EU Trade Explorer

Get Volatility

get_volatility

Coefficient-of-variation (CV = stdev / mean) volatility per partner/reporter x product pair, over inactive-period-excluded history. Higher CV = the flow swings more relative to its typical level. Returns per-entity CV bars (with drill-down series) and, unless include_heatmap=false, an entity x sub-product CV heatmap.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`flow`	No	Trade flow to use.	Imports
`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.
`combos`	No	Comma-separated flow x indicator keys to compute at once: imp-qty, imp-price, exp-qty, exp-price. When set, overrides flow/indicator and returns a dict keyed by combo.
`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.
`indicator`	No	Measure for single-grid mode (ignored when combos is set).	quantity
`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'.
`n_top_entities`	No	Number of top entities to include in the volatility bar chart.
`n_top_products`	No	Max number of sub-products in the entity x product heatmap.
`include_heatmap`	No	Whether to compute the entity x sub-product CV heatmap (false is faster, bar chart only).

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, the description carries the transparency burden and does well: it discloses the exact metric, the data scope ('inactive-period-excluded history'), interpretation, and conditional output based on include_heatmap. It could add more about data source or limitations, but for a read-only analytical tool this is solid.

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 concise sentences, front-loaded with the metric definition and formula, and gives the key output structure without unnecessary padding.

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 very rich input and output schemas, the description is sufficient for an agent to understand the core behavior and outputs. It does not need to restate parameter details, and the output schema covers return values.

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 adds context for include_heatmap (bar-only vs. heatmap) and entity level, but it does not explain other parameters beyond what the schema already provides.

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 defines the tool as computing coefficient-of-variation volatility per partner/reporter x product pair, with a specific formula (CV = stdev / mean). This distinguishes it from sibling tools like get_volatility_summary, which presumably provides a more aggregated view.

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 when to use the tool: when you need CV volatility breakdowns across entities and sub-products. However, it does not explicitly state alternatives or exclusions, such as when to prefer get_volatility_summary or get_concentration instead.

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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