El Tablero: Argentina economic data

Get a statistical projection

get_projection

Read-onlyIdempotent

Project a series forward: point values with 80% and 95% bands, the models combined (ETS, ARIMA, Theta, chosen by walk-forward backtest), backtest errors and, when the series has one, the BCRA market-expectations survey (REM) median for the same months. Use it for "what's next" questions about inflation, exchange rates, interest rates or reserves; use get_series for past values. It is a statistical reference, not a forecast or advice: say so when you quote it. Needs a key from search_series. Series with too little history return an error. Read-only, no side effects, no authentication. Rate limited to 60 calls per minute per IP. Data updates at most once per business day.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`key`	Yes	Series key as returned by search_series, e.g. "bcra:27".
`horizon`	No	How far ahead: calendar days for daily series (default 30, up to 183), periods for the rest (default 12 months, 8 quarters).

Output Schema

TableJSON Schema

Name	Required	Description
`key`	Yes
`name`	Yes
`unit`	Yes
`bands`	Yes
`engine`	Yes	lote: daily batch models; sitio: computed on request
`method`	Yes
`horizon`	Yes
`members`	No
`backtest`	Yes
`benchmark`	Yes
`consensus`	No
`disclaimer`	Yes
`attribution`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 5, 2026

addedOutput schema / properties / backtest / properties / mae_by_step

Added value: +{
+  "description": "Mean absolute error at each step ahead (index 0 = one step)",
+  "items": {
+    "type": "number"
+  },
+  "type": "array"
+}

addedOutput schema / properties / backtest / properties / origins

Added value: +{
+  "additionalProperties": false,
+  "description": "Last known data point at the first and last backtest origin",
+  "properties": {
+    "from": {
+      "type": "string"
+    },
+    "to": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "from",
+    "to"
+  ],
+  "type": "object"
+}

addedOutput schema / properties / consensus / properties / track_record

Added value: +{
+  "additionalProperties": false,
+  "description": "Past mean absolute error of the survey and of this model on the same forecast origins and target months, by months ahead of the last published data point (index 0 = one month)",
+  "properties": {
+    "consensus_mae": {
+      "items": {
+        "type": "number"
+      },
+      "type": "array"
+    },
+    "model_mae": {
+      "items": {
+        "type": "number"
+      },
+      "type": "array"
+    },
+    "note": {
+      "type": "string"
+    },
+    "origins": {
+      "additionalProperties": false,
+      "properties": {
+        "from": {
+          "type": "string"
+        },
+        "to": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "from",
+        "to"
+      ],
+      "type": "object"
+    },
+    "samples": {
+      "items": {
+        "type": "number"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "consensus_mae",
+    "model_mae",
+    "samples",
+    "origins",
+    "note"
+  ],
+  "type": "object"
+}

First observedSep 29, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, but the description adds substantial context they don't carry: 60 calls/min per IP rate limit, no authentication, once-per-business-day data refresh, error behavior on short series, and a quoting/disclaimer obligation ('statistical reference, not a forecast or advice'). That is unusually rich 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.

Conciseness4/5

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

Front-loaded with what the tool returns, then routing, then caveats and operational constraints. Dense but every clause carries information; the single long paragraph is slightly heavy and could be broken up, costing the top score.

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?

Output schema exists, yet the description still conveys return composition and the caveat that it is a statistical reference rather than advice. Combined with rate limits, refresh cadence, and error behavior, an agent has everything needed to invoke and present results responsibly.

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 both parameters (key, horizon) are fully documented in the schema with formats, bounds, and defaults. The description adds no additional parameter syntax or unit guidance beyond what the schema provides, so the baseline 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?

States a specific verb (project) and resource (a series) and enumerates the output content: point values, 80%/95% bands, combined models (ETS, ARIMA, Theta), backtest errors, and the REM median. It explicitly distinguishes itself from get_series ('use get_series for past values'), so an agent can route without opening schemas.

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 when-to-use ('what's next' questions about inflation, exchange rates, interest rates, reserves), an explicit alternative (get_series for past values), and a stated prerequisite (needs a key from search_series). Also notes failure conditions ('series with too little history return an error').

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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