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Glama

Ine Pt

by io.github.pipeworx-io

Get Indicator

get_indicator
Read-onlyIdempotent

Fetch data values for an INE (Statistics Portugal) indicator. Pass the indicator code (varcd) and optionally a dims object mapping dimension slots ("Dim1","Dim2",...) to dimension-value codes to select a slice (codes come from indicator_meta). Omit a Dim slot to return all of its values. Requires varcd — INE has no keyword/search endpoint, so look the code up on https://www.ine.pt first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dimsNoDimension selections, e.g. {"Dim1":"S7A2021","Dim3":"1"}. Keys are Dim1..DimN (matching the dim_num from indicator_meta); values are dimension-value codes. May also be passed as an array, treated as [Dim1, Dim2, ...].
langNoResponse language. Default EN.
varcdYesINE indicator code (varcd), e.g. "0008273". INE has no keyword search API — find the varcd on https://www.ine.pt (BDDXplorer database browser) first.

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: +[
+  {
+    "varcd": "0008159"
+  },
+  {
+    "dims": {
+      "Dim1": "S7A2021",
+      "Dim3": "1"
+    },
+    "lang": "EN",
+    "varcd": "0008159"
+  }
+]
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive. Description adds behavioral details: no keyword search endpoint, external code lookup needed, and behavior when dims slots are omitted (returns all values).

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 packed with essential information. First sentence states purpose, second provides detailed usage. No unnecessary words, front-loaded.

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?

Fully covers all needed information for an agent: how to get indicator code, how to use dims, language parameter, and links to metadata. No gaps despite lack of output schema. Complete and self-contained.

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 has 100% description coverage, but description adds context: explains dims as dimension slot mapping, notes dims can be array, and explains varcd requires external lookup. Adds significant meaning beyond schema.

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?

Clearly states the verb 'Fetch' and resource 'data values for an INE indicator'. Distinguishes from sibling tool indicator_meta by noting that dimension codes come from there.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains when to use (fetch data by indicator code) and when not (no keyword search, need external lookup). Gives guidance on optional parameters and the external dependency on www.ine.pt.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions. However, a few tools like 'discover_tools' and 'suggest_questions' both serve exploratory functions and could cause confusion. Similarly, 'ask_pipeworx' and 'deep_research' overlap in scope but are differentiated by depth and account requirements. Overall, an agent can typically pick the right tool, but a few pairs require careful reading.

Naming Consistency3/5

All names use snake_case and are generally readable, but the convention varies: some are verb_noun (e.g., 'resolve_entity'), some are noun_noun (e.g., 'entity_profile'), and a few are just verbs (e.g., 'remember', 'forget'). The 'polymarket_' prefix helps group related tools, but the diversity in patterns slightly reduces predictability.

Tool Count4/5

With 32 tools, the set is slightly large but justified by the wide range of functionality: data querying, prediction markets, memory, subscriptions, and utilities. Each tool serves a distinct purpose, and the count is not excessive given the server's role as a gateway to thousands of data sources. It feels well-scoped for its domain.

Completeness4/5

The tool set covers most essential operations: querying data, entity profiles, comparisons, subscriptions, memory, and onboarding. Minor gaps exist, such as the lack of a generic subscription for all data changes or a way to list all available data packs directly. However, 'discover_tools' partially addresses this. Overall, the surface is comprehensive for the server's purpose.

Resources