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Glama

Bpstat Pt

by io.github.pipeworx-io

Get Series Metadata

get_series_metadata
Read-onlyIdempotent

Look up metadata for one or more series by numeric series id. Returns label, short_label, description (Portuguese by default), dataset_id, domain_ids, and dimension_category. Use this to identify a series and find which dataset_id holds its observations, then call get_dataset. NOTE: this endpoint returns metadata only — it does NOT return the numeric values. lang defaults to PT.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage: "PT" (default) or "EN".
series_idsYesOne or more numeric series ids, comma-separated, e.g. "8201" or "8201,8202".

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",
+    "series_ids": "8201"
+  },
+  {
+    "lang": "PT",
+    "series_ids": "8201,8202,8203"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. Description adds value by confirming that 'metadata only' means no numeric values, and specifies fields returned and language behavior. 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?

Three sentences, no waste. Front-loaded with main purpose, followed by returned fields, then a clear note about behavior. Every sentence contributes.

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?

With only 2 parameters, no output schema, and annotations providing safety profile, the description fully covers what the tool does, what it returns, and how to use it in the broader workflow.

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. Description adds meaning by stating series_ids are 'numeric' and comma-separated, and lang defaults to PT. The schema examples reinforce this.

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?

Description uses specific verb 'Look up metadata' and resource 'series by numeric series id'. It lists returned fields and distinguishes from sibling tools by stating it does not return numeric values, clarifying its role relative to get_dataset.

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?

Explicitly states when to use the tool ('identify a series and find which dataset_id holds its observations, then call get_dataset') and mentions language default. However, it does not explicitly exclude cases or name alternatives.

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
Disambiguation2/5

Multiple tool clusters are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly documented as currently identical, scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and the five polymarket_* tools (edges, edge_tracker, arbitrage, fill_risk, kalshi_spread) share heavily overlapping edge-detection/fill-analysis concerns. entity_profile and recent_changes also both fan out to the same SEC/news/patents sources, so an agent must read full descriptions to avoid misselection.

Naming Consistency3/5

The dominant pattern is imperative verb_first (list_datasets, get_dataset, resolve_entity, validate_claim, scan_dependency, subscribe), but there are notable deviations: noun-phrase names like entity_profile, recent_alerts, recent_changes, bet_research, and deep_research; the ask_pipeworx brand family sits awkwardly beside get_/search_ verbs; and the polymarket_* prefix family forms yet another convention. Names are readable and mostly self-explanatory, but no single consistent scheme is followed.

Tool Count2/5

At 35 tools, this exceeds the 'too many (25+)' threshold, and the bloat is compounded by the server's nominal identity: despite being named 'Bpstat Pt' (Banco de Portugal statistics), only about 5 tools (list_domains, list_datasets, get_dataset, get_series_metadata) actually serve that domain. The other 30 tools span unrelated subsystems — Polymarket betting, npm dependency scanning, AI visibility audits, generic memory, and subscriptions — suggesting several products bundled into one server.

Completeness3/5

For the broader data-gateway interpretation, the lifecycle is well covered: discovery (discover_tools, suggest_questions), query (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), profiles/comparisons (entity_profile, compare_entities), monitoring (recent_changes, subscribe/recent_alerts), and memory (remember/recall/forget). However, for the nominal Bpstat statistical domain there is no keyword search over series or datasets — navigation requires knowing domain/dataset ids in advance — and the extreme scatter across unrelated domains leaves each subsystem only partially developed.

Resources