sarb_exchange_ratesSelected South African Rand exchange rates. frequency="daily" (default) returns latest daily rates (Rand per USD, GBP, Euro, etc.). frequency="monthly" returns latest monthly figures including the nominal and real effective exchange rate indices. Each item includes a TimeseriesCode usable with the sarb_timeseries tool.
| Name | Required | Description | Default |
|---|---|---|---|
| frequency | No | daily (default) or monthly. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "frequency": "daily"
+ },
+ {
+ "frequency": "monthly"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds that rates are 'latest' and monthly includes indices, plus hints at integration with sarb_timeseries, providing useful behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. First sentence states purpose, second explains the parameter effect. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description mentions TimeseriesCode and gives examples of currencies and indices. Some output structure details are missing, but overall it provides reasonable completeness for a simple data retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with enum values described. The description goes further by explaining what each frequency returns (daily: major currencies, monthly: indices), adding meaningful context for parameter selection.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides selected South African Rand exchange rates with specific frequency options. It distinguishes from sibling tools by mentioning TimeseriesCode usable with sarb_timeseries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the two frequency options but does not explicitly state when to use this tool versus other Sarb tools like sarb_cpd_rates or sarb_current_market_rates. Usage context is implied but lacks direct guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.