Skip to main content
Glama

问另一个模型(二次意见)

ask_model

Submit a prompt or multi-turn conversation to any LLM model for consultation or testing, with asynchronous polling to retrieve the result when ready.

Instructions

调用一个 LLM 做对话,定位为“向另一个模型征询二次意见 / 试用”,不是主对话通道、不支持流式。异步提交 + 轮询:默认等待 60s,短问答直接返回 text;超时返回 task_id 用 get_task / wait_for_task 取回。model 用 llm-router 注册表模型(如 gemini-3.5-flash、claude-sonnet-4-6、gpt-5.5;可用集见 /v1/configs/llm_generations_models,与生成类不同)。传 prompt(单轮)或 messages(多轮,OpenAI 格式)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesllm-router 模型 id
promptNo单轮用户输入(与 messages 二选一)
systemNo系统指令(prompt 模式下前置)
messagesNo多轮消息(OpenAI 格式,与 prompt 二选一)
max_tokensNo
temperatureNo
wait_secondsNo最长等待秒数,默认 60,设 0 立即返回 task_id
Behavior4/5

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

Without annotations, the description covers key behaviors: non-streaming, async with 60s default wait, immediate return for short queries, and timeout handling via task_id. It does not mention rate limits or side effects, but is sufficient.

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 a single paragraph that front-loads the purpose and efficiently covers all essential aspects without redundant or unnecessary information.

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

Completeness3/5

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

The description covers the main behavior and parameter interactions, but lacks detail on the return value structure (e.g., format of direct text response) and error cases. Given no output schema, this is a notable gap.

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?

The description adds meaning beyond the schema by explaining the mutual exclusivity of prompt and messages, the role of system with prompt mode, and the behavior of wait_seconds (default 60, 0 returns task_id). Schema coverage is 71%, so description compensates well.

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 the tool's purpose as calling an LLM for dialogue, positioned as seeking a second opinion or trial, and distinguishes it from the main chat channel and sibling tools like media/audio generation.

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?

The description explicitly states this tool is for second opinions or trial, not the main chat channel, and mentions async polling with fallback to sibling tools get_task/wait_for_task. It provides clear context although not exhaustive.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cookaihq/aihub-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server