Get the biggest stock movers across ALL African exchanges combined — top gainers and losers by % change from every covered market today.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of movers per category. Defaults to 10. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint and idempotentHint true. The description adds useful context: aggregation across all African exchanges, metric is % change, timeframe is today, and categories include top gainers and losers. 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, well-structured sentence that front-loads the action and scope, with no filler or repeated schema content.
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?
For a simple read-only tool with one optional parameter and good annotations, the description covers action, scope, metric, timeframe, and output category. No output schema exists, but the description provides enough context for an agent to invoke correctly.
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?
The input schema already fully describes the only parameter, limit, with 'Number of movers per category. Defaults to 10.' The description adds no additional parameter detail, so the baseline of 3 applies.
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 uses a specific verb 'Get' with a precise resource: 'biggest stock movers across ALL African exchanges combined — top gainers and losers by % change'. It clearly distinguishes from per-exchange siblings by emphasizing 'ALL' and 'combined'.
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 signals when to use this tool through 'ALL African exchanges combined' and 'every covered market today', implying it is for a pan-African view rather than exchange-specific tools. However, it does not explicitly name alternatives or exclusions.
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.
Tools are largely distinct in purpose, with clear resource-specific patterns like get_african_exchange_stocks vs get_ngx_all_stocks. Some overlap exists between generic African tools and NGX-specific convenience wrappers (e.g., get_market_open_status vs get_ngx_market_status), and get_african_policy_rates overlaps with get_country_macro_indicators on inflation/policy rate data, but descriptions clarify these boundaries.
All tools follow a consistent get_<domain>_<object> snake_case pattern, with plural/singular used appropriately (e.g., get_african_exchanges vs get_african_exchange). No mixed conventions or vague generic verbs—every name clearly signals its resource.
At 21 tools, the server is on the heavier side but within the 16-25 borderline zone. Each tool addresses a distinct data need for the broad African-markets domain, though some redundancy from NGX-specific wrappers (e.g., get_ngx_market_status vs get_market_open_status) could be trimmed.
The server covers a wide range of market data: exchanges, indices, stocks, FX, macro indicators, yield curves, and bank data. Minor gaps include lack of single-stock price/history for non-NGX exchanges and limited historical data beyond Nigeria's macro series, but core market-analysis workflows are well supported.
