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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".

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

A4/5.0
Disambiguation3/5

Multiple tools answer factual questions (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, validate_claim, deep_research), and ask_pipeworx_beta is explicitly identical to the stable router right now, creating genuine selection ambiguity. Most other clusters—memory, subscriptions, entity research, Polymarket—are reasonably distinct once the verbose descriptions are read.

Naming Consistency4/5

Names are consistently lowercase snake_case with recognizable family prefixes (ask_pipeworx_*, data360_*, polymarket_*, pipeworx_*), which aids grouping. The convention mixes verb-first names like resolve_entity with noun/prefix names like polymarket_edges, but it is still readable and predictable enough.

Tool Count2/5

34 tools is well past the heavy range, and the server bundles several unrelated concerns—universal data routing, prediction-market analytics, memory, subscriptions, AI-visibility marketing, and npm dependency scanning—into one surface. Many tools earn their place, but the aggregate is overloaded and likely to slow tool-selection.

Completeness4/5

The data-research side has strong coverage: discovery, universal routing, grounded verification, entity resolution, profiles, comparisons, recent-changes tracking, and in-record search. Subscription lifecycle and memory are complete, and the prediction-market suite even covers fill-risk and edge persistence; only a few niche read/write operations are absent.

Resources