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Glama

Cbs Il

by io.github.pipeworx-io
OverviewAdmin

Get Series Data

get_series_data
Read-onlyIdempotent

Fetch the actual time-series observations for a CBS series by its numeric series id (the leaf id found via catalog_browse). Returns metadata (subject path, unit, prices, last update) plus an array of observations with TimePeriod (e.g. "2026-04") and Value. Covers Israeli economic and demographic statistics. Optionally filter by start_period / end_period (YYYY-MM or YYYY).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoContent language. Default "en".
series_idYesNumeric series id, e.g. 1 (foreign-trade deficit).
end_periodNoLatest period, e.g. "2026-06".
start_periodNoEarliest period, e.g. "2025-01" or "2024".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "series_id": 1
+  },
+  {
+    "end_period": "2026-06",
+    "lang": "en",
+    "series_id": 1,
+    "start_period": "2025-01"
+  }
+]
  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 declare readOnlyHint, idempotentHint, etc. The description adds useful behavioral context: returns metadata (subject path, unit, prices, last update) and observations array with TimePeriod and Value, plus coverage of Israeli statistics. No contradictions.

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 that are front-loaded with main purpose and return structure, followed by context and optional filters. Every word earns its place with no redundancy.

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?

Despite no output schema, the description thoroughly explains return value (metadata + array of observations with fields). It covers all 4 parameters, the source domain (Israeli statistics), and optional filtering. Complete for a tool of this complexity.

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 is 3. The description adds value beyond schema: explains series_id as 'leaf id found via catalog_browse', provides format for periods, and clarifies lang default. This enriches parameter understanding.

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 clearly states 'Fetch the actual time-series observations for a CBS series by its numeric series id', which is specific and distinguishes from sibling like catalog_browse (used to find the leaf id).

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?

The description specifies that the series id is obtained via catalog_browse, implying a prerequisite usage pattern. It also mentions optional start_period/end_period filters, but does not explicitly state when not to use or alternatives beyond catalog_browse.

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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