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Data360

by io.github.pipeworx-io

Data360 Search Indicators

data360_search_indicators
Read-onlyIdempotent

Full-text search across all Data360 indicators (every WDI/IMF/WHO/ILO/education series in one index). Returns indicator codes (use as INDICATOR in data360_get_data), names, owning DATABASE_ID/name, units, and definitions. Optionally restrict to one database with database_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoMax results (default 20).
skipNoOffset for pagination (default 0).
queryYesKeyword(s), e.g. "GDP per capita" or "maternal mortality". Use "*" for all.
database_idNoOptional: restrict to one source database, e.g. "WB_WDI" or "IMF_BOP" (from data360_list_databases).

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: +[
+  {
+    "query": "GDP per capita"
+  },
+  {
+    "database_id": "WB_WDI",
+    "query": "maternal mortality",
+    "top": 10
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds value by detailing indexed sources and returned fields (indicator codes, names, database info, units, definitions). 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, front-loaded with core functionality. Every sentence is informative with no redundancy. Ideal conciseness.

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?

No output schema, but description lists all returned fields (indicator codes, names, database info, units, definitions). All parameters are documented in schema; description adds inter-tool guidance. Complete for a search tool.

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 covers all 4 parameters with descriptions (100% coverage). Description adds minor context by relating parameters to usage (e.g., 'use as INDICATOR in data360_get_data', 'restrict to one database'). Baseline of 3 appropriate.

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 the tool performs full-text search across all Data360 indicators, listing specific data sources (WDI/IMF/WHO/ILO/education). It distinguishes itself from sibling tools like data360_get_data and data360_list_databases by specifying it returns indicator codes for use in data360_get_data.

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?

Implies usage: search indicators before getting data with data360_get_data. Mentions optional database_id restriction. Lacks explicit when-not-to-use or alternatives, but context from sibling tools makes it 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

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