Top mentioned named entities (people, organizations, places) in news for a country in the last N hours. Returns name + mentions count.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| window | No | 1d | |
| country | Yes | Source-country ISO 2-letter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that returns are named entities with counts, but lacks details on ordering, frequency, or any constraints. Minimal behavioral insight.
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?
Single sentence, very concise. However, it omits important information that could be conveyed without much more text. Balance between conciseness and completeness is reasonable but not optimal.
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, no annotations, and low parameter coverage, description is insufficient. It does not explain return format, ordering, or provide guidance on window parameter usage. Incomplete for effective use.
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 33% (only country has description). Description adds no additional meaning for 'limit' or 'window'; window format is unspecified. Does not compensate for low 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?
Description clearly states the tool returns top mentioned named entities (people, organizations, places) for a country and in the last N hours, with name and mention count. It effectively differentiates from siblings gdelt_search_events and gdelt_tone_timeseries.
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?
Description implies usage for retrieving trending entities, but does not explicitly state when to use this tool versus siblings. No mention of when not to use or context for choosing between tools.
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.
Each tool targets a distinct function: event search, tone timeseries, and trending actors. There is no overlap in purpose.
All tools follow the pattern 'gdelt_<noun>_<plural_noun>' with descriptive names using underscores. Naming is fully consistent.
Three tools are appropriate for a focused global events MCP server, covering core capabilities without bloat.
The tools cover search, tone analysis, and actor trends. Missing date-range filtering for search and detailed event lookup are minor gaps for a basic events server.