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Get Volatility Summary

get_volatility_summary

Report-section helper ("Volatility & shocks"): the entities analysed are picked by get_volatility (ranks by trade volume, reports each one's CV) and by get_price_shocks/get_supply_shocks at default parameters (both already scoped to top-value partners via cumulative_share=0.8) -- never re-picked from the shocks themselves. Retries shock detection once with loosened thresholds before concluding "no shocks"; an empty result is itself a reportable finding, not a gap. When shocks are found, the top events by abnormality and their series are the section's findings; when none are found (even after the retry), stability is the finding. get_pattern_shift is intentionally not part of this bundle -- its before/after quadrant framing is too easily misread; call it directly if you specifically need it.

Returns {narrative_facts, chart_data, available, reason}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe 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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral burden. It discloses the retry mechanism for shock detection, explains that an empty result is a reportable finding rather than a gap, describes how findings are prioritized, and mentions the intentional exclusion of `get_pattern_shift`. This is rich, honest behavioral disclosure.

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?

Although the description is relatively long, every sentence earns its place by conveying critical behavioral and relational context that is not available from annotations or the schema. It is front-loaded with the tool's role and well-organized into logical clauses.

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 absence of annotations and the presence of an output schema, the description thoroughly covers the tool's dependencies, retry behavior, reporting semantics, and return shape. It is sufficiently complete for an agent to select and invoke 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?

The single `query` parameter is fully documented by the input schema (100% coverage), including defaults and semantics for each nested field. The description adds no parameter-specific detail beyond what the schema already provides, so the baseline score of 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 identifies the tool as a report-section helper for "Volatility & shocks" and explains that it aggregates findings from `get_volatility`, `get_price_shocks`, and `get_supply_shocks`. It also differentiates itself from `get_pattern_shift`, making its purpose and scope unambiguous.

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

Usage Guidelines5/5

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

Explicit guidance is provided: the tool is a helper used at default parameters, entities are never re-picked from shock results, and `get_pattern_shift` is intentionally excluded from the bundle with a note to call it directly if needed. This clarifies when to use this tool versus siblings.

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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