EU Trade Explorer

Get Price Shocks

get_price_shocks

Detect sudden price regime shifts ("rapid price changes"), accounting for each flow's usual volatility so naturally volatile flows aren't over-flagged. Returns ranked shock events per flow with timeline phases (price spike/drop, volatile trade, return to baseline, ...), magnitude and 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'.
`lookback_years`	No	Years before the shock used to establish the coefficient-of-variation baseline.
`min_abnormality`	No	Minimum unusualness of the price shift vs. baseline volatility (CV multiples). Lower = more (weaker) shocks flagged.
`cumulative_share`	No	Only the top trading partners that together account for this share of total trade are analysed.
`max_shocks_per_pair`	No	Number of non-overlapping shocks kept per trading partner.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses the output structure (timeline phases, magnitude, series) and the methodology (volatility adjustment), which adds transparency. However, it does not mention any prerequisites, limitations, or edge cases, leaving some behavioral aspects unspecified. No contradiction with annotations since none exist.

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 two sentences, front-loaded with the primary action ('Detect sudden price regime shifts') and every clause adds value. It explains the algorithm's intent and the return format without unnecessary words.

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?

For a tool with 8 top-level parameters, nested query objects, and an output schema, the description provides sufficient context about the tool's core function and typical outputs. It clearly states what shocks are detected and the output components. It lacks explicit differentiation from similar tools, but the purpose is clear enough given the high schema coverage.

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 baseline is 3. The description itself does not add parameter-level semantics beyond what the schema already provides. It mentions flow volatility and shock detection in general terms, but does not elaborate on specific parameters like lookback_years or min_abnormality.

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 uses a specific verb 'Detect' and names the resource 'sudden price regime shifts', clearly distinguishing this from sibling tools like get_supply_shocks (supply shocks) and get_volatility (general volatility). It also explains the methodology and expected output, making the purpose unambiguous.

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 usage for detecting abnormal price movements by mentioning volatility adjustment, but it does not explicitly state when to use this tool over alternatives or provide exclusions/alternatives. No comparison to get_supply_shocks or get_volatility is given, so guidance is only implicit.

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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