EU Trade Explorer

Get Concentration Map

get_concentration_map

Cross-sectional HHI per country for one year -- the choropleth counterpart of get_concentration. 'partner' entity_level = for every EU member state, HHI of its trade across partners; 'reporter' = for every partner, HHI of its trade across EU member states (subject to the Rotterdam/Antwerp entry-point distortion).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No	Year for the snapshot. Defaults to the latest full year with data.
`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'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the cross-sectional year-specific behavior and the Rotterdam/Antwerp entry-point distortion, which is a useful caveat. It does not detail return behavior, but the output schema fills that gap.

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?

Two sentences, front-loaded with the main purpose, and no unnecessary detail. Highly concise while conveying essential information.

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 description captures the core concept, the two modes, and a caveat. While it does not mention the 'query' or 'compact' parameters, the schema fully describes them. For an agent, it provides enough context to select 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 coverage is 100%, so baseline is 3. The description adds the conceptual explanation of entity_level and the distortion, but the schema already documents entity_level and other parameters thoroughly, so minimal added value.

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 computes cross-sectional HHI per country for one year and identifies itself as the choropleth counterpart of get_concentration, distinguishing it from sibling tools. It also explains both entity_level modes in concrete terms.

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 description implies when to use this tool (when a choropleth/map view is needed) by referencing get_concentration, and explains the two entity_level options. However, it does not explicitly exclude alternatives or state when not to use it.

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.

Resources

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