill before 2026-Q3). This gives the agent important caveats about the data's nature and temporal limitations, which is 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 well-structured with clear sections (Purpose, Guidelines, Limits, Example). It is front-loaded with the core purpose, then provides actionable guidance, limitations, and a concrete example. Every sentence earns its place—there is no fluff or repetition. The example usage ('rCB index P2 CN 2026-09', 'current rCB reference price EU') is practical and demonstrates natural language invocation.

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 is highly complete given the tool's complexity. It covers the anchor method, parameter semantics, defaults, limitations, and even provides examples. Since an output schema exists, the description does not need to detail return values. It explains key caveats (non-transaction price, forward-only VF) that affect interpretation. The only minor gap is lack of explicit handling for invalid inputs or edge cases, but this is acceptable given the output schema and the thorough parameter guidance.

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 each parameter has a description in the schema. However, the tool description adds meaning beyond the schema: it explains the grade levels (P1 refined / P2 standard / P3 industrial) which are not defined in the enum descriptions, and clarifies the date format and default behavior (blank = latest) and region defaults. This enriches the agent's understanding of parameter choices.

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: a query that returns the rCB price index (ABP), absolute reference price (ARP), and AF/VF decomposition for a given grade, period, and region. It specifies the anchor method (N550) and distinguishes itself from other price tools by naming exactly what it returns. The verb 'query' and resource 'rCB price index' are precise, and the mention of the anchor method helps differentiate it from other index tools like get_elt_price_index.

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 explicit guidelines: how to pass each parameter (grade with defaults, date format and blank behavior, region with defaults), and it explicitly recommends pairing with get_rcb_vcb_spread for discount and get_grade_trend for history. It also discloses limitations (reference range is not a transaction price; VF is forward-only). This is comprehensive, telling the agent exactly when and how to use this tool and when to use 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond that: the N550-anchored reference range, the single-period snapshot behavior (no forecast), and the caveat that output is not a transaction price. These interpretive constraints help the agent use the result correctly.

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 labeled sections (Purpose, Guidelines, Limits, Ex) make the description highly scannable. Purpose is front-loaded, each sentence earns its place, and the example is the minimal illustration needed. No filler or redundancy with the schema's 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?

With an output schema present, annotations covering the safety profile, and full parameter documentation, the description covers the remaining essential context: anchoring basis, temporal scope, exclusion limits, and a usage example. The only gap is that it doesn't explicitly differentiate from semantically-similar siblings (get_rcb_index, compare_rcb, get_substitution_boundary), leaving that to inference.

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's parameter list ('date (YYYY-MM), region (CN/EU/NA/SEA), grade (P1/P2/P3)') largely restates the schema, but the concrete example 'rCB vs N550 discount CN 2026-09' adds value by demonstrating how a natural-language request maps to the actual parameters, which nudges this to a 4.

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-resource pair ('anchored spread between rCB and native carbon black') and defines the deliverable precisely: anchoring ratio rho, discount rate, and price gap per grade/region/period. It positions itself as the quantitative 'how much cheaper is rCB' indicator, which clearly distinguishes it from sibling tools like get_grade_trend (direction) and calculate_saving (custom quotes).

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 Guidelines section explicitly names complementary tools and their conditions: 'pair with get_grade_trend for direction and calculate_saving for custom quotes.' The Limits section adds exclusion guidance ('not transaction price', 'no forecast'), telling the agent when this tool is inappropriate. It stops short of 5 because it doesn't explicitly route the agent away from closely-related siblings like get_rcb_index or compare_rcb.

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, idempotentHint, and no destructive behavior, so the description does not need to restate those. It adds behavioral scope beyond annotations: the tool covers technical/regulatory possibility only, not commercial viability, and omitting dimension returns all four dimensions. This provides meaningful context without contradicting the annotations.

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

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

The description is compact and front-loaded with labeled sections: Purpose, Guidelines, Limits, and Examples. Every sentence earns its place, with no filler or repetition of schema content. The structure makes it easy to scan and act on quickly.

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

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

For a single optional parameter with strong annotations and an output schema, the description is complete. It covers what the tool does, how to invoke it, what scenarios it applies to, what it excludes, and where to route non-matching needs like pricing.

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 only marks dimension as optional and gives high-level Chinese labels. The description adds exact accepted values in English, the default behavior when omitted, and natural-language query examples like 'rCB application limit in seals'. This materially enriches the bare schema for an AI agent.

