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Glama

Datos Ar

by io.github.pipeworx-io

Get Series

get_series
Read-onlyIdempotent

Fetch observations for one or more Argentine time series by id (ids come from search_series). Data is official Argentine statistics; titles are in Spanish. Pass multiple comma-separated ids to align several series on the same dates. Use collapse to resample (e.g. monthly→yearly avg) and representation_mode to transform values (e.g. percent change vs a year ago). Returns rows of [date, value, ...] plus metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesOne series id, or several comma-separated, e.g. "143.3_NO_PR_2004_A_21". From search_series.
limitNoMax rows to return (default 100, API max 1000).
collapseNoResample frequency, e.g. "year" to roll monthly data up to yearly.
end_dateNoLatest date, ISO YYYY-MM-DD (optional).
metadataNoMetadata detail level (default simple).
start_dateNoEarliest date, ISO YYYY-MM-DD (optional).
representation_modeNoValue transform: raw value (default), period change, or change vs a year ago.
collapse_aggregationNoHow to aggregate when collapsing (default avg).

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: +[
+  {
+    "ids": "143.3_NO_PR_2004_A_21"
+  },
+  {
+    "collapse": "year",
+    "end_date": "2023-12-31",
+    "ids": "143.3_NO_PR_2004_A_21,145.2_NO_PR_2004_A_15",
+    "representation_mode": "percent_change_a_year_ago",
+    "start_date": "2020-01-01"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and non-destructive nature. Description adds behavioral details: aligning series on same dates, returns rows of [date, value, ...] plus metadata, and transformation options. No contradiction.

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 no wasted words. First sentence states core purpose and source of ids; second sentence details key features and output format. 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?

Covers return format and key behaviors (alignment, resampling, transformation). Mentions Spanish titles for context. Could mention metadata parameter detail but schema covers that. Complete for a data retrieval tool with no output schema.

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% with each parameter described. Description adds value by explaining that ids come from search_series, alignment behavior, and output row format. Moderate extra context beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'Fetch' and resource 'observations for Argentine time series'. Identifies source of ids as 'from search_series', a sibling tool, which provides context. However, it does not explicitly distinguish from other sibling tools beyond this.

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?

Describes key usage: passing comma-separated ids for alignment, and optional parameters like collapse and representation_mode. Lacks explicit when-not-to-use guidance but effectively implies typical use case.

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

A3.8/5.0
Disambiguation3/5

Several tools cluster around the same underlying data router (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the prediction-market family has five overlapping members, so misselection is possible. The descriptions are detailed enough to separate most intents, but ask_pipeworx_beta is explicitly identical to ask_pipeworx right now and ai_visibility_check/scan_competitor_ai_presence are close cousins.

Naming Consistency4/5

All tool names consistently use lowercase snake_case, and most follow a clear verb_noun shape like ask_pipeworx, get_series, subscribe, or validate_claim. A few noun-style names (entity_profile, pipeworx_trending, polymarket_edges) and the bare memory verbs (remember, recall, forget) break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

33 tools is a heavy surface that exceeds the 25-tool threshold where selection cost becomes a real problem for agents. The count is inflated by auxiliary concerns like memory, subscriptions, feedback, trending, and AI-presence scans that sit alongside the core data-access mission.

Completeness4/5

The core data-research workflows are well covered: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, deep_research, get_series), entity resolution (resolve_entity), profiles, comparisons, claim validation, and prediction-market analysis all have end-to-end support. Memory and subscription lifecycles are also complete. Minor gaps exist — some sources soft-fail and there is little Argentina-specific tooling beyond the time-series pair — but agents can generally work around them.

Resources