EU Trade Explorer

Get Supply Shocks

get_supply_shocks

Detect abnormal collapses in trade volumes ("sudden volume changes"). Flags periods where a partner/product pair's volume drops well below its historical average (>2 sigma by default). Returns ranked shock events per flow, each with timeline phases (baseline, decline, disruption, recovery, ...), magnitude, abnormality score and underlying series.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The 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.
`compact`	No	If 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.
`entity_level`	No	Dimension to analyse. 'partner' -- how concentrated/volatile is the reporter's trade across its trading partners? 'reporter' -- how concentrated/volatile is a given partner's trade across EU member states? When 'reporter', 'partner_name' is required.	partner
`partner_name`	No	Partner country name (English). Required when entity_level='reporter'.
`min_abnormality`	No	Minimum statistical abnormality of the volume crash, in sigma. Lower = more (weaker) shocks flagged.
`cumulative_share`	No	Only the top trading partners that together account for this share of total trade are analysed.
`min_active_years`	No	Consecutive years of regular trade required before a collapse can be flagged.
`max_tolerated_gap`	No	Consecutive quiet periods allowed within an otherwise stable trading relationship.
`relative_threshold`	No	Minimum drop relative to peak volume to count as a collapse (0.10 = 10% drop).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It details the statistical threshold (>2 sigma by default), the output structure (ranked shock events with timeline phases, magnitude, abnormality score, and underlying series). This goes beyond a simple statement of purpose, though it does not cover limitations or edge cases.

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

Conciseness5/5

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

The description is three sentences long, front-loaded with the primary action and resource, followed by key behavioral and output details. Every sentence provides meaningful information with no redundancy or filler, making it appropriately concise and well-structured.

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?

The tool has a complex input schema (9 parameters, nested objects) and an output schema, both of which carry significant detail. The description provides a clear high-level summary—what it detects, how it measures shocks, and what the response contains, including the notable timeline phases. This is adequate given the richness of the structured metadata.

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 description does not need to explain parameters. The description does not add parameter-specific context beyond what the schema already provides, except for referencing the default sigma threshold which is also captured in the schema. Baseline 3 applies.

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's function with a specific verb ('Detect') and resource ('abnormal collapses in trade volumes'). It further clarifies the exact nature of the collapse (volume drops well below historical average) and distinguishes itself from sibling tools like get_price_shocks by focusing on volume rather than price.

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 implies the tool is used for detecting supply shocks but does not explicitly state when it should be used over alternatives, nor does it mention any exclusions. While the purpose is clear, the lack of explicit guidance on alternative tools or contexts leaves the usage dimension at a baseline implied level.

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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