Create Einstein Bot (Classic)

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate a write operation, open-world behavior, and idempotency. The description adds valuable context by explaining the bot is created with an ML domain and by listing post-creation steps (train, activate, connect). It does not cover potential side effects like name collisions, but the annotations reduce the burden, and the added lifecycle guidance is useful.

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 well-structured with a high-level summary, a 'Note' for alternatives, a bulleted list of use cases, a dialog-definition breakdown, and a numbered post-creation workflow. Every section serves a clear purpose, and the length is justified given the complexity of the tool.

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?

Despite no output schema, the description is thorough: it covers what metadata types are created, how dialogs are configured, the ML domain behavior, and the necessary manual follow-up steps (train, activate, connect). This gives the agent enough context to use the tool and set expectations, making it 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?

The input schema provides 100% coverage with descriptive parameter names and comments. The description goes beyond by explaining the purpose of each dialog field ('utterances: training phrases that trigger this dialog', 'messages: bot responses') and clarifying dialog types (Main, System, Rule). This enriches the schema with semantic context.

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 opens with a specific verb and resource: 'Creates a classic Einstein Bot (Bot + BotVersion metadata types) with one or more conversation dialogs.' It clearly distinguishes from the sibling sf_create_agent by contrasting classic bots with AI-first agents, leaving no ambiguity about what this tool does.

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 tells when to use classic Einstein Bots ('Structured FAQ automation', 'Simple data collection workflows', 'Channels that don't support Agentforce') and directly points to sf_create_agent as the alternative for AI-first agents. This is textbook usage guidance with clear exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.