eyepup_visitorFetch the full LLM-written profile for a single visitor by distinct_id. Use after eyepup_visitors_hot or eyepup_todo to dig into a specific visitor.
| Name | Required | Description | Default |
|---|---|---|---|
| distinct_id | Yes | UUID-shaped distinct_id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'Fetch' clearly indicates a read-only operation with no side effects. It describes the nature of the return value ('full LLM-written profile'), though it does not mention error cases or permissions. This is transparent enough for a simple retrieval tool.
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 two sentences with no redundant words. The core action is front-loaded, and the usage guidance is succinctly appended. Every word earns its place.
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 one-parameter lookup tool, the description covers the why, what, and when. The lack of an output schema is mitigated by the description saying it returns the 'full LLM-written profile'. It does not detail error handling, but that is not essential for this simple tool.
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 already fully describes distinct_id as a 'UUID-shaped distinct_id' with 100% coverage. The description only repeats 'by distinct_id' without adding meaning beyond the schema, so it hits the baseline for schema-heavy tools.
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 action ('Fetch') and resource ('full LLM-written profile for a single visitor'), with the required parameter ('by distinct_id'). It distinguishes itself from sibling tools by focusing on a specific visitor's detailed profile, contrasting with list or task-oriented 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 explicitly provides usage context: 'Use after eyepup_visitors_hot or eyepup_todo to dig into a specific visitor.' This tells the agent when to invoke this tool and names the preceding tools, making the workflow explicit.
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.