Return aggregated AfriResolve usage intelligence, including total activity, demand signals, popular queries, tool usage, and recurring knowledge gaps.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of rows returned in each intelligence list |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the result is aggregated and names the included intelligence components, implying a read-only analytics operation. It does not mention freshness, caching, or output structure, but it gives a reasonable behavioral picture for a getter.
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?
A single, front-loaded sentence with no wasted words. It states the action, the resource, and the specific content categories efficiently, earning every part of the sentence.
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?
The description provides enough information to select and invoke the tool correctly: it is a zero-required-parameter analytics getter with an optional row limit, and it names the main output categories. It lacks exact return field names or temporal scope, but for this low-complexity tool it is largely complete.
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 only parameter, limit, is fully explained in the input schema as the maximum rows per intelligence list, so the schema carries the semantic weight. The description adds no additional parameter-level detail, matching the baseline for full schema coverage.
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 names a specific verb ('Return'), a clear resource ('AfriResolve usage intelligence'), and enumerates the categories of data included. It is immediately distinguishable from sibling tools, which all operate on food/dishes rather than system usage analytics.
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?
Usage context is implied rather than explicit: the tool clearly returns usage analytics while siblings perform food lookups, but the description never states when to choose this over alternatives or when not to use it. It is inferable but not directly guided.
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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rank_african_foods and search_african_foods have nearly identical descriptions and field lists, making their boundary unclear. find_dishes_by_country vs find_foods_by_country and find_foods_by_country vs find_foods_by_region also create overlapping selection paths, though the facet descriptions help somewhat.
All tools follow a consistent snake_case verb_noun pattern: find_X_by_Y for faceted lookups, list_african_X for enumerations, resolve_african_X for normalization, and search/rank/get for other operations. There are no mixed naming conventions or camelCase deviations.
Thirteen tools is within a reasonable range for a food and dish knowledge base. However, rank_african_foods and search_african_foods largely duplicate each other, and some find_* variants could be consolidated, so not every tool fully earns its place.
The set covers listing, faceted lookup, name resolution, and food search/ranking well, but dishes lack a general keyword search tool equivalent to search_african_foods. There is also no dish category filter, so agents cannot search across all dish metadata without enumerating the full list.