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Glama

Ine Pt

by io.github.pipeworx-io

Indicator Meta

indicator_meta
Read-onlyIdempotent

Metadata for an INE (Statistics Portugal) indicator: title, periodicity, unit, time range, and the full list of dimensions with their valid dimension-value codes. Call this BEFORE get_indicator so you know which Dim1/Dim2/... codes are valid. Requires the indicator code (varcd) — INE has no keyword/search endpoint, so look the code up on https://www.ine.pt first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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: +[
+  {
+    "lang": "EN",
+    "varcd": "0008159"
+  },
+  {
+    "lang": "PT",
+    "varcd": "0008159"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate a safe, idempotent read operation. Description adds specific behavioral details: what data is returned (title, periodicity, unit, time range, full dimension-value list) and the intended usage sequence.

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 succinct sentences plus a brief note. No wasted words; each sentence adds crucial information: purpose, usage order, and parameter sourcing.

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?

Despite lacking an output schema, the description adequately conveys what the tool returns, making it functional. Could optionally include response format, but not critical.

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 covers both parameters with descriptions and enum. Description reinforces importance of varcd and how to obtain it, adding value beyond the 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?

The description clearly states it returns metadata for an INE indicator, listing specific fields (title, periodicity, unit, time range, dimensions with codes). It also distinguishes from the sibling get_indicator by advising to call it first.

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 instructs to call this before get_indicator to obtain valid dimension codes. Provides external lookup instruction for the required varcd parameter, acknowledging INE's lack of a search API.

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