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AI Lifeline (read-only observed values)

get_series

Return one series by its permanent id ('//', e.g. 'jp/fiscal/jgb_yield') with the latest published value, unit, observed_at (observation date), value_status (final/provisional), the primary source (name, source_url, license, attribution), update frequency, last confirmed date, staleness judgement from health.json, limitations, page and API URLs, and checked_at (generation time of the site files). Values are the ones published by the site; nothing is recomputed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
series_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it does disclose useful behavior: values are as published by the site and 'nothing is recomputed', plus it surfaces staleness judgement and checked_at generation time so the agent can judge data freshness. It omits error/not-found behavior and any auth or rate-limit notes, which keeps it short of a 5.

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 verb, resource, and key format, and the trailing sentence about data provenance is short and earns its place. The middle field enumeration is a dense run-on list, but every item describes a real return attribute rather than padding.

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 no output schema, the description must describe the return, and it does so thoroughly (value, unit, observed_at, value_status, source/license/attribution, update frequency, staleness, URLs, checked_at). The remaining gap is failure behavior for an unknown or malformed id, which an agent would benefit from knowing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% – the schema only carries a bare 'Series Id' title – so the description must compensate entirely. It provides the full composite key format '<region>/<chapter>/<series_key>', a concrete example, and flags the id as permanent, which is exactly what an agent needs to construct a valid argument.

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 and resource ('Return one series by its permanent id') and pins the scope to a single series, which cleanly separates it from list_series and search_series. The inline ID format and example ('jp/fiscal/jgb_yield') make the purpose unambiguous without opening the schema.

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?

The phrase 'by its permanent id' implies this is the lookup-by-known-id path, but the description never states when to use it versus search_series (find the id) or list_series (enumerate). No exclusions, prerequisites, or alternative routing are given.

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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