Open Economics

Query official SICONFI fiscal data

get_siconfi_data

Read-onlyIdempotent

Retrieve an explicit official SICONFI DCA, RREO, RGF, or entity-registry selection. First inspect the schema. This tool preserves account, column, annex, period, entity, raw value fields, and upstream provenance without aggregating or substituting concepts.

Input Schema

TableJSON Schema

Name	Required	Description
`year`	No	Calendar year of the official report.
`annex`	No	Exact official annex name from the SICONFI documentation.
`power`	No	RGF government branch.
`entity`	No	Official IBGE entity code.
`period`	No	RREO bimestre (1-6) or RGF period (1-3).
`sphere`	No	Government sphere: municipal, state, federal, or consortium.
`dataset_id`	Yes	Exact SICONFI dataset ID returned by search_official_data.
`periodicity`	No	RGF only: four-month (Q) or half-year (S).
`report_type`	No	Official SICONFI report type for the selected dataset.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Source-preserving data or schema payload for the selected official dataset.
`meta`	Yes	Response metadata and source provenance.
`links`	No	Related API links.

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedSep 11, 2026

addedInput schema / properties / dataset_id / description

Added value: +"Exact SICONFI dataset ID returned by search_official_data."

addedInput schema / properties / report_type / description

Added value: +"Official SICONFI report type for the selected dataset."

addedInput schema / properties / sphere / description

Added value: +"Government sphere: municipal, state, federal, or consortium."

addedInput schema / properties / year / description

Added value: +"Calendar year of the official report."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "description": "REST-aligned Open Economics response with data, metadata, links, and source provenance.",
+  "properties": {
+    "data": {
+      "description": "Source-preserving data or schema payload for the selected official dataset."
+    },
+    "links": {
+      "additionalProperties": {},
+      "description": "Related API links.",
+      "properties": {
+        "observations": {
+          "description": "Canonical observations endpoint for this dataset.",
+          "type": "string"
+        },
+        "self": {
+          "description": "Canonical URL for this response.",
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "meta": {
+      "additionalProperties": {},
+      "description": "Response metadata and source provenance.",
+      "properties": {
+        "dataset": {
+          "description": "Open Economics dataset identity when supplied."
+        },
+        "provenance": {
+          "description": "Upstream source URLs, versions, timestamps, and methodology details."
+        }
+      },
+      "type": "object"
+    }
+  },
+  "required": [
+    "data",
+    "meta"
+  ],
+  "type": "object"
+}

First observedSep 9, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds genuine value beyond them by stating it preserves raw account/column/annex/period/entity fields and upstream provenance without aggregating or substituting concepts, telling the agent the data is faithful and unmodified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences with the retrieval scope front-loaded followed by the schema-inspection directive and the fidelity guarantee. No wasted text, though the middle imperative slightly interrupts the flow.

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?

With annotations, a rich fully-documented schema, and an output schema, the description need not explain return values. It covers purpose, prerequisite, and data-fidelity behavior, leaving only sibling differentiation as a gap.

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% with all nine parameters documented and four enums, so the schema does the heavy lifting. The description reinforces that account, column, annex, period, and entity fields are preserved but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Retrieve) and resource (official SICONFI DCA, RREO, RGF, or entity-registry selection), which lets an agent identify the domain. It does not clearly distinguish this tool from the sibling get_siconfi_schema, so it falls short of a 5.

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?

'First inspect the schema' hints at the get_siconfi_schema/get_bcb_schema-style prerequisite pattern, which is useful implied guidance. However it never states when to prefer this over search_official_data or describe_official_dataset, nor any exclusions.

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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