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Glama

Data360

by io.github.pipeworx-io

Data360 Get Data

data360_get_data
Read-onlyIdempotent

Fetch observations for one Data360 series. DATABASE_ID selects the source database (e.g. WB_WDI), INDICATOR is the code from data360_search_indicators (e.g. WB_WDI_SP_POP_TOTL), REF_AREA is an ISO3 country code (e.g. BRA, USA). Returns SDMX-style records with OBS_VALUE, TIME_PERIOD, UNIT_MEASURE and disaggregation attributes (SEX, AGE, etc.). Omit TIME_PERIOD for the full series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoMax observations to return (default 100).
skipNoOffset for pagination (default 0).
REF_AREAYesISO3 country/region code, e.g. "BRA", "USA", "WLD".
INDICATORYesIndicator code, e.g. "WB_WDI_SP_POP_TOTL".
DATABASE_IDYesSource database ID, e.g. "WB_WDI".
TIME_PERIODNoOptional single year, e.g. "2020".

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: +[
+  {
+    "DATABASE_ID": "WB_WDI",
+    "INDICATOR": "WB_WDI_SP_POP_TOTL",
+    "REF_AREA": "USA"
+  },
+  {
+    "DATABASE_ID": "WB_WDI",
+    "INDICATOR": "WB_WDI_NY_GDP_PCAP_CD",
+    "REF_AREA": "BRA",
+    "TIME_PERIOD": "2020"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details: returns SDMX-style records with specific attributes and explains that omitting TIME_PERIOD returns the full series. 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 with no wasted words. First sentence states purpose, second explains parameters, third covers output and optional behavior. 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 purpose, parameter roles, output structure, and cross-reference to sibling. No output schema is present, but the description explains return fields. Could mention pagination (top/skip) but those are in schema. Overall adequate given complexity.

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 coverage is 100%, but the description adds rich meaning: explains DATABASE_ID selects source, INDICATOR is a code from a sibling tool, REF_AREA is ISO3, and TIME_PERIOD is optional. Examples illustrate usage, fully compensating for schema's baseline.

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 'Fetch observations for one Data360 series', specifying the verb and resource. It distinguishes from sibling tools like data360_list_databases and data360_search_indicators by focusing on fetching observations for a single 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?

Provides clear parameter guidance, including cross-referencing data360_search_indicators for INDICATOR codes and explaining optional TIME_PERIOD. However, it does not explicitly state when not to use this tool or alternative tools for multiple series.

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

A3.9/5.0
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near variants (beta currently identical), and the prediction-market tools share adjacent territory. The descriptions do delineate most use cases, but an agent could easily confuse the ask_pipeworx variants or pick between polymarket_edges and bet_research.

Naming Consistency2/5

Naming is a mix of domain-prefixed verbs (data360_get_data, pipeworx_feedback), bare verbs (forget, recall, subscribe), and noun phrases (entity_profile, recent_changes, polymarket_edges). The ask_pipeworx family is consistent, but there is no server-wide verb_noun convention and tool names are not predictable from their function.

Tool Count2/5

34 tools is too many for a well-scoped server, and the set spans unrelated areas: data retrieval, prediction markets, memory, subscriptions, npm dependency scanning, and llms.txt generation. While each tool has a purpose, the overall surface feels sprawling rather than focused.

Completeness4/5

The core data/research workflows are well covered: ask/grounded/deep research, entity identity and profiles, comparisons, claim validation, and subscription lifecycle management are all present. Minor gaps exist, such as no explicit raw-record fetch tool and some auxiliary features appearing as one-off utilities, but no major dead ends are apparent.

Resources