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Glama

Norges Bank

by io.github.pipeworx-io

Get Series

get_series
Read-onlyIdempotent

Generic SDMX data fetch from any Norges Bank flow. key is dot-separated SDMX dimension filters; leave a position empty to wildcard it. Examples: flow_ref="EXR", key="B.USD.NOK.SP" (daily USD→NOK spot); flow_ref="IR" (all interest-rate series). Use list_flows to see available flow references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoSeries key: dot-separated dimension values (empty positions = wildcard). Optional; omit for the whole flow.
last_nNoReturn only the last N observations.
flow_refYesFlow reference — EXR, IR, GOVT_KEYFIGURES, GOVT_GENERIC_RATES.
end_periodNoEnd period (YYYY-MM-DD or YYYY).
start_periodNoStart period (YYYY-MM-DD or YYYY).

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: +[
+  {
+    "flow_ref": "EXR",
+    "key": "B.USD.NOK.SP"
+  },
+  {
+    "flow_ref": "IR",
+    "last_n": 10
+  },
+  {
+    "end_period": "2024",
+    "flow_ref": "GOVT_KEYFIGURES",
+    "start_period": "2023"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context about the key parameter's dot-separated format and wildcard semantics, which goes beyond the structured annotations and helps the agent use the tool correctly.

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 concise and well-structured: it front-loads the primary purpose, explains the key syntax, provides two illustrative examples, and ends with a pointer to a related tool. Every sentence earns its place with no unnecessary filler.

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?

With no output schema, the description could be expected to explain return values, but 'data fetch' implies the data is returned. The description covers the core usage, key construction, and flow discovery, making it sufficient for a generic fetch tool. Slightly incomplete regarding response format, hence a 4.

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%, so the schema already documents all parameters. The description adds examples and clarifies the key format, but the core semantic meaning is redundant with the schema. Baseline 3 is appropriate because the description doesn't significantly add meaning beyond the schema.

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 as 'Generic SDMX data fetch from any Norges Bank flow', using a specific verb and resource. It distinguishes itself from siblings like get_exchange_rate by emphasizing its generic nature and provides concrete examples (EXR, IR).

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 gives clear usage context by explaining how to construct keys, providing examples, and pointing to list_flows for available flow references. However, it doesn't explicitly state when not to use this tool or mention specific alternatives for the same data-fetching purpose.

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

All Norges Bank toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes (e.g., entity_profile vs compare_entities), but there is some overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as among polymarket tools, which could cause misselection.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun (list_flows, get_series) with descriptive names (ask_pipeworx, bet_research) and varying conventions (snake_case vs no underscores).

Tool Count3/5

33 tools is on the high side but manageable for a broad platform; however, the server name 'Norges Bank' suggests a narrower scope, making the count feel bloated.

Completeness2/5

For a server named 'Norges Bank', many tools are irrelevant (polymarket, pipeworx meta-tools, memory, etc.), leaving significant gaps in core Norwegian banking data coverage.

Resources