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Ine Es

by io.github.pipeworx-io
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Series Data

series_data
Read-onlyIdempotent

Fetch data for ONE INE series by its code. Use nult=N for the last N periods, or date="YYYYMMDD:" for from that date on. Returns the series with its Data[] array of {Fecha (epoch ms), Anyo, Valor} points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate filter, e.g. "20250101:" for from Jan 2025 onward.
langNoResponse language. Default EN.
nultNoReturn the last N periods (default 12 if no date given).
seriesCodeYesSeries code (COD), e.g. "IPC251852".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "seriesCode": "IPC251852"
+  },
+  {
+    "lang": "ES",
+    "nult": 24,
+    "seriesCode": "IPC251852"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds return structure details (Data array with fields) and explains behavioral difference between nult and date parameters, adding value beyond annotations.

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 cover purpose, key parameters, and return format. No redundant or unnecessary text. Efficiently front-loaded.

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?

No output schema, but description fully explains return value structure (Data array with fields). With 4 parameters all described, and no hidden behaviors, the description provides complete context for an AI agent to use the tool correctly.

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 baseline 3. Description adds context for nult and date parameters with usage examples, beyond schema descriptions. Explains return format which ties to parameters.

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?

Description clearly states 'Fetch data for ONE INE series by its code.' Specific verb (Fetch data), resource (INE series), and constraint (ONE) differentiate from sibling tools like series_in_table.

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?

Explicitly explains use of nult and date parameters for filtering, but does not explicitly state when to use vs. alternatives. Implicit differentiation from sibling tools is present but not confirmed.

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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