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 returns the four-dimension substitution boundary of rCB vs native carbon black and enumerates the dimensions: application limits, regulatory access, certification requirements, material spec constraints. It is specific about the resource and scope, and the Limits section differentiates it from calculate_saving on commercial viability, so it is clearly not a tautology.

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

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

It gives an explicit usage condition: 'call before any substitution feasibility claim' with a concrete example question. It also states an exclusion — not commercial viability — and routes pricing to calculate_saving, giving an agent clear guidance on when not to use this tool.

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

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

Annotations already mark the tool as read-only and idempotent, and the description adds meaningful behavioral limits: it returns metadata and source link only, not full standard text due to copyright, and warns to verify currency before compliance use. This contextualizes expectations beyond the structured annotation 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 uses labeled sections (Purpose, Guidelines, Limits, Ex) with the core purpose front-loaded. Every sentence carries practical information, and the examples are compact yet illustrative.

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 read-only lookup tool with a rich input schema, existing output schema, and strong annotations, the description fully covers purpose, parameter selection, limitations, and usage context. An agent has everything needed to decide whether to call it and how to invoke it 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?

The schema already covers all four parameters in detail, establishing a baseline of 3. The description adds value by giving concrete id format ('rcb-tech-001'), keyword examples ('D8178', 'pyrolysis', 'GB/T 40009'), and clarifying the id-or-name relationship, which helps agents pick the right parameter.

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 a specific verb and resource: 'fetch one rCB technical standard or process by entity ID or keyword'. It enumerates the covered standard families and the returned content (scope, core indicators, status, source link), making the tool's job unmistakable and distinct from sibling getters.

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 states when to use the tool: 'when an Agent needs the governing standard behind a parameter or certification claim' and gives concrete parameter guidance with examples. It does not mention when not to use it or name query_techs as an alternative, so it falls just short of full routing guidance.

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

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

Annotations already signal readOnly, idempotent, and non-destructive behavior. The description adds valuable context beyond those flags: the dataset is UN Comtrade official statistics only, fields have customs-reported versus derived declarations, and the call itself is documentation-only, returning no trade records.

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 with Purpose, Guidelines, Limits, and Ex sections. The core purpose is front-loaded, each section earns its place, and the examples are compact and useful.

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 one optional parameter, an output schema, and strong annotations, the description supplies all necessary invocation context: what the tool documents, when to call it, its data-source limits, and what it will not return. Nothing needed for correct use is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for response_format, including enum values, default, and description. The description only adds call examples rather than new semantic meaning for the parameter, so it stays at the baseline 3 for high schema coverage.

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

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

The description opens with a specific verb and resource: 'describe the trade flow dataset.' It then lists concrete content categories (source, caliber, unit conversion, derived-field declarations, compliance boundaries) and explicitly states it returns no trade records, distinguishing it from query/data-retrieval tools among siblings.

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 guidelines explicitly say to call this before any trade query that will be quoted seems an exact when-to-use instruction. It also states the tool returns no trade records and that only UN Comtrade official statistics are involved, giving agents clear boundaries for when not to rely on 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?

Annotations already mark the tool read-only and idempotent, and the description adds material behavioral caveats: World aggregate rows are excluded from partner rankings, unit prices are not comparable where quantity reporting is incomplete, and only customs-reported totals are included. This goes well beyond the structured annotations.

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

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

The description is organized into Purpose, Guidelines, Limits, and Ex with the most important scoping information front-loaded. Every sentence adds value: output definition, input rule, calculation formula, sibling routing, data limitations, and examples.

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 read-only aggregation tool with zero required parameters, an output schema, and rich annotations, the description covers all the operational context an agent needs: input range, output scope, data caveats, and drilling into a single reporter. No significant gap remains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds usable parameter detail: the exact YYYY format, the empty-period behavior across 2024-2025, and concrete call examples for period and response_format. This is more than the schema alone supplies because it combines syntax with intended usage.

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 precise verb and resource: aggregate UN Comtrade carbon black (HS 280300) records for a period, and lists exactly what is returned (per reporter totals, weighted unit price, top-10 partners). It also distinguishes itself from get_trade_partner_ranking by framing that sibling as the drill-down tool for a single reporter.

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?

