get_top_risky_aiGet the highest-risk AI tools by AI Exposure Index risk score. Returns name, domain, category, and risk score + band. Covers the free top 100.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many (default 10, max 25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds the 'free top 100' scope and notes the returned fields (name, domain, category, risk score + band). This provides some context beyond annotations, but does not disclose other behaviors like pagination or rate limits. It adds moderate value without contradiction.
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 three short, front-loaded sentences: purpose, return fields, and scope. Every sentence adds distinct value with no redundancy or filler.
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 simple read-only list operation with one optional parameter and no output schema, the description covers return fields, the sorting basis (highest risk), and the universe (free top 100). It is sufficiently complete, though it could mention sorting order or limit behavior explicitly.
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 input schema has 100% coverage for the only parameter 'limit', including default and max, so the description does not need to elaborate. The description adds no additional parameter context, keeping the baseline score of 3.
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 returns the highest-risk AI tools ranked by AI Exposure Index risk score. It specifies the resource (AI tools), the metric (risk score), and the output fields, distinguishing it from siblings that list all shadow AI or look up specific tools.
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 when you need a ranked list of risky AI tools, but does not explicitly mention when to prefer this over sibling tools like list_shadow_ai or lookup_ai_tool_risk. No exclusions or alternative guidance are provided, so guidance is only implied.
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 serves a clearly distinct purpose: check_domain_against_registry verifies domains, get_top_risky_ai returns ranked risk, list_shadow_ai provides a filterable list, and lookup_ai_tool_risk retrieves detailed info by name/vendor/domain. No two tools overlap in a way that would cause selection confusion.
All tool names follow a consistent verb_noun pattern using snake_case: check_, get_, list_, lookup_. The verbs accurately describe each action and the pattern is uniform across the set.
With 4 tools, the server is well-scoped for a read-only registry. Each tool covers a distinct query type without being overly redundant or too sparse. This is an appropriate size for the intended purpose.
The server covers the core operations: domain checking, listing, top-risk ranking, and detailed lookup. A minor gap is the lack of a way to discover available categories for filtering, since list_shadow_ai accepts a category parameter but no tool enumerates them. Overall, the surface is nearly complete for a registry lookup service.