Get 7-day internet shutdown and censorship risk forecast for a country using XGBoost ML model trained on historical OONI data and political event calendar.
| Name | Required | Description | Default |
|---|---|---|---|
| country_code | Yes | ISO 3166-1 alpha-2 country code (e.g., IR, MM, TR) |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds useful context about the model family (XGBoost) and training data (historical OONI + political events), improving trustworthiness understanding, but omits forecast output shape, confidence, and horizon semantics.
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 efficient sentence that front-loads verb, resource, and horizon, then the methodology. No wasted words, though slightly dense.
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 read-only prediction tool with one well-documented parameter and no output schema, the description is adequate but lacks return-value context (prediction, confidence, factors) that would help interpret results, especially against many sibling forecast tools.
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% and country_code is fully documented with format and examples in the schema. The description adds no parameter meaning beyond the schema, so baseline 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?
States a specific verb+resource (get 7-day shutdown/censorship risk forecast), plus the model and data sources. However, it does not differentiate from closely related siblings such as get_multi_horizon_forecast or get_shutdown_risk, leaving the agent to infer the difference.
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?
No explicit when-to-use guidance or exclusion criteria. Siblings like get_shutdown_risk, get_multi_horizon_forecast, and get_election_risk occupy overlapping territory, and the description gives no routing logic between them.
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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