Monthly history for an African macro indicator. Nigeria reaches back to January 2006 (policy_rate, inflation, inflation_food, inflation_core, treasury_bill_rate and more).
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Optional end period, YYYY-MM. | |
| from | No | Optional start period, YYYY-MM. | |
| country | Yes | ISO-2 country code, e.g. NG. | |
| indicator | Yes | e.g. policy_rate or inflation. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds useful behavioral context by specifying the data frequency ('monthly') and the depth of history for Nigeria (2006 onward), which sets expectations about data availability. It also lists example indicators and notes 'and more', indicating an extensible set. This goes beyond the annotations without contradicting them.
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, compact sentence that conveys the core functionality and key data coverage detail. There is no fluff or redundant phrasing; it earns its place by adding the Nigeria history depth and example indicators. Ideal conciseness.
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?
With no output schema, the description carries some burden to set expectations. It clarifies the data frequency (monthly), the region (Africa), and that history depth varies by country (Nigeria since 2006). It does not describe the response format (e.g., array of {period, value}), but for a straightforward history fetch this may be inferred. The tool is adequately contextualized within its sibling suite.
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 description coverage is 100%, with all four parameters (country, indicator, from, to) already described in simple terms. The description adds a minor hint about data availability (Nigeria reaches back to 2006) that could inform the 'from' parameter, but it largely repeats indicator examples already present in the schema. Since the schema fully documents parameter formats, the description's incremental semantic contribution is limited.
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 the tool retrieves monthly historical data for African macro indicators. The verb 'history' and resource 'African macro indicator' make the primary function clear. It is distinguishable from sibling tools like get_country_macro_indicators (which likely returns current values) and get_african_policy_rates (current rates), though it does not explicitly name alternatives.
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 usage for historical macro data, especially given the mention of Nigeria's data extending back to January 2006. However, it does not explicitly state when to use this tool versus alternatives like get_country_macro_indicators or get_african_policy_rates, nor does it provide exclusions. The data availability note gives some practical context, but the guidance is mostly implicit.
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.
