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Glama

Cbs Il

by io.github.pipeworx-io

Get Index Data

get_index_data
Read-onlyIdempotent

Fetch monthly values for a CBS price index by its numeric code (from index_catalog, e.g. 120010 = CPI General). Returns per-month entries with the index value, the monthly percent change (percent) and the year-over-year change (percentYear) — i.e. Israeli inflation. Optionally filter by start_period / end_period (YYYY-MM or YYYY).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesIndex code, e.g. 120010 (Consumer Price Index - General).
langNoContent language. Default "en".
end_periodNoLatest period, e.g. "2026-06".
start_periodNoEarliest period, e.g. "2025-01".

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: +[
+  {
+    "code": 120010
+  },
+  {
+    "code": 120010,
+    "end_period": "2026-06",
+    "start_period": "2025-01"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing the output shape (per-month entries with specific fields) and the parameter format (YYYY-MM or YYYY), going beyond the annotations. 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 with zero waste. The first sentence states purpose and required parameter; the second details output and optional filters. Front-loaded and efficient.

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?

Given the tool's simplicity (4 parameters, rich annotations, no output schema), the description covers purpose, parameter format, output fields, and source reference. It could be improved by explicitly stating the return type (array of entries) or handling of the lang parameter, but overall is adequate.

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 meaningful context beyond schema: it explains the date format for start_period/end_period, clarifies the example code (120010 = CPI General), and describes the computed fields (percent, percentYear) that the parameters don't directly cover.

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 it fetches monthly values for a CBS price index, specifying the required numeric code from index_catalog and the returned fields (index value, percent change, year-over-year change). It distinguishes itself by referencing index_catalog as a prerequisite and mentioning 'Israeli inflation', which differentiates it from generic index tools.

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 description provides usage context (fetching from CBS, using code from index_catalog, optional period filtering) but does not explicitly compare with siblings like get_series_data or state when to avoid this tool. The guidance is implied but lacks direct alternatives or 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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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the polymarket_edges / polymarket_arbitrage / polymarket_kalshi_spread trio all scan for mispricings with overlapping descriptions. The rich usage notes help, but they cannot fully rescue a set where two tools literally do the same thing right now.

Naming Consistency3/5

The majority of tools use readable snake_case, but conventions are mixed: verb-first names (get_index_data, resolve_entity, validate_claim) sit alongside noun-first names (catalog_browse, index_catalog, entity_profile, bet_research), standalone verbs (remember, forget, subscribe), and adjective-led names (recent_alerts, deep_research). The pattern is predictable within clusters but not uniform across the set.

Tool Count2/5

35 tools is well above the comfortable range, and the set spans many unrelated domains—CBS Israel statistics, Pipeworx data routing, Polymarket betting, memory, subscriptions, npm dependency scanning, and AI visibility checks. Even if each tool has a purpose, the surface is bloated and poorly scoped for a server named 'Cbs Il'.

Completeness4/5

For a general data-access gateway, the set covers the major workflows: routing questions, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, memory, and tool discovery. Minor gaps exist, such as no direct raw-record fetch without routing and no keyword search over the CBS catalog, but agents can work around these.

Resources