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Talk to one or more AI models in parallel, consensus, or roundtable modes. Supports files, images, and multi-turn threads.

Instructions

UNIFIED CHAT — talk to one or more AI models. mode "chat" (default): 1..N models answer independently in parallel. mode "consensus": ≥2 models answer, then refine after seeing each other. mode "roundtable": models answer sequentially, each building on the running transcript. Supports files, images, and continuation_id for multi-turn threads (you may switch modes on resume). Use model "auto" for automatic selection. IMPORTANT: use the "files" parameter to share code/file content instead of pasting into the prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExecution mode. "chat" (default): independent parallel answers. "consensus": ≥2 models answer then refine via cross-feedback. "roundtable": sequential turn-based dialogue in the given model order. Default: "chat".
asyncNoExecute in the background. When true, returns a continuation_id immediately and processes the request asynchronously; poll with check_status. Default: false
filesNoFile paths to include as context (absolute or relative). Supports line ranges: file.txt{10:50}, file.txt{100:}. Example: ["./src/utils/auth.js{50:100}", "./config.json"]. IMPORTANT: Always use this parameter to share file content instead of copying code into the prompt.
exportNoExport the conversation to disk. Creates a folder named for the continuation_id with numbered request/response files and metadata. Default: false
imagesNoImage paths for visual context (absolute or relative paths, or base64 data). Example: ["C:\Users\username\diagram.png", "./screenshot.jpg", "data:image/jpeg;base64,/9j/4AAQ..."]
modelsNoModels to use. Examples: ["auto"] (recommended), ["codex"], ["codex", "gemini", "claude"]. In mode "chat" each model answers independently; in "consensus" they refine after seeing each other; in "roundtable" they speak in the given ORDER, each seeing the transcript. Default: ["auto"].
promptYesYour question, topic, or task with relevant context. More detail enables better responses. Example: "How should I structure the authentication module for this Express.js API?"
continuation_idNoContinuation ID for a persistent multi-turn thread. Auto-generated in the first response; pass it back to continue. You MAY change the mode or models on a resuming turn.
reasoning_effortNoReasoning depth for thinking models. Examples: "none" (no reasoning, fastest - GPT-5.1+ only), "minimal", "low", "medium" (balanced), "high", "max". Default: "medium"
Behavior4/5

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

Despite no annotations, the description details the three execution modes, async behavior, export functionality, and reasoning_effort. It discloses file and image support. No contradictions; the description carries the burden well.

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 moderately concise with 6-7 sentences, each providing value. It front-loads the main purpose and uses bold for emphasis. Minor redundancy could be trimmed, but overall efficient.

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?

Covers the tool's complexity: multiple modes, async, export, file/images, continuation_id, reasoning_effort. Missing explicit error handling or rate limits, but these are not critical for selection. Adequate for an AI agent to use correctly.

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 coverage is 100% (baseline 3), but the description enriches understanding with examples for models, images, reasoning_effort, and the 'IMPORTANT' note about files. It adds practical context beyond the schema definitions.

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 it's a unified chat tool for talking to one or more AI models, defines three modes with distinct behaviors, and distinguishes itself from sibling tools (cancel_job, check_status) which handle job management. The verb 'talk' and resource 'AI models' are specific.

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 explicit guidance on when to use each mode, how to use continuation_id for multi-turn, and emphasizes using the 'files' parameter for sharing code. It does not contrast with siblings, but siblings are unrelated. The context is clear and actionable.

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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