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Get Net Import Reliance

get_net_import_reliance

Net Import Reliance (NIR) = (Imports - Exports) / Apparent consumption, annual, joining PRODCOM production with Comext trade. E.g. 40% means 40% of EU consumption is met by imports; negative means the EU is a net exporter. Returns the NIR % series, its supply/disposition components, sibling-category/per-code comparisons, the resolved PRODCOM codes and availability notes (CN-to-PRODCOM mapping is not always 1:1; see the self.notes / unavailability_type fields in the response). Use query.prodcom_code to pin a specific mapping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe product/reporter/partner-set/period/frequency slice to query -- the same request body every tradedashboard.eu analytical endpoint takes. See its own field descriptions (product, reporter, partner_set, period_start, period_end, frequency, n_top, ...) for details; only `product` is required, everything else has a sensible default.
compactNoIf true, condense long numeric time series (more than ~6 points -- typically monthly/quarterly windows or wide multi-partner/multi-period breakdowns) into summary statistics (first, last, min, max, mean, pct_change) instead of returning every data point. Leave false for full-fidelity series (e.g. to actually plot a chart); set true when you just need the headline trend and want to save context.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden and does well: it discloses the formula, the meaning of negative values, the data sources (PRODCOM and Comext), the non-1:1 CN-to-PRODCOM mapping caveat, and where to find availability notes. It does not mention rate limits or pagination, but for a data-returning tool this is a solid level of behavioral disclosure.

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

Conciseness4/5

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

The description is front-loaded with the formula and interpretation, then lists outputs and the key mapping caveat. It is somewhat dense and long, but every clause adds value and no words are wasted, making it appropriately concise for the complexity of the tool.

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

Completeness4/5

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

Given the complexity of the nested query object and the presence of an output schema, the description effectively covers the core purpose, the metric's interpretation, key caveats, and the parameter that resolves mapping ambiguity. It leaves out no critical information needed for an agent to select and invoke the tool correctly.

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

Parameters3/5

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 thoroughly. The description adds useful guidance about the role of query.prodcom_code in pinning mappings, but it does not substantially expand on parameter semantics beyond what the schema provides, keeping this at the baseline.

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

Purpose5/5

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

The description clearly states the tool calculates and returns Net Import Reliance with the explicit formula and a concrete interpretation example, making it distinct from sibling analytics tools. It also enumerates the specific outputs: NIR % series, supply/disposition components, comparisons, resolved PRODCOM codes, and availability notes.

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

Usage Guidelines3/5

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

The description provides implied context for when to use this tool by explaining the NIR metric and its interpretation, and it gives a concrete instruction to use query.prodcom_code for ambiguous mappings. However, it does not explicitly contrast with alternatives or state when not to use it, leaving some usage ambiguity among the many sibling tools.

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

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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but several concentration-related tools (get_concentration, get_concentration_compare, get_concentration_map) and volatility-related tools (get_volatility, get_volatility_summary) could be confused without careful reading. The detailed descriptions help, but the boundaries are not always immediately obvious.

Naming Consistency4/5

The vast majority of tools follow a consistent get_ prefix pattern for data retrieval. A few exceptions (guidelines_for_a_*, resolve_product_code, search_codes, validate_code) deviate to signal different kinds of operations, which is sensible but breaks uniformity.

Tool Count2/5

With 37 tools, the server is heavily overloaded. Many tools are variations on the same analytical theme (e.g., multiple concentration and production tools) and could be consolidated or parameterized. This creates a steep learning curve and increases the chance of selecting the wrong tool.

Completeness5/5

The tool set comprehensively covers the trade-exploration workflow: product code resolution, hierarchical browsing, headline stats, partner/reporter detail, concentration, volatility, shocks, production metrics, and report generation. There are no obvious gaps or dead ends for its stated purpose.

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