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Glama

Snb Ch

by io.github.pipeworx-io

Cube Structure

cube_structure
Read-onlyIdempotent

Fetch a Swiss National Bank cube's dimensions/structure (the dimension items / series keys available within the cube). Use this to understand what a cube contains before/after calling get_cube. Verified-live cube 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": "snbmonagg"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: the default cube ID (devkua), the list of verified-live IDs, and what each ID represents. It does not describe return format, but given the simple tool and no output schema, the added context is valuable.

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, all purposeful: purpose, usage, verified IDs, and default. It is front-loaded with the core action and immediately clarifies value. No redundant phrasing or padding.

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 tool with one optional parameter, no output schema, and rich annotations, the description provides complete context: what it does, when to use it, what inputs are valid, and the default. The mention of 'dimension items / series keys' hints at the response nature, making it adequately comprehensive.

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% with a description and examples for cubeId. The description enriches this by explaining what each cube ID means (e.g., devkua = foreign exchange rates, zimoma = money market), which is not present in the schema. This helps the agent select appropriate parameter values.

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 starts with a specific verb ('Fetch') and resource ('Swiss National Bank cube's dimensions/structure'), and clarifies with parenthetical detail ('dimension items / series keys'). It explicitly contrasts with sibling get_cube by stating 'Use this to understand what a cube contains before/after calling get_cube', clearly distinguishing its role.

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?

The description gives explicit usage context: use it before/after get_cube. It also provides a list of verified-live cube IDs with meanings, which guides the agent on valid inputs and when to apply each. No alternative is named, but the relationship to get_cube is sufficient.

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