get_chatbotGet a chatbot's current settings plus the models allowed on its plan and the credit cost per model. Read this before changing settings.
| Name | Required | Description | Default |
|---|---|---|---|
| chatbot_id | Yes | The chatbot id (from list_chatbots). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it is a read operation ('Get' and 'Read this before changing'), but does not detail authorization, rate limits, or other behavioral aspects.
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 sentences, front-loaded with the core purpose, no unnecessary words. Every sentence serves a purpose.
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 no output schema, the description adequately lists what the tool returns (settings, plans, credit costs). Single parameter fully covered by schema, and usage hint provided. Complete for its complexity.
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% with one parameter (chatbot_id). The description adds context: 'from list_chatbots', which provides additional meaning beyond the schema, improving usability.
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 specifies the verb 'Get' and the resource 'chatbot's current settings plus models and credit cost', clearly distinguishing from sibling tools like get_chatbot_analytics or get_chatbot_insights.
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?
Explicitly advises 'Read this before changing settings', indicating when to use the tool and implying a sequence of operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.