Spraay x402 MCP Server

spraay_compute_text_inference

Send chat messages to run text inference using multiple LLMs. Choose from 11 models priced between $0.003-$0.10 USDC per request.

Instructions

Run LLM text inference via Spraay Compute. 11 models across Chutes, Replicate, OpenRouter (DeepSeek, Llama, Qwen, Gemma). Costs $0.003-$0.10 USDC depending on model.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "messages": "<messages>"
    },
    "name": "spraay_compute_text_inference"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	Model ID (e.g. 'deepseek-ai/DeepSeek-V3-0324', 'auto' for cheapest). Use spraay_compute_models to list all.	auto
`messages`	Yes	Chat messages array
`max_tokens`	No	Maximum tokens to generate
`temperature`	No	Sampling temperature (0-2)

Output Schema

Name	Required	Description
`ok`	Yes	True when the gateway call succeeded; false when it returned an error.
`data`	No	The gateway response payload on success. The exact shape depends on the tool (see the tool description and the JSON in the text content block).
`error`	No	Human-readable error message, present only when ok is false.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv4.0.0Jun 27, 2026

TDQS

A4/5.0

Behavior4/5

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

Adds cost and provider details beyond annotations. Annotations already indicate non-read-only and non-destructive, but description provides cost range and model count, which aids decision-making.

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?

Two sentences, front-loaded with the main action, no wasted words. Efficient and clear.

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?

Covers main purpose, model sources, and cost. Output schema exists so return details not needed. Could mention error handling or rate limits but not essential for this moderate-complexity 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 coverage is 100%, so baseline 3. Description adds context on cost variation by model but does not elaborate on parameter specifics beyond schema.

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?

Clearly states the tool runs LLM text inference, specifies model sources and cost range. Distinguishes from sibling tools like image generation, embeddings, etc.

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?

Implied use for text inference but lacks explicit guidance on when to use over similar tools like spraay_chat or spraay_bittensor_chat_completions. No exclusions or alternatives mentioned.

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

Deploy Server