eyepup_visitors_hotReturn the top high-intent visitors currently active. Use when the user asks 'who's hot', 'who's about to convert', 'who should I focus on'.
| Name | Required | Description | Default |
|---|---|---|---|
| site | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says it returns 'active high-intent visitors' but does not clarify how 'currently active' is defined, whether a site parameter is required, how the limit parameter affects results, or what the return format looks like. This is minimal transparency.
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 extremely concise: one sentence for purpose plus a short usage clause. Every word earns its place, with no fluff or repetition. It is well-front-loaded and easy to scan.
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 tool with no output schema and no annotations, the description is incomplete. It tells the agent when to use the tool but not what the returned data looks like or how parameters behave. The agent can infer intent but not confidently compose correct invocations.
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 0%, and the description does not mention either 'site' or 'limit' at all. It does not explain that 'site' likely filters by domain or that 'limit' caps the number of results, leaving the agent with no semantic guidance beyond raw schema names.
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 function with a specific verb and resource: 'Return the top high-intent visitors currently active.' It distinguishes itself from the sibling 'eyepup_visitor' by focusing on the 'top' and 'high-intent' aspect, so the agent knows this is for ranked visitor recommendations.
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 provides explicit usage examples: 'Use when the user asks 'who's hot', 'who's about to convert', 'who should I focus on'.' This gives clear context for when to invoke the tool. However, it does not state when not to use it or mention alternatives among siblings, so it just misses a perfect score.
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 has a clearly distinct purpose: ask is for free-text questions, log is for recording shipped changes, todo is for top friction patterns, visitor is for a specific visitor profile, and visitors_hot is for active high-intent visitors. There is minimal overlap, and the descriptions explicitly guide when to use each.
All tool names share the 'eyepup_' prefix, but the second part mixes verbs (ask, log) with nouns (todo, visitor, visitors_hot). This is readable but not a consistent verb_noun convention. A more uniform pattern like 'ask_question', 'log_change', 'get_todo', 'get_visitor', 'list_hot_visitors' would be clearer.
With 5 tools, the server is well-scoped for its purpose of visitor behavior analysis and optimization. Each tool addresses a distinct need without unnecessary bloat or overlap. The count feels right for a focused analytics assistant.
The toolset covers the core workflow: asking questions, identifying friction patterns, viewing hot visitors, drilling into individual visitor profiles, and logging changes for impact analysis. Minor gaps exist, such as no direct tool for listing all visitors or viewing historical trend charts, but the 'ask' tool can likely address many such queries. Overall, the lifecycle is adequately covered for the stated purpose.