ai-netcafe

Run a prompt on a specific LLM

ask_model

Send a prompt to one specific large language model and get the answer plus measured platform cost metadata. The beta platform covers the user charge ($0.00); capacity limits still apply. Example — GET https://ainetcafe.com/t/ask_model?prompt=Say+hi&model=deepseek-v4-flash

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": {
      "prompt": "<prompt>"
    },
    "name": "ask_model"
  }
}

Input Schema

Name	Required	Description
`model`	No	Model id. Call list_models for available ids. Defaults to a cheap capable model.
`prompt`	Yes	The prompt to send.
`system`	No	Optional system instruction.
`max_tokens`	No	Optional output cap.

Output Schema

Name	Required	Description	Default
`model`	No
`answer`	No
`cost_usd`	No
`latency_ms`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJul 31, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "type": "string"
+    },
+    "cost_usd": {
+      "type": "number"
+    },
+    "latency_ms": {
+      "type": "number"
+    },
+    "model": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedJul 29, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations declare readOnly=false, destructive=false, idempotent=false, and openWorld=false, so the safety profile is covered. The description adds meaningful context beyond that: it returns measured cost metadata, the beta platform currently covers the user charge ($0.00), and capacity limits still apply – useful operational detail an agent would not get from annotations.

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 tight sentences: purpose first, then cost/capacity caveat, then a concrete example. Front-loaded and no wasted phrasing, though the pricing detail is somewhat tangential to invocation.

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?

An output schema exists, so return values need not be explained; the description still flags that cost metadata comes back. Cost, capacity limits, and an example cover the essentials for a single-prompt tool, leaving only the model-vs-sibling routing lightly implied.

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 schema already documents all four parameters (model default, prompt, system, max_tokens). The example URL illustrates prompt and model usage but adds no semantics beyond what the schema provides, so 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?

States a specific verb and resource: send a prompt to one specific LLM and get the answer plus cost metadata. The phrase 'one specific large language model' implicitly distinguishes it from compare_models, but the contrast is left for the agent to infer rather than named.

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 'one specific' framing hints at when this is appropriate versus a multi-model tool like compare_models, and the note that capacity limits apply signals a constraint. However, no alternative is named explicitly and there is no clear when-not-to-use guidance.

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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