lookup_ai_tool_riskLook up an AI tool by name, vendor, or domain and return its AI Exposure Index risk score, risk band, category, and whether it trains on user data. Covers the free top 100.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Tool name, vendor, or domain (e.g. 'Cursor', 'OpenAI', 'cursor.sh'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful behavioral context: the 'free top 100' coverage limitation, which affects results when the tool is not in that set. It does not contradict annotations. It goes beyond simple read-only disclosure with this scope constraint.
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?
Two concise sentences. The first states purpose and outputs, the second states the coverage limit. No unnecessary words, front-loaded with the action and resource.
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 the tool's simplicity (one param, no output schema, no nested objects), the description is complete. It lists all key return fields and the coverage limitation. An agent can confidently invoke the tool based on this description alone.
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 coverage is 100%, and the query parameter description already explains the accepted values. The tool description restates 'name, vendor, or domain', which matches the schema, but adds no new parameter-specific meaning beyond what the schema provides. Baseline 3 is appropriate.
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 uses the specific verb 'look up' and clearly identifies the resource (AI tool) and the exact data returned (risk score, risk band, category, training on user data). It distinguishes itself from siblings like list_shadow_ai or get_top_risky_ai by focusing on querying a specific tool by name/vendor/domain.
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 clear usage: use this when you need risk details for a particular AI tool by name, vendor, or domain. It provides context without explicit alternatives or exclusions, but the scope limitation ('free top 100') gives practical guidance. It does not name sibling tools as alternatives, so not a 5.
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.