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Chat through the gateway

lobstack_chat

Send a prompt or conversation through Lobstack Gateway and get a reply plus a receipt with serving model, token counts, USD cost, and savings reason; auto routes to a capable model by cost.

Instructions

Send a prompt or conversation through the Lobstack Gateway and get the reply plus a receipt: the model that actually served it, token counts, USD cost, and any saving with the reason it may be claimed. Model "auto" (the default) lets the router pick the cheapest model that can handle the prompt. This call spends money against the configured key's allowance.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Lobstack model key, e.g. "claude-sonnet-5". Defaults to "auto", which lets Token Intelligence pick the cheapest model that can handle the prompt. lobstack_models lists the keys.
`prompt`	No	A single user message. Use this or `messages`, not both.
`system`	No	System prompt, prepended to the conversation.
`messages`	No	A full conversation, OpenAI-shaped. Use this or `prompt`, not both.
`max_tokens`	No	Cap on the reply length.
`temperature`	No	Sampling temperature. Some served models do not accept it; the receipt says when it was dropped.

Output Schema

TableJSON Schema

Name	Required	Description
`text`	Yes	The assistant's reply.
`model`	Yes
`quota`	Yes	Allowance remaining, as the gateway reported it.
`usage`	Yes
`receipt`	Yes	Null when the endpoint sent no receipt at all.
`dropped_params`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 11, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already disclose openWorldHint, non-readonly, non-idempotent, non-destructive. The description earns credit beyond that by stating it charges the configured key, that the receipt reports the actually-served model and any saving/claim reason, and that temperature may be silently dropped for some models.

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?

Three sentences with no wasted words, and the core action plus the cost consequence are front-loaded. Slightly dense but each sentence carries distinct information.

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 chat/completion call with an output schema, the agent has the action, the money-spend warning, the routing behavior, and receipt contents. The main omission is explicit guidance to preview with lobstack_route_preview before spending.

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?

Schema description coverage is 100%, so model, prompt, system, messages, max_tokens and temperature are all already documented in the schema. The description restates the auto default but adds no syntax or format detail beyond what the schema carries; baseline 3 applies.

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?

Names a specific verb (Send) plus resource (prompt/conversation) and adds the scope of the return (reply + receipt with model, tokens, cost, savings). The function is unmistakable, but the description never names a sibling like lobstack_route_preview to sharpen the boundary, so it stops short of a 5.

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

Usage Guidelines3/5

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

It flags a real usage consideration (this call spends money) and explains the auto default, but gives no explicit when/when-not guidance and does not route the agent to lobstack_route_preview for a cost-free preview. Usage is implied rather than stated.

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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