EU Trade Explorer

Get Pattern Shift

get_pattern_shift

Before/after trade-pattern shift around a split point (query.midpoint): for each entity, compares average quantity and price before vs. after, and returns scatter points (quantity-change % vs. price-change %) with quadrant labels -- top-right = demand-driven growth, top-left = supply constraint/monopoly risk, bottom-right = dumping/oversupply risk, bottom-left = market contraction. Suggested workflow: spot a shock date via get_supply_shocks / get_price_shocks first, then set it as query.midpoint here.

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.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the before/after comparison logic and the meaning of each quadrant, which is essential behavioral detail. It does not mention limitations or data requirements, but the output schema and full parameter descriptions mitigate this.

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 dense sentences with no redundancy. It front-loads the core purpose and follows up with a practical workflow, making every part earn its place.

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

Completeness5/5

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

Given the tool's complexity, the output schema, and full parameter descriptions, the description covers the essential gaps: what the tool does, how to interpret results, and how it relates to sibling tools. It is complete enough for an agent to select and invoke correctly.

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

Parameters4/5

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

Schema coverage is 100% for top-level parameters, and query subfields all have descriptions, so the baseline is 3. The description adds value by explicitly explaining that query.midpoint is the split point and how it fits into the workflow, which goes beyond the schema's field description.

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 a specific verb ('compares') and resource ('average quantity and price before vs. after') and defines the output (scatter points with quadrant labels). It distinguishes itself from siblings by framing it as a post-shock analysis step, not a shock detector.

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

Usage Guidelines4/5

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

The 'Suggested workflow' explicitly tells the agent to use get_supply_shocks/get_price_shocks first and then set the shock date as query.midpoint. This gives clear when-to-use context, though it does not explicitly list alternative tools to avoid.

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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