Explicit Guidelines and Limits sections tell the agent how to form input (pass YYYY or leave empty to cover 2024-2025), how the weighted price is calculated, and when the data is not appropriate (customs-reported totals only, no commercial quote platform data). The pairing advice with get_trade_partner_ranking gives clear routing versus the nearest 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?

Beyond the readOnly/idempotent annotations, the description discloses operational nuances: World aggregate rows are excluded, shares are computed within the returned set rather than against global trade, and only one reporter-period-flow combination is allowed per call. These are non-obvious behaviors an agent needs to interpret results correctly.

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 organized into Purpose, Guidelines, Limits, and Examples, with every sentence adding useful information. It is long but not bloated; the examples at the end demonstrate realistic calls without explaining what the code already shows.

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 read-only ranking tool with an output schema, the description provides everything needed to select and invoke it correctly: input constraints, result semantics, limits on the response tail, and worked examples. The only caveat is the flow-required mismatch already noted, but that doesn't leave a major contextual gap.

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 five parameters. The description reinforces formats (ISO3, YYYY, X/M) and gives working examples, but mostly repeats schema info; it also states 'flow... required' while the schema's required array omits flow, creating a notable inconsistency that prevents a higher score.

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?

Starts with a specific verb+resource: 'rank partner countries for one reporter, period and flow direction by quantity', and lists the exact returned fields (rank, partner, quantity, value, unit price, share). This is clearly distinct from sibling tools like get_trade_flow_summary or query_trade_flows, which do not promise ranking with share calculations.

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?

Explicit 'Guidelines' and 'Limits' sections state the required inputs, the condition for raising top_n ('only when a longer tail is needed'), and the one-combination-per-call constraint. It lacks an explicit when-not-to-use or named alternative, so it stops short of full 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, destructiveHint=false, idempotentHint=true, openWorldHint=false. The description adds useful context: the limit of 100 rows, the interpretation of absence (not untrustworthy), and the lack of commercial rating/ranking. This goes beyond the safety profile to clarify data 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?

The description is a single structured paragraph with clear sections (Purpose, Guidelines, Limits, Ex) and front-loads the core purpose. It is dense but every sentence serves a purpose; a minor issue is that it could be slightly shorter, but the structure aids readability.

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 that an output schema exists (so return values are separately documented), the description fully covers purpose, usage, limits, and parameter guidance. Nothing an agent needs to know in order to call this tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and all parameters have descriptions. The description adds value by recommending specific parameter combinations (language='en' + response_format='json') for downstream automation and demonstrating usage in examples, which the schema alone doesn't convey.

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 returns companies/projects with verified=true, and explains the 'trust layer' context. It distinguishes itself from siblings by specifying 'call this first' and pairing with get_company for detail, making its scope 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 instructs when to use this tool ('call this first for any recommendation, shortlisting or transaction-style task'), how to pair it with get_company, and provides parameter usage guidance for automation (language='en', response_format='json'). Examples reinforce the intended invocation pattern.

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 establish read-only, idempotent, non-destructive behavior. The description adds genuinely non-obvious traits: source material is Chinese-language, coverage is limited to 63 articles from 2026-04 onward, and 'A-grade' means first-hand rather than independently peer-reviewed. No contradiction with annotations.

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

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

The description uses a clear Purpose/Guidelines/Limits/Examples structure and front-loads the main purpose. It is somewhat long, but each segment—coverage limits, A-grade clarification, and examples—carries real information that helps correct invocation. 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 6 optional parameters, an output schema, and a large sibling list, this description is complete: it defines scope, lists match targets, clarifies the confidence label, states coverage boundaries, and provides usage examples. Nothing an agent needs to call or interpret the result is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers all 6 parameters at 100%, so the description only needs to add value. It does so by explaining keyword matching semantics, giving concrete examples like query_articles(keyword='Pyrum'), and contextualizing category values and the meaning of A-grade. It does not walk through every parameter, but the schema already handles that.

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

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

The description opens with a specific verb and resource: 'search the first-hand research archive of the RCB industry WeChat official account,' followed by what is matched (title/topic/author/scope). This clearly distinguishes query_articles from siblings such as get_article (single-article retrieval) and other data-query 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?

The 'Guidelines' section gives concrete usage context: keyword matches certain fields, category filters to a single theme, and the coverage/date limits. It does not explicitly name alternative sibling tools or state when not to use query_articles, so it stops short of full when/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?

