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AIOCANA
by AIOCANA

AIOrouter Chat

aiorouter_chat

Send chat completion requests to AIOrouter models and get the full response. Choose from Qwen, DeepSeek, GLM, Kimi, Grok and more, with adjustable max tokens and temperature.

Instructions

Send a chat completion to any AIOrouter model (Qwen, DeepSeek, GLM, Kimi, Grok, etc). Returns the model's full response. Non-streaming only (MCP limitation). Increase max_tokens (up to 8192) for long responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesModel ID from aiorouter_list_models
messagesYesConversation messages
max_tokensNoMax response tokens (1-8192)
temperatureNoSampling temperature (0-2)
Behavior4/5

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

Despite having only destructiveHint=false as an annotation, the description discloses the non-streaming limitation (MCP constraint) and the behavior of returning the model's full response. It also highlights the max_tokens cap, which is a meaningful constraint. This goes beyond the sparse annotations without contradicting them.

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

Conciseness5/5

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

The description is two sentences with no filler. It front-loads the primary purpose and adds useful caveats (non-streaming, token limit) in a compact way. Every clause earns its place.

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 tool with no output schema and sparse annotations, the description covers the core action, response behavior, and a key limitation. It does not explain the exact response format or error cases, but the chat completion context makes the return type reasonably predictable. It is sufficient for an agent to use the tool 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 description coverage is 100%, so parameters are already well-documented. The description adds extra value by advising to increase max_tokens for long responses, which is a usage hint beyond the schema's plain range. It also reinforces the model parameter's source via aiorouter_list_models, though this lives in the schema as 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 uses a specific verb ("Send") and resource ("chat completion to any AIOrouter model"), clearly distinguishing this as the core execution tool among siblings like aiorouter_list_models and aiorouter_get_usage. It lists example models to add further specificity.

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 provides clear context for use (sending chat completions) and even includes a cross-reference to aiorouter_list_models via the model parameter description. It states the non-streaming limitation and advises increasing max_tokens for long responses, offering practical usage guidance. It does not explicitly name alternatives, but sibling tools are clearly informational or comparative.

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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