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Glama

Snb Ch

by io.github.pipeworx-io

Get Cube

get_cube
Read-onlyIdempotent

Fetch a Swiss National Bank statistical data cube's time series as JSON. Cube discovery is limited — pick a cubeId from these documented, verified-live IDs: devkua = foreign exchange rates (CHF per currency); zimoma = money market & reference rates incl. SARON; rendoblid = Confederation/euro bond spot interest rates (yield curve); snbmonagg = monetary aggregates (M1/M2/M3, currency in circulation). Defaults to devkua (FX rates).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cubeIdNoSNB cube ID, e.g. "devkua", "zimoma", "rendoblid", "snbmonagg". Default "devkua".

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: +[
+  {
+    "cubeId": "devkua"
+  },
+  {
+    "cubeId": "zimoma"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent), the description discloses that cube discovery is limited to a set of verified-live IDs and provides the exact list and default, which are crucial for correct invocation. No contradiction with annotations.

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?

Two sentences, front-loaded with the action and output type. The ID mapping is compact and efficient, with no fluff.

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 single-parameter tool with no output schema and strong annotations, the description fully covers what the agent needs: purpose, valid inputs, default, and output format. Nested or complex behavior is absent.

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?

The schema already has a description for cubeId, and the tool description enriches this by mapping each known ID to its semantic meaning (e.g., devkua = FX rates). This goes beyond the schema's basic example list.

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 uses 'Fetch' as a specific verb and identifies the resource as 'Swiss National Bank statistical data cube's time series'. It distinguishes from sibling cube_structure by focusing on time series data, not structure.

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?

Provides clear context by listing four valid cube IDs and their meanings, and states the default. However, it does not explicitly mention when to use this vs alternatives like cube_structure, so no explicit exclusions.

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
Disambiguation3/5

Several tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and suggest_questions all handle question-answering, with beta currently identical to the main router. The Polymarket family has six distinct but related tools, and descriptions are detailed enough to differentiate, but an agent could still misselect when the boundaries are subtle.

Naming Consistency3/5

All tool names use snake_case, but the pattern is mixed: some start with verbs (resolve_entity, validate_claim), while many are noun phrases (entity_profile, bet_research, polymarket_edges). This is readable but not a consistent verb_noun convention, and the repeated ask_pipeworx_* and polymarket_* prefixes add some structure.

Tool Count2/5

At 33 tools, the surface is heavy for an agent to navigate. While the server covers a broad data-research and prediction-market domain, the count exceeds what is typically manageable, and several tools (ask_pipeworx_beta, discover_tools) could be collapsed or merged without losing core capability.

Completeness4/5

The tool set covers the domain well: querying, grounded verification, entity profiles, comparisons, prediction-market analysis, subscriptions, memory, and onboarding. Minor gaps exist (e.g., no tool to fetch a full SEC filing body directly, relying on ask_pipeworx routing), but there are no obvious dead ends for the stated purposes.

Resources