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Submit a text prompt to a Grok model and receive its reply. Persist conversation context with sessions, and activate the agentic loop for tool-augmented reasoning.

Instructions

Send a text prompt to a Grok model and return its reply.

Absorbs the old agentic_chat tool: the ReAct AgentLoop is now the default route, so the model has its tool surface and self-directs. Set enable_agentic=False to force a single toolless completion.

Args: prompt: User message to send to the model. session: Optional session name. Persists conversation history. model: Grok model id (defaults to grok-build-0.1). system_prompt: Optional system instruction prepended to the conversation. agent_count: 4 or 16. Only valid with grok-4.20-multi-agent. enable_agentic: If True (default), runs through the ReAct AgentLoop. require_reasoning_level: Minimum required Grok reasoning level (low, medium, high).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNogrok-build-0.1
promptYes
sessionNo
agent_countNo
system_promptNo
enable_agenticNo
require_reasoning_levelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoHuman-formatted output (includes footers, citations, cost summary).
modelYesActual executing model ID (e.g. 'grok-4.5').
planeNoAPI
routeYesHigh-level route (fast/agentic/research/etc.).
tokensNoTotal tokens consumed.
profileNoInternal routing profile.
sessionNoPersistent session name.
cost_usdNoExact USD cost from xAI billing metadata.
responseYesRaw model output or primary content.
citationsNoNative xAI/X citations with URL + snippet.
latency_secNo
response_idNoServer-side stateful ID for continuation.
finish_reasonNounknown
reasoning_effortNoGrok 4.5+ native reasoning level.
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses that the model self-directs with tool surface by default, and that setting enable_agentic=False forces a toolless completion. Mentions reasoning level requirement. Does not address rate limits or auth, but for a chat tool these are less critical.

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?

Description is a multi-line docstring with a concise one-sentence summary upfront. The args list is well-structured. Every sentence adds value, though could be more terse. Still, it is efficient and front-loaded.

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?

Given the presence of an output schema (Has output schema: true), the description need not explain return values. It covers all parameters, modes, and behavioral notes (agentic vs toolless, reasoning levels). Mentions multi-agent constraint. Sufficient for a chat tool.

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

Parameters4/5

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

Schema description coverage is 0%, so description must compensate. It explains each parameter: prompt (user message), session (persists history), model (Grok model id), system_prompt (prepended instruction), agent_count (4 or 16, only with grok-4.20-multi-agent), enable_agentic (default True), require_reasoning_level (enum values). Adds significant meaning beyond schema.

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

Purpose5/5

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

The description clearly states 'Send a text prompt to a Grok model and return its reply,' using a specific verb and resource. It distinguishes from siblings like chat_with_files and chat_with_vision by focusing on text input. The absorption of the old agentic_chat tool is also noted, adding clarity.

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

Usage Guidelines4/5

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

Provides clear guidance on when to use agentic vs non-agentic mode via enable_agentic parameter. Mentions default model and session persistence. Does not explicitly exclude use cases or list alternatives, but sibling tool names imply other tools for file/vision. Lacks when-not-to-use conditions.

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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