aeX402 — Cross-Chain DeFi MCP: LINQ, AMM, Bridge, AI

ai_chat

Send a chat completion request to the aeX402 AI Router. Free tier: 3 calls/day. Paid: deducts credits based on token usage.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Model ID or alias: gpt-4o-mini (default), gpt-4o, claude-sonnet, claude-haiku, llama-70b, llama-8b, gemini-flash
`messages`	Yes	Chat messages array [{role: "user"\|"system"\|"assistant", content: "..."}]
`max_tokens`	No	Maximum tokens to generate (model-dependent default)
`temperature`	No	Sampling temperature (0-2)
`reasoning_effort`	No	low \| medium \| high — ask a reasoning-capable model to think harder. The chain-of-thought comes back in the tool result _meta ("com.aex402/reasoning").

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No
`model`	No
`usage`	No	prompt/completion/total tokens
`object`	No	always 'chat.completion'
`choices`	Yes
`created`	No	unix seconds

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedAug 8, 2026

addedOutput schema / properties / created

Added value: +{
+  "description": "unix seconds",
+  "type": "integer"
+}

addedOutput schema / properties / object

Added value: +{
+  "description": "always 'chat.completion'",
+  "type": "string"
+}

Changed1 schema field changedJul 17, 2026

addedInput schema / properties / reasoning_effort

Added value: +{
+  "description": "low | medium | high — ask a reasoning-capable model to think harder. The chain-of-thought comes back in the tool result _meta (\"com.aex402/reasoning\").",
+  "type": "string"
+}

First observedJul 17, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations provided, the description partially carries the burden. It discloses pricing behavior (free tier caps, credit deduction) but lacks details on rate limits, concurrency, error handling, or how the reasoning_effort parameter's chain-of-thought appears in the result. The transparency is adequate but not comprehensive.

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 two sentences, efficiently stating the core purpose and then adding pricing context. No irrelevant details. It could be slightly more front-loaded with the cost information, but overall it is concise and well-structured.

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?

Given the presence of an output schema, the description does not need to explain return values, but it omits guidance on streaming, context length, error scenarios, and how to interpret the reasoning meta. Although the tool has only 5 parameters and no nested objects, the description leaves gaps in completeness for a chat completion tool.

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 the input schema already documents all parameters. The tool description adds no additional meaning beyond what the schema provides. Baseline for high coverage is 3, and no extra value is contributed.

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 action ('Send a chat completion request') and the target resource ('aeX402 AI Router'). The verb 'send' and noun 'chat completion request' are specific and distinguish this tool from siblings, none of which appear to offer chat functionality.

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?

The description mentions free tier limits and paid credit deduction, providing some context on when cost is a factor. However, it does not explicitly state when to use this tool versus alternatives (e.g., other AI tools) or provide exclusion criteria. Usage guidance is implied but not fully elaborated.

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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