sarb_home_ratesCurrent headline South African rates shown on the SARB home page: CPI & PPI inflation, SARB Policy Rate, prime lending rate, government bond yields, Sabor/Zaronia money-market rates, and key Rand exchange rates (USD/GBP/EUR). Each item includes its TimeseriesCode for use with the sarb_timeseries tool. No arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false. The description adds context on what data is returned (specific rates) and that the tool takes no arguments, aligning with annotations. No contradiction.
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?
The description is a single concise paragraph that efficiently lists the data provided, mentions the TimeseriesCode link, and explicitly states the lack of arguments. No redundant information.
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?
Given no output schema, the description provides a good overview of what the output contains (inflation rates, policy rate, exchange rates, etc.) and the TimeseriesCode. However, it could be slightly more explicit about the return format, though the tool's simplicity makes it sufficient.
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?
There are zero parameters, so the description's statement 'No arguments' is sufficient. Baseline for 0 parameters is 4. Schema coverage is 100% trivially, and no additional parameter meaning is needed.
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 that the tool returns 'Current headline South African rates shown on the SARB home page' and lists specific rate categories (CPI, PPI, policy rate, etc.). This distinguishes it from sibling tools like sarb_exchange_rates which only returns exchange rates.
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 implies when to use the tool: to get current headline rates. It mentions that each item includes a TimeseriesCode for use with sarb_timeseries, suggesting a workflow. However, it does not explicitly state when not to use it or provide comparisons with siblings like sarb_current_market_rates.
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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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.