Annotations already declare the tool read-only and non-destructive, and the description adds meaningful behavioral context beyond that: only publicly releasable cards are returned, each card is a single fact with no recommendation, and every card returns its source. This materially helps an agent predict what the tool will and will not do.

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 with labeled Purpose, Guidelines, Limits, and Ex sections, and the purpose is front-loaded. Every sentence contributes either a filter rule, a return characteristic, or an exclusion, 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?

Given the read-only annotations, full input schema, and existing output schema, the description adds the essential missing context: what kind of cards are searched, how search behaves, what results contain, and what is excluded. An agent has enough information to invoke this 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 coverage is 100%, so baseline is 3; the description does add useful semantics by explaining that keyword matches title and fact fields and that type and region combine to narrow results. However, the example uses type='标准解读', which is not among the enum values in the schema, making the parameter guidance partially unreliable.

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 the specific verb 'search' and names a unique resource, the public knowledge-card collection, further defining it as atomic sourced facts compiled from statistics, standards, papers, and official announcements. This clearly distinguishes it from sibling query_* tools that target other resource types.

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

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

The description gives concrete search guidance: keyword matches title and fact fields, type and region can be combined to narrow results, and each result includes the fact summary and its source. It also states exclusions such as gated/internal cards never being returned, but it does not explicitly name an alternative tool, so the selection guidance is strong but not fully explicit.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond those: it discloses the record-level granularity ('capacity is a record attribute, not a separate projects table') and the confidence grading (A/B/C). It also explains that results are filtered to company-level records, which affects how an agent interprets results. 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 efficiently structured with labeled sections (Purpose, Guidelines, Limits, Example). The purpose is front-loaded, and every sentence carries functional weight—no filler. It is longer than a one-liner but justified given the tool's complexity (11 parameters, multiple filter dimensions) and the guidance it provides.

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 11 parameters (all documented), an output schema, and no nested objects. The description covers the discovery intent, filter combinations, record granularity, confidence levels, and pairing with get_company. An agent has everything needed to call the tool correctly, including example queries. The output schema handles return format, so no further description is required.

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?

Although the input schema covers all 11 parameters with descriptions (100% coverage), the description adds meaning beyond the schema by explaining how to combine parameters: 'combine segment (rcb/virgin/conductive) + status (在建/规划/under construction) + location' and by providing concrete example queries ('rCB projects under construction in China', 'conductive carbon black producers worldwide'). This elevates the semantic value beyond the schema's per-parameter documentation.

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

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

The description opens with a clear statement of purpose: 'query the global rCB company and project database... the discovery tool for capacity projects and players in the chemical recycling industry.' It specifies the resource (company/project database) and the verb (query), and differentiates from sibling get_company by noting it pairs for full entity detail. The scope is precise and immediately actionable.

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 explicit usage guidance: 'combine segment (rcb/virgin/conductive) + status (在建/规划/under construction) + location' and 'for capacity-project lists filter status, then pair with get_company for full entity detail.' It also states a clear limitation ('company-level records only - capacity is a record attribute, not a separate projects table'), which serves as a when-not and points to the correct alternative.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral boundaries: exactly 37 records, spec ranges rather than lot CoA, rCB vs-N550 gap analysis, and the ash-gap limitation on rCB reinforcement. This materially prevents agents from misinterpreting the data as lot-specific or broader than it is.

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

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

The description is dense but organized into Purpose/Guidelines/Limits/Ex segments and front-loads the resource. A few semicolon-packed clauses reduce readability, but every sentence contributes domain-relevant constraints or usage guidance.

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 7-parameter, all-optional search tool with a complete schema, annotations, and an output schema, the description covers library composition, dataset limits, filter-combination guidance, and example queries. Nothing essential for selecting or invoking the tool appears to be missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter already explained. The description adds value by showing how to combine type, category, and keyword, and by giving example query intents. It doesn't deeply explain parameter formats, but that's unnecessary given the schema's completeness.

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

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

States a specific verb and resource: 'search the carbon black grade benchmark library' with explicit scopes (ASTM D1765 native grades, conductive benchmarks, rCB types). The word 'search' plus the enumerated contents distinguishes it clearly from sibling tools like get_grade or get_grade_spec.

