Kyrodata — Brazil Trade, Crop & Commodity Data

Commodity hub summary and forecast verdict

kyrodata_get_hub_summary

Read-onlyIdempotent

Reads the Kyrodata pyramid verdict for a commodity hub: direction of the leading horizon, expected move in % per horizon (1, 3, 6 and 12 months), the 80% band as a half-width in percentage points, which levels drive the verdict and the measured accuracy. A horizon without an arrow names the reason, and an empty one means no level passed the confidence gate: no signal, not a stable price. horizon re-centres the verdict on the month given; omitted, it centres on the horizon the model leads with, and all four come back either way. It also carries the month-over-month and year-over-year % change of the reference price and its kind (doméstico, mundial or paridade de exportação), never the price level. This is the tool for where a hub's price is heading. The arithmetic behind the verdict is kyrodata_explain_pyramid_level, and the physical harvest of the same hub is kyrodata_get_climate_reading. Credit class: level (any level tool in a 60-second session = 2 credits; a session is capped at 3).

Input Schema

TableJSON Schema

Name	Required	Description
`hub`	Yes	Which commodity hub to read.
`horizon`	No	How far ahead the forecast looks: 1, 3, 6 or 12 months from the last published month. Omitted, the verdict centres on the horizon the model leads with; given, it re-centres on that one — either way all four horizons come back.
`response_format`	No	How much of the answer to return. `concise` (the default) carries the headline figures; `detailed` adds the row-level series behind them and counts against the export quota.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Raw numbers behind the text.
`memo`	Yes	true = identical call in the last 10 min, served again: 0 credits.
`rows`	No	Table rows; detailed only, capped per tool.
`error`	No	Failure message when status = error.
`links`	Yes	screen = product page with these numbers.
`denied`	No	When status = denied: reason, feature, upgradeUrl.
`status`	Yes	ok = data; denied = plan; error = failure or timeout.
`window`	No	Like-for-like window: from, to (YYYY-MM), label, months, crossesSeason.
`caveats`	Yes	Reading caveats.
`credits`	Yes	charged, balance (null = unlimited), resetAt, session {charged, endsAt} of the 60-s billing session.
`sources`	Yes	Per source: label, nameable, asOf.
`dataVersion`	Yes	Identity of the data that answered.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 10, 2026

changedInput schema / properties / horizon / description

Previous value: -"How far ahead the forecast looks: 1, 3, 6 or 12 months from the last published month."New value: +"How far ahead the forecast looks: 1, 3, 6 or 12 months from the last published month. Omitted, the verdict centres on the horizon the model leads with; given, it re-centres on that one — either way all four horizons come back."

AddedSep 7, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark the tool read-only and idempotent, and the description adds valuable behavior beyond that: omitted horizon centers on the model's leading horizon, all four horizons always come back, arrowless horizons name the reason, empty horizons mean no signal, and it never returns the price level. It also discloses the credit class and session cap, which are non-obvious runtime behaviors.

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 dense but every sentence earns its place: the headline capability is front-loaded, parameter behavior is explained, alternatives are named, and the credit quirk is placed at the end. It is long because the tool has genuine nuances, not because of filler.

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 rich schema with 100% parameter coverage, a present output schema, and strong annotations, the description covers everything needed to select and invoke the tool correctly. It explains the return contents, horizon semantics, no-price-level limitation, related tools, and credit cost. No meaningful gap remains.

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%, so the baseline is 3. The description goes further by explaining the horizon parameter's re-centering behavior and that all four horizons return regardless, and it clarifies the response_format distinction including the export-quota implication for 'detailed'. This adds real meaning beyond the schema without needing to repeat the enum values.

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 opens with a specific verb and resource: it reads the Kyrodata pyramid verdict for a commodity hub, and then enumerates exactly what comes back (direction, expected move %, band, driving levels, accuracy). It also differentiates itself from siblings by naming kyrodata_explain_pyramid_level and kyrodata_get_climate_reading and stating what each is for.

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?

It explicitly says 'This is the tool for where a hub's price is heading' and points to alternatives: the arithmetic behind the verdict is kyrodata_explain_pyramid_level, and the physical harvest is kyrodata_get_climate_reading. This gives an agent clear routing decisions without needing to inspect sibling schemas.

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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