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Bundesbank De

by io.github.pipeworx-io

List Dataflows

list_dataflows
Read-onlyIdempotent

List the available Bundesbank statistics dataflows (each is a series collection you can query with get_series). Returns SDMX 2.1 structure XML listing every dataflow id + name under agency BBK. Well-known flows: BBEX3 (exchange rates), BBK01 (legacy single time series), BBSIS (interest rates), BBBK1/BBBK2 (banking statistics), BBDB1 (balance of payments). Pass a flowRef to fetch just that one dataflow (includes its DataStructure reference).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowRefNoOptional. A single dataflow id (e.g. "BBEX3") to fetch instead of 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: +[
+  {},
+  {
+    "flowRef": "BBEX3"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
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. The description adds value by specifying the return format (SDMX 2.1 structure XML), the agency scope (BBK), and the behavior when flowRef is provided. This goes beyond what annotations convey.

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?

The description is three sentences, front-loaded with the primary purpose, and each sentence provides distinct value: what the tool lists, the output format and scope, and how to narrow the result. No wasted words or repetition.

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?

For a read-only listing tool with one optional parameter and no output schema, the description covers the output type, the list scope (all under BBK), the meaning of a dataflow, common identifiers, and the effect of the flowRef parameter. This is sufficiently complete for an agent to use the tool effectively.

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

Parameters4/5

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

Schema coverage is 100% for the single parameter flowRef, with a clear description in the schema. The tool description adds extra nuance: 'includes its DataStructure reference' when a flowRef is passed, which is not stated in the schema. This meaningful addition justifies a score above 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 the tool's purpose: 'List the available Bundesbank statistics dataflows' with specific context that each dataflow is a series collection queryable via get_series. It distinguishes itself by mentioning the operation is listing, and the inclusion of well-known flow IDs adds specificity.

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?

It explicitly connects to get_series as the way to query data inside a dataflow, and explains that passing a flowRef fetches a single dataflow with its DataStructure reference. While it doesn't state explicit exclusions or mention the sibling dataflow_structure tool, the context is clear enough for an agent to decide when to use this tool.

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
Disambiguation2/5

There are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same broad lookup pipeline, and the six polymarket tools cover heavily overlapping edge/arbitrage territory. The detailed descriptions help, but an agent would need to read very carefully to avoid misselecting, especially since ask_pipeworx_beta is currently identical to ask_pipeworx.

Naming Consistency3/5

Most names are snake_case and many follow verb_noun (list_dataflows, get_series, validate_claim, resolve_entity), but there are notable exceptions: brand-prefixed nouns (ask_pipeworx, pipeworx_trending, polymarket_arbitrage), bare verbs (forget, recall, remember), and noun-style names (entity_profile, dataflow_structure). The pattern is readable but not consistently applied.

Tool Count2/5

Thirty-four tools is well into the heavy range for a server named Bundesbank De, and only three of them (list_dataflows, dataflow_structure, get_series) are actually Bundesbank-specific. The rest are generic Pipeworx data, prediction-market, memory, subscription, and meta tools that look bolted on rather than part of a scoped Bundesbank server.

Completeness4/5

For the Bundesbank data-access core, the trio of list_dataflows, dataflow_structure, and get_series provides a workable discovery-to-retrieval path with no dead end. Minor gaps exist (no keyword search across series, no bulk/export option), but an agent can work around them; the bigger problem is scope pollution rather than a missing operation.

Resources