Skip to main content
Glama

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
NameRequiredDescriptionDefault
modelNoModel ID or alias: gpt-4o-mini (default), gpt-4o, claude-sonnet, claude-haiku, llama-70b, llama-8b, gemini-flash
messagesYesChat messages array [{role: "user"|"system"|"assistant", content: "..."}]
max_tokensNoMaximum tokens to generate (model-dependent default)
temperatureNoSampling temperature (0-2)
reasoning_effortNolow | 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
NameRequiredDescriptionDefault
idNo
modelNo
usageNoprompt/completion/total tokens
objectNoalways 'chat.completion'
choicesYes
createdNounix seconds

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.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation3/5

Several tool pairs overlap in function: aex402_quote vs aex402_buildSwap, search vs search_tokens vs discover_programs, describe_program vs reconstruct_abi, and mcp_linq vs agent_delegate. Descriptions attempt to differentiate, but boundaries are fuzzy and an agent could easily select the wrong one when uncertain.

Naming Consistency2/5

Naming is inconsistent: some tools use camelCase (aex402_buildSwap) while most use snake_case, verbs vary widely (build, get, list, quote, search, describe, discover, launch, delegate, chat), and there is no uniform verb_noun pattern. This makes predicting tool names difficult.

Tool Count2/5

32 tools is excessive for a single MCP server, exceeding the 25+ threshold. While the scope is broad (AMM, bridge, AI, RPC, discovery, launchpad), many tools could be consolidated (e.g., search tools) or are too fine-grained (multiple solana_get* tools).

Completeness4/5

Core workflows are well covered: AMM (quote/build/get/list), bridge (quote/status), AI (chat/delegate/linq), Solana and EVM RPCs, search/discovery plus health check, launchpad, and payment help. Minor gaps include no direct bridge history or AMM execution, but these are intentional in a non-custodial design.

Resources