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Sarb Za

by io.github.pipeworx-io

Sarb Timeseries

sarb_timeseries
Read-onlyIdempotent

Historical observation series for a single SARB indicator, identified by its TimeseriesCode (obtain codes from home_rates, current_market_rates, cpd_rates or exchange_rates). Examples: "MMRD002A" (SARB Policy Rate), "MMRD000A" (prime lending rate), "EXCX135D" (Rand per US Dollar), "CPI1000F" (CPI). Optionally bound the range with start_date and end_date (YYYY-MM-DD); omit both for the full available history. An unknown code returns an empty list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesSARB TimeseriesCode, e.g. "MMRD002A", "EXCX135D", "CPI1000F".
end_dateNoRange end, YYYY-MM-DD (optional). Requires start_date.
start_dateNoRange start, YYYY-MM-DD (optional). Requires end_date.

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: +[
+  {
+    "code": "MMRD002A"
+  },
+  {
+    "code": "EXCX135D",
+    "end_date": "2024-12-31",
+    "start_date": "2023-01-01"
+  }
+]
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds critical context: unknown codes return an empty list, date format (YYYY-MM-DD), and the relationship between start/end dates. This goes beyond annotations, though rate limits or data freshness are not mentioned.

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 two sentences plus examples, front-loading purpose and usage. Every sentence adds value, with no redundancy. Examples are embedded naturally.

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?

Given 3 straightforward parameters, no output schema, and no nested objects, the description covers all essential aspects: what the tool does, how to get codes, date behavior, and error handling (empty list for unknown code). It is complete for an agent to use correctly.

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 descriptions, but the schema's mutual requirement statements for start/end dates are ambiguous. The description clarifies that both are optional when used together, and provides concrete examples. This adds meaning beyond schema alone.

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 retrieves historical observation series for a single SARB indicator by TimeseriesCode. It provides specific examples of codes, differentiating from sibling tools (which provide the codes) and making the purpose unambiguous.

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 advises obtaining codes from four sibling tools, which is excellent guidance. It explains optional date range parameters and the behavior when omitted. It could be improved by explicitly stating when not to use this tool, but the guidance is clear enough.

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

Several tools have overlapping or redundant purposes, most notably ask_pipeworx and ask_pipeworx_beta (currently identical), and the cluster of Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) which all surface trading opportunities. While the lengthy descriptions help, an agent could easily select the wrong tool.

Naming Consistency4/5

All tool names use snake_case with a readable verb/noun structure, and there are no casing inconsistencies. However, the verb-first vs noun-first pattern is not uniformly applied (e.g., ask_pipeworx vs sarb_timeseries vs polymarket_edge_tracker), so it's mostly consistent with minor deviations.

Tool Count2/5

At 36 tools, the set is large and exceeds the 25-tool threshold; the broad scope justifies some volume but the presence of duplicate/overlapping tools (ask_pipeworx_beta, multiple Polymarket scanners) makes the count feel inflated.

Completeness4/5

The set covers a wide domain — SARB data, company research, prediction markets, AI visibility, memory, and subscriptions — with a good lifecycle for most features. Minor gaps exist (no subscription update, no bulk data export), but the core workflows are well covered.

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