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Chatbot Manage Tool

chatbot_manage
Destructive

Manage embeddable chatbots backed by FleetQ agents: create, update, delete, toggle status, manage widget tokens, list sessions, view analytics and learning entries.

Instructions

Embeddable chatbots backed by an existing FleetQ agent. Each instance issues per-widget tokens for embedding, tracks sessions, and exposes analytics + learning entries. Conversations consume team credits via the bound agent's provider.

Actions:

  • list / get (read) — list all or fetch one (chatbot_id).

  • create (write) — name, agent_id, config (theme, greeting, allowed_origins[]).

  • update (write) — chatbot_id + any creatable field.

  • delete (DESTRUCTIVE) — chatbot_id. Cascades — also revokes all widget tokens.

  • toggle_status (write) — chatbot_id. Flips active ↔ disabled.

  • token_create (write) — chatbot_id. Returns an embeddable widget token (display once).

  • token_revoke (DESTRUCTIVE) — chatbot_id, token_id. Invalidates a single widget instance.

  • session_list / analytics / learning_entries (read) — chatbot_id; optional date range.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform: list, get, create, update, delete, toggle_status, token_create, token_revoke, session_list, analytics, learning_entries
deadline_msNoOptional: max wall-clock time (ms) the tool may spend. If exceeded during the call, returns a DEADLINE_EXCEEDED error. Minimum 100 ms. Leave unset for no deadline.
statusNoFilter by status: active, inactive, draft, suspended
limitNoMax results (default 20, max 100)
idYesChatbot UUID or slug
nameYesChatbot display name
typeYesChatbot type
system_promptYesSystem prompt for the backing agent
providerNoLLM provider (default: anthropic)anthropic
modelNoLLM model (default: claude-haiku-4-5)
descriptionNoOptional description
welcome_messageNoWelcome message shown on first open
confidence_thresholdNoConfidence threshold for escalation (0.0-1.0, default 0.7)
human_escalation_enabledNoEnable human escalation for low-confidence responses
workflow_idNoOptional workflow UUID to delegate message processing
approval_timeout_hoursNoHours before escalated approval expires (default 48)
fallback_messageNoFallback message for escalated responses
widget_configNoWidget config: {position, theme_color, title}
chatbot_idYesThe chatbot UUID
rotate_existingNoIf true, existing active tokens get a 48-hour expiry grace period before being invalidated. Default: false
token_idYesThe chatbot token UUID (returned by chatbot_token_create)
channelNoFilter by channel: web_widget, api, telegram, slack
daysNoNumber of days to look back (default 30, max 90)
Behavior5/5

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

The description goes far beyond the destructiveHint annotation by explicitly labeling actions as read, write, or destructive, detailing cascading effects (e.g., delete revokes all widget tokens), and noting credit consumption. This provides rich behavioral context that annotations alone do not cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a concise overview, a bulleted action list, and clear categorization. It is slightly long due to the number of actions, but each sentence serves a purpose. Minor redundancy could be trimmed without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 23 parameters and 11 actions, the description covers the core aspects: action types, destructive behavior, credit usage, and optional filters. It does not describe output format or pagination details, but these are partially covered by the input schema. Overall, it provides sufficient context for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the parameter semantics are already well-documented in the input schema. The tool description adds no additional parameter-specific details beyond listing actions, so it meets the baseline but does not surpass it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool manages 'Embeddable chatbots backed by an existing FleetQ agent' and lists all actions. While it differentiates from other tool management siblings by focusing on chatbots, it does not explicitly contrast with sibling tools like agent_manage or assistant_manage.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus other similar manage tools. The description lacks any 'when to use' or 'when not to use' context, leaving the agent to infer from the name alone.

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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