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Datos Ar

by io.github.pipeworx-io

Search Series

search_series
Read-onlyIdempotent

Search Argentina's national time-series catalog (apis.datos.gob.ar) for series matching a keyword. Titles/descriptions are in Spanish (e.g. q="inflacion", "pbi", "emae", "tipo de cambio", "desempleo"). Returns matching series with their ids (use these with get_series), titles, units, frequency, date coverage, dataset and source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch keyword in Spanish, e.g. "inflacion", "pbi", "tipo de cambio".
limitNoMax results to return (default 10).
unitsNoOptional units filter, e.g. "Porcentaje".
offsetNoResult offset for pagination (default 0).
dataset_themeNoOptional theme id filter, e.g. "actividad", "precios".

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: +[
+  {
+    "q": "inflacion"
+  },
+  {
+    "dataset_theme": "precios",
+    "limit": 20,
+    "q": "tipo de cambio"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds that it returns specific fields and queries a known endpoint (apis.datos.gob.ar). It does not detail pagination behavior (offset, limit) or mention that openWorldHint means results may change, but the schema covers pagination parameters. No contradiction with 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?

The description is three sentences with no wasted words. It front-loads the main purpose, then provides practical examples, and ends with what is returned. Every sentence adds essential information.

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 is a simple keyword search with one required parameter and no output schema, the description covers input (keyword, optional filters), output (returned fields), and follow-up action (use ids with get_series). It could mention default limit or pagination behavior, but the schema examples help. Overall, it's sufficiently complete for an agent to use 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% (all parameters described). The description adds value with examples (q values like 'inflacion', limit=20, dataset_theme='precios') and clarifies that q should be in Spanish. This goes beyond the schema parameter descriptions to give real-world usage context.

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 the tool searches Argentina's national time-series catalog for series matching a keyword (verb 'Search', resource 'catalog'). It provides example Spanish keywords and specifies return fields (ids, titles, units, frequency, date coverage, dataset, source). It distinguishes from sibling tool get_series by noting that returned ids are used with get_series.

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 explains when to use the tool (to find series by keyword) and gives example queries in Spanish. It implies an indirect alternative (get_series for full data) but lacks explicit when-not-to-use guidance or comparison with other search tools among siblings. However, the context is clear enough for most agents to determine appropriateness.

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