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WarungCyber MCP Server

by d4rks0ul555
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

warungcyber_list_models

Retrieve available AI models from the WarungCyber AI Gateway with context window limits, token pricing in USD and IDR, and capability filters for choosing the right model.

Instructions

List available AI models from the WarungCyber AI Gateway, including context window limits, token pricing (USD and IDR), and capability categories. Read-only operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format for the model list (default: markdown)
categoryNoFilter models by capability category

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.2
    • changedInput schema / properties / category / description
      Previous value: -"Filter models by capability"New value: +"Filter models by capability category"
    • changedInput schema / properties / format / description
      Previous value: -"Output formatting (default: markdown)"New value: +"Output format for the model list (default: markdown)"
    • changedInput schema / properties / format / enum
      Previous value: -[
-  "json",
-  "markdown"
-]New value: +[
+  "markdown",
+  "json"
+]
  2. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden and directly states 'Read-only operation,' which is the key safety trait an agent needs. It also discloses the returned data categories, though it does not address authentication requirements or rate limits.

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 core action and followed by a brief content summary and safety note. Every clause earns its place with no redundancy.

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 simple, parameter-light list tool with no output schema and no annotations, the description covers the essential purpose, scope, returned data, and read-only nature. It is adequately complete, though it could optionally mention the format parameter or when to prefer this over directly calling chat_completion.

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 both the format and category parameters. The description references 'capability categories' which loosely maps to the category filter, but adds no syntax or format details beyond what the schema provides, so the baseline of 3 applies.

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 uses the specific verb 'List' with the resource 'available AI models from the WarungCyber AI Gateway.' It also enumerates the returned data types (context window limits, token pricing, capability categories), making it clearly distinct from siblings like check_balance, get_setup_guide, and chat_completion.

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 implies usage by stating what the tool returns, but it does not explicitly say when to use it versus alternatives or when not to use it. No alternatives are named, so the agent must infer that this is the tool for discovering available models.

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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