Skip to main content
Glama

Dbnomics

by io.github.pipeworx-io

List Datasets

list_datasets
Read-onlyIdempotent

DBnomics economic time-series aggregator — enumerate the datasets published by one DBnomics provider code (ECB, BLS, EUROSTAT, IMF, OECD, WB and 80+ national statistics agencies). Returns each dataset's code, name and dimension metadata, paged with limit/offset; those codes are what get_series and find_series expect. Answers which statistical datasets the ECB, IMF or Eurostat publish on DBnomics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo1-1000 (default 100)
offsetNo0-based offset
providerYesProvider code (e.g. "ECB", "BLS", "EUROSTAT")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoLimit applied
offsetNoOffset applied
datasetsNoAvailable datasets for provider
total_countNoTotal dataset count

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "provider": "ECB"
+  },
+  {
+    "limit": 50,
+    "provider": "BLS"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "datasets": {
+      "description": "Available datasets for provider",
+      "items": {
+        "properties": {
+          "code": {
+            "description": "Dataset code",
+            "type": "string"
+          },
+          "last_update": {
+            "description": "Last update timestamp",
+            "type": "string"
+          },
+          "name": {
+            "description": "Dataset name",
+            "type": "string"
+          },
+          "series_count": {
+            "description": "Number of series",
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "limit": {
+      "description": "Limit applied",
+      "type": "number"
+    },
+    "offset": {
+      "description": "Offset applied",
+      "type": "number"
+    },
+    "total_count": {
+      "description": "Total dataset count",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints, so the description adds meaningful behavioral context: pagination via limit/offset, the return of code/name/dimension metadata, and the single-provider scoping. It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, with the first verb ('enumerate') front-loaded. The opening phrase 'DBnomics economic time-series aggregator' is slightly verbose but provides useful context. Every sentence contributes to purpose or usage, so it earns its place.

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?

Given the schema and annotations, the description is complete for a list tool: it covers pagination, return contents, and integration with sibling tools. The output schema exists, so detailed return structure is not needed in the description. No significant gaps remain.

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 description coverage is 100%, with all three parameters described and example values provided. The description does not significantly add meaning beyond the schema, merely reinforcing that provider is a DBnomics code, which is already in schema examples.

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 the tool enumerates datasets for a specific DBnomics provider code, using a specific verb and resource. It distinguishes from siblings by mentioning that returned codes are what get_series and find_series expect, and provides concrete provider examples.

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?

The description provides clear context for when to use the tool (to list datasets for a provider) and even indicates downstream usage ('those codes are what get_series and find_series expect'). However, it lacks an explicit 'do not use when' or comparison with list_providers, so it's not fully explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Dbnomics toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, while bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. The detailed descriptions mitigate some confusion, but an agent must read carefully to avoid selecting the wrong member of these overlapping groups.

Naming Consistency3/5

The set is consistently snake_case and many tools follow verb_noun conventions like list_datasets, get_series, find_series, and validate_claim. However, a large minority are noun-led names such as entity_profile, deep_research, bet_research, pipeworx_feedback, and polymarket_arbitrage, so there is no single predictable naming pattern across the whole server.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for an over-heavy surface, and the set spans DBnomics data, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI-visibility checks. Several near-duplicate meta-tools could be consolidated, and unrelated domains would be better split into separate servers.

Completeness4/5

Core workflows are well covered: DBnomics browse/fetch/search, company resolve/profile/compare/change, prediction-market discovery and fill-risk, and memory/subscription lifecycles are all represented. Minor gaps exist, such as no subscription-update operation and no direct single-dataset detail fetch without listing, but agents can work around them.

Resources