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Glama

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).

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: +[
+  {
+    "top": 200
+  },
+  {
+    "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 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

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