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Data360

by io.github.pipeworx-io

Data360 List Databases

data360_list_databases
Read-onlyIdempotent

List the source databases aggregated by World Bank Data360 (e.g. WB_WDI = World Development Indicators, IMF_BOP = Balance of Payments, WB_EDSTATS = Education Statistics). Returns each DATABASE_ID with its full name and indicator count. Use a DATABASE_ID here to scope data360_search_indicators and data360_get_data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoMax databases to return (default 200, the full list).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about return format (DATABASE_ID, full name, indicator count) and default pagination (top=200). 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?

Two sentences: first covers purpose and examples, second covers return format and downstream usage. No unnecessary words, information is front-loaded and structured efficiently.

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?

Given the tool's simplicity (listing databases), no output schema is needed. The description fully explains what the tool does, what it returns, and how the output is used, making it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a description for the single 'top' parameter. The description does not add any extra semantic information about the parameter beyond what the schema provides.

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 it lists source databases aggregated by World Bank Data360, provides concrete examples of database IDs (WB_WDI, IMF_BOP) and explains what is returned (DATABASE_ID, full name, indicator count). Distinguishes from siblings by specifying that the returned DATABASE_ID can be used to scope other tools.

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 that the tool provides DATABASE_IDs for use with data360_search_indicators and data360_get_data, guiding the agent on when to use it. Does not explicitly state when not to use it or mention alternatives, but the context is clear.

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.

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