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Bundesbank De

by io.github.pipeworx-io
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Get Series

get_series
Read-onlyIdempotent

Pull observations for a Bundesbank series as SDMX-JSON. flowRef is the dataflow id (e.g. "BBEX3"); key is a dot-separated SDMX dimension filter in key order (e.g. "D.USD.EUR.BB.AC.000" = daily USD/EUR reference rate). Use dataflow_structure to discover the dimensions/codes for a flow. Leave a dimension empty to wildcard it (e.g. "D..EUR.BB.AC.000"). Filter by lastNObservations (most recent N) or a startPeriod/endPeriod date range (YYYY, YYYY-MM, or YYYY-MM-DD).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesDot-separated SDMX dimension filter, e.g. "D.USD.EUR.BB.AC.000". Empty segments wildcard a dimension.
detailNoSDMX detail level: "full" (default), "dataonly", "serieskeysonly", or "nodata".
flowRefYesDataflow id, e.g. "BBEX3".
endPeriodNoInclusive end period (YYYY, YYYY-MM, or YYYY-MM-DD).
startPeriodNoInclusive start period (YYYY, YYYY-MM, or YYYY-MM-DD).
lastNObservationsNoReturn only the most recent N observations.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "flowRef": "BBEX3",
+    "key": "D.USD.EUR.BB.AC.000",
+    "lastNObservations": 10
+  },
+  {
+    "endPeriod": "2023-12-31",
+    "flowRef": "BBSIS",
+    "key": "M..EUR.DE.A.R....",
+    "startPeriod": "2020-01-01"
+  }
+]
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description only needs to supplement behavioral details. It adds key semantics, wildcarding ('Leave a dimension empty to wildcard it'), output format (SDMX-JSON), and date formats, which go beyond the schema. This is a rich addition.

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 four sentences, front-loaded with the main purpose, and each sentence adds a distinct piece of practical guidance (format, key format, discovery tool, filtering). No wasted words.

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 6-parameter tool with no output schema, the description covers the core concepts: data source, key construction, wildcards, and filter options. It also references the sibling discovery tool, making it self-contained. The only omission is the 'detail' parameter, but that is fully covered by the schema, so completeness is high.

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

Parameters5/5

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

Schema coverage is 100% with property descriptions, but the description adds contextual examples: flowRef example 'BBEX3', key example 'D.USD.EUR.BB.AC.000' with explanation, and wildcard syntax. This clarifies the abstract schema definitions, raising the value beyond 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 opens with a clear verb+resource statement: 'Pull observations for a Bundesbank series as SDMX-JSON.' This distinguishes it from sibling tools like dataflow_structure or list_dataflows, which serve different discovery/list purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs to 'Use dataflow_structure to discover the dimensions/codes for a flow,' pointing to an alternative when users lack dimension codes. It also clarifies when to use filtering parameters (lastNObservations vs date range), giving practical examples.

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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