Update Legacy Bot

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

Annotations already declare readOnlyHint=false (mutation) and idempotentHint=true. The description adds valuable behavioral context: 'Partial update — omitted fields are left unchanged', and that activating the bot is 'best-effort; needs a prompt + LLM and a backend AI service'. It also details error conditions (401/403, 404, 422). This goes beyond the annotations without contradicting them, though it doesn't mention every possible side effect (e.g., propagation to chat history).

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 compact but information-dense. It front-loads the core purpose, then covers partial update semantics, activation condition, prerequisites, alternatives, auth, and errors in about four sentences. While it is longer than minimal, every sentence earns its place and there is no fluff. The structure is logical 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?

Given the tool has 17 optional parameters, no required fields, and no output schema, the description is remarkably complete. It explains the critical prerequisite (legacy aiBot vs. API-created), provides the correct alternative workflow, specifies auth modes, and lists expected errors. An agent has everything needed to decide whether to call this tool and how to interpret failures. No significant gap remains.

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 schema already fully documents all 17 parameters with detailed descriptions. The tool description groups parameters into categories (prompt, LLM, trigger, etc.) but adds no per-parameter semantics beyond what the schema provides. Per the rubric, a baseline of 3 is appropriate when the schema does the heavy lifting.

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 precise verb ('Configure') and resource ('AI bot for an app'), and enumerates the configurable aspects (prompt, LLM, trigger, greeting, RAG behavior, identity, public widget settings). It explicitly distinguishes this tool from the agents family by stating it targets a 'legacy per-app aiBot (dashboard-created)' and points to the alternative workflow for API-created apps, so an agent can tell it apart from sibling tools without inspecting schemas.

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 gives explicit when-to-use guidance: 'Requires: an app with a legacy per-app aiBot (dashboard-created). API-created apps have none: use ethora-agents-create-v2 -> ethora-agent-invite-to-chat -> ethora-agents-activate-v2 instead.' It also clarifies auth modes and lists common error codes, leaving no ambiguity about the correct context for invocation.

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