get_chatbot_analyticsGet a chatbot's analytics for a period: conversation volume, feedback, answered-question %, negative-sentiment %, popular sources/categories, top countries.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ISO date; used when period=custom. | |
| from | No | ISO date; used when period=custom. | |
| period | No | last_7_days, last_30_days (default), or custom. | |
| chatbot_id | Yes | The chatbot id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description does not disclose behavioral traits such as data freshness, rate limits, authentication requirements, or whether the operation is read-only. The description implies a query but lacks explicit behavioral context.
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 a single sentence that efficiently lists the key metrics. It is concise but lacks structural elements like bullet points or separate sections. Still, it is appropriately sized for the tool's simplicity.
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?
The tool has no output schema and minimal annotations. The description explains what metrics are returned, which is useful. However, it does not mention data aggregation details, time range behavior, or limitations. Given the tool's simplicity, the description is moderately complete but could be improved.
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%, so the baseline is 3. The description adds value by enumerating the metrics returned (e.g., conversation volume, feedback), but does not further explain parameter specifics beyond what the schema provides.
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 retrieves analytics for a chatbot and lists specific metrics (conversation volume, feedback, percentages, sources, countries). This distinguishes it from sibling tools like get_chatbot_insights, which likely provides different analytics.
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 does not provide any guidance on when to use this tool versus alternatives (e.g., get_chatbot_insights). No when-to-use or when-not-to-use context is given.
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 targets a distinct resource and action (sources, suggested messages, chatbots, inbox, leads, analytics, insights). There is no ambiguity between tool purposes.
All tool names follow a consistent verb_noun pattern (add_, get_, list_, recrawl_, update_). The naming convention is uniform and predictable.
15 tools is well-scoped for a chatbot manager. The set covers all key functional areas without being excessive or insufficient.
The tool surface is largely complete but lacks delete operations: there is no 'remove_suggested_message' (though referenced in a description) and no way to delete a source. These are notable gaps.