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Bundesbank De

by io.github.pipeworx-io

Dataflow Structure

dataflow_structure
Read-onlyIdempotent

Get the data structure definition (DSD) for a dataflow: its dimensions and the valid codes for each, which you need to build a series key for get_series. Returns SDMX 2.1 structure XML. The DSD id differs from the dataflow id (e.g. dataflow BBEX3 uses DSD "BBK_ERX"). Pass the dataflow id (flowRef) and this tool resolves the DSD for you; the dimensions appear in in key order. Set withCodes=true (default) to inline the codelists (references=children).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowRefYesDataflow id whose structure you want, e.g. "BBEX3".
withCodesNoInclude the referenced codelists (valid dimension values). Default true.

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"
+  },
+  {
+    "flowRef": "BBSIS",
+    "withCodes": true
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial value beyond the readOnly/idempotent annotations: it states the return is SDMX 2.1 XML, explains that the DSD id may differ from the dataflow id, notes the dimension order in DimensionList, and explains the withCodes default behavior. This gives the agent a clear picture of what to expect.

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 efficiently structured: it opens with the core purpose, then gives return format, then the DSD-id caveat, then parameter guidance. Every sentence adds relevant information without padding 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?

With no output schema, the description adequately covers return format (SDMX 2.1 XML, DimensionList), the relationship between dataflow and DSD ids, and parameter behavior. It is sufficiently complete for an agent to select and invoke the tool correctly without additional context.

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% and the schema already documents both parameters. The description adds extra context by explaining that flowRef is a dataflow id that gets resolved to a DSD, and clarifies the withCodes default and its effect on codelist inclusion, which goes beyond the schema 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 gets the data structure definition (DSD) for a dataflow, including dimensions and valid codes, and explicitly ties this to building a series key for get_series. This specific verb+resource distinguishes it from sibling tools like list_dataflows or get_series.

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 provides a clear use case (needed for get_series) and explains how to invoke the tool with a flowRef. However, it does not explicitly mention alternatives or when not to use it, so it lacks the explicit exclusion guidance that would earn a 5.

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