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 an explicit Guidelines section telling agents to combine type/category/keyword and to use this tool 'as the anchoring benchmark layer before any rCB vs native comparison'. It also gives concrete example queries, but it does not name sibling tools to use instead under specific circumstances, so it lacks explicit when-not/alternative routing.

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, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond annotations: verified=true excludes B/C-grade entries, each hit carries issuing body/policy type/scope/rCB impact/deadline/source link, and results are limited to policy documents only. This gives the agent a clear sense of what will be returned and what is excluded.

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 organized into Purpose, Guidelines, Limits, and Examples, making it easy to scan. It is fairly long but every section adds value for an 8-parameter search tool. Minor formatting issues like 'PPWR /CSRD' aside, it remains front-loaded 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?

Given the high parameter count, existing output schema, and rich sibling context, the description covers the essential aspects: what the tool searches, how to combine filters, when to require verified results, what each result contains, and clear usage examples. The limits section prevents misuse. It is sufficiently complete for an agent to invoke the tool correctly without external knowledge.

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 schema already documents all parameters. The description adds valuable parameter-usage guidance beyond the schema, such as 'combine keyword with region and category', 'set verified=true when the answer must rest on government documents', and concrete examples like query_policies(keyword='CBAM') and query_policies(keyword='ISCC', language='en'). This helps the agent choose effective combinations.

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 an explicit verb and resource: 'search the global rCB regulation library'. It then enumerates the covered jurisdictions and frameworks (EU CBAM/EUDR/ESPR, China MIIT, India/Korea EPR, US), which clearly identifies the tool's scope and differentiates it from singular get_policy or other query_* 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?

Guidelines are explicit and actionable: 'combine keyword with region and category' and 'set verified=true when the answer must rest on government documents'. Limits also state when the tool is not appropriate ('no legal advice and no ruling on how a rule applies to a specific company or shipment'). However, it does not explicitly name sibling alternatives or state when to choose get_policy 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?

Beyond the readOnly/idempotent annotations, it discloses meaningful behavioral constraints: only summaries and key parameters are returned, full standard text is never reproduced, C-grade entries are unverified leads, and the category taxonomy is the tool's own rather than the issuing body's. This materially shapes agent 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?

The description is organized into Purpose/Guidelines/Limits/Ex with no filler. The scope enumeration is dense but informative, and the examples are pragmatic invocation hints that earn their 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?

With 8 optional parameters and an output schema present, the description covers scope, query strategy, reliability caveats, and example calls. Nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description adds value by recommending parameter combinations, mapping category labels in English to the Chinese enum values, and clarifying verified=true and C-grade semantics. This goes beyond bare schema repetition.

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

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

States a specific verb ('search') and resource ('rCB technology and standards library'), then names concrete scope: ASTM D36 series, Chinese standards, continuous pyrolysis, post-treatment, and sustainable carbon black. This clearly differentiates it from sibling get_tech, which implies a single-record lookup, and from other query_* 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 explicit guidance: combine keyword with category and region, use verified=true for standards-backed entries, and includes three example invocation patterns. It does not explicitly name sibling alternatives or state when not to use this tool, 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?

Beyond the read-only/idempotent annotations, the description discloses important behavioral and data-quality traits: customs-reported data only, derived fields, missing quantity handling ('missing_qty'), and a USD/t guardrail band. This gives agents substantial context for interpreting results and avoiding misuse.

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 organized with clear labeled sections (Purpose, Guidelines, Limits, Ex) and every sentence carries useful information. It is dense but not bloated, and the purpose is front-loaded.

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 complexity and the presence of a full input schema and output schema, the description is complete: it covers dataset scope, supported values, derived fields, limitations, guardrails, and example queries. Nothing critical is missing for selecting and invoking 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 coverage is 100% and the schema already describes each parameter, including flow enums and partner formats. The description adds some useful context like derived-field definitions and natural-language examples, but largely restates parameter guidance already present 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?

The description states a specific verb ('query'), a precise resource (global carbon black HS 280300 bilateral trade flows), and the data source (UN Comtrade official customs statistics). It also gives concrete scope details (1751 records, reporters, years) and examples, making it easy to distinguish from sibling trade 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?

It clearly describes when to use the tool: for capacity shift, trade-partner concentration, and price-gap analysis, and explicitly excludes commercial quote platforms. It does not name sibling tools as alternatives or state when not to use them, so it falls just short of a perfect score.

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