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Mansa African Financial Reference Data

Get public holidays for an African country and year

get_public_holidays

Return the public holidays for a country and calendar year, with each holiday's date, name and type. Names are returned in English wherever the country publishes it; pass a language code for others (Kenya also publishes Swahili).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoOptional language code for holiday names, e.g. 'sw' for Swahili.
yearYesFour-digit calendar year, e.g. 2026.
countryYesISO-2 country code, e.g. NG, KE, GH, ZA.

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the transparency burden. It discloses a key behavioral trait: holiday names default to English when available, and a language code can be passed for alternatives, with a specific note about Kenya/Swahili. However, it does not mention error handling, data source, or edge cases like unsupported countries or years, leaving some gaps.

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 long, front-loaded with the core action, and wastes no words. It efficiently conveys the return contents and the language nuance. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter tool with no output schema and no annotations, the description is reasonably complete. It states what is returned (date, name, type) and how language is handled. It could be slightly more explicit about the response format (e.g., array of objects), but the implied list structure is sufficient 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.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already explains each parameter. The description adds minor semantic detail by calling the year a 'calendar year' and clarifying the language behavior, but this does not significantly go beyond the schema. Baseline of 3 is appropriate.

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 returns public holidays for a given country and year, with specific output fields (date, name, type). It distinguishes itself from sibling tools, which all deal with financial or phone-related lookups, so there is no ambiguity about its purpose.

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 implies when to use the tool: when you need public holidays for a country and year. It provides context for the optional language parameter and mentions Kenya's Swahili support, but it does not explicitly state when not to use it or name alternatives. Still, the sibling tools are clearly unrelated, so the usage context is clear.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct data type: currency, holidays, banks (list vs lookup), phone network, and NUBAN account resolution. Even the two bank tools are clearly separated by purpose—listing all institutions vs resolving one from an identifier. No tool overlaps meaningfully with another.

Naming Consistency5/5

All tool names follow a uniform verb_noun snake_case pattern (get_, list_, lookup_, resolve_), making the action and resource immediately clear. The convention is applied consistently across all six tools.

Tool Count5/5

With six tools, the server sits comfortably in the ideal 3-15 range. Each tool covers a distinct reference data need without bloat or redundancy, making the count well-scoped for the stated purpose.

Completeness4/5

The set covers core African financial reference data—currencies, holidays, banks, mobile networks, and NUBAN validation. However, there is no tool to list supported countries or all currencies, which would be a natural complement to the country-based lookups. This is a minor gap that agents can work around.