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WarungCyber MCP Server

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JavaScript
Remote

warungcyber_chat_completion

Send chat prompts to selected WarungCyber AI models through an HTTP API, using an API key and token balance to return generated text and usage metrics.

Instructions

Send a chat completion prompt to a selected WarungCyber AI model via external HTTP API call. Consumes account token balance based on prompt and completion length. Requires an active WarungCyber API key (sk-wc-...). Returns generated text and token usage metrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesTarget AI model identifier to execute
apiKeyNoWarungCyber API key (starts with sk-wc-). Uses WARUNGCYBER_API_KEY environment variable if omitted.
promptYesThe user prompt, task instruction, or code snippet to complete
maxTokensNoMaximum completion tokens to generate (default: 2048)
temperatureNoSampling temperature between 0.0 and 2.0 (default: 0.7)
systemPromptNoOptional system instruction or persona definition

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv1.0.2
    • changedInput schema / properties / apiKey / description
      Previous value: -"WarungCyber API key (format: sk-wc-...). If omitted, uses WARUNGCYBER_API_KEY environment variable."New value: +"WarungCyber API key (starts with sk-wc-). Uses WARUNGCYBER_API_KEY environment variable if omitted."
    • changedInput schema / properties / maxTokens / description
      Previous value: -"Maximum number of tokens to generate in the completion (default: 2048)."New value: +"Maximum completion tokens to generate (default: 2048)"
    • changedInput schema / properties / model / description
      Previous value: -"The target AI model identifier. Choose 'claude-sonnet-4-6' or 'deepseek-reasoner' for coding/reasoning, 'venice-uncensored' for unrestricted security tasks, or 'gemini-3.1-pro' for massive context."New value: +"Target AI model identifier to execute"
    • changedInput schema / properties / prompt / description
      Previous value: -"The user prompt, task instruction, code snippet to refactor, or query to execute."New value: +"The user prompt, task instruction, or code snippet to complete"
    • changedInput schema / properties / systemPrompt / description
      Previous value: -"Optional system instruction or persona definition to guide the model's tone and output format."New value: +"Optional system instruction or persona definition"
    • changedInput schema / properties / temperature / description
      Previous value: -"Sampling temperature between 0.0 (deterministic) and 2.0 (creative). Default is 0.7."New value: +"Sampling temperature between 0.0 and 2.0 (default: 0.7)"
  2. Changed11 schema fields changedv1.0.1
    • changedInput schema / properties / apiKey / description
      Previous value: -"WarungCyber API key (optional if WARUNGCYBER_API_KEY env set)"New value: +"WarungCyber API key (format: sk-wc-...). If omitted, uses WARUNGCYBER_API_KEY environment variable."
    • changedInput schema / properties / maxTokens / description
      Previous value: -"Maximum completion tokens"New value: +"Maximum number of tokens to generate in the completion (default: 2048)."
    • addedInput schema / properties / maxTokens / maximum
      Added value: +32000
    • addedInput schema / properties / maxTokens / minimum
      Added value: +1
    • changedInput schema / properties / maxTokens / type
      Previous value: -"number"New value: +"integer"
    • changedInput schema / properties / model / description
      Previous value: -"Model ID (e.g. claude-sonnet-4-6, deepseek-reasoner, qwen-2.5-coder-32b, venice-uncensored, gemini-3.1-pro)"New value: +"The target AI model identifier. Choose 'claude-sonnet-4-6' or 'deepseek-reasoner' for coding/reasoning, 'venice-uncensored' for unrestricted security tasks, or 'gemini-3.1-pro' for massive context."
    • addedInput schema / properties / model / enum
      Added value: +[
+  "claude-sonnet-4-6",
+  "claude-sonnet-3-7",
+  "claude-opus-4-6-thinking",
+  "claude-3-haiku",
+  "deepseek-reasoner",
+  "deepseek-chat",
+  "qwen-2.5-coder-32b",
+  "qwen-2.5-72b",
+  "venice-uncensored",
+  "gemini-3.1-pro",
+  "gemini-3.8-flash",
+  "gemini-3.7-flash",
+  "gemini-3.6-flash",
+  "gemma-4-31b-it",
+  "gpt-4o-mini",
+  "llama-3.3-70b",
+  "gpt-oss-120b",
+  "atria-dawn-preview",
+  "gemini-2.5-flash-image"
+]
    • changedInput schema / properties / prompt / description
      Previous value: -"User prompt or instruction"New value: +"The user prompt, task instruction, code snippet to refactor, or query to execute."
    • addedInput schema / properties / prompt / minLength
      Added value: +1
    • changedInput schema / properties / systemPrompt / description
      Previous value: -"Optional system instruction"New value: +"Optional system instruction or persona definition to guide the model's tone and output format."
    • changedInput schema / properties / temperature / description
      Previous value: -"Sampling temperature (default: 0.7)"New value: +"Sampling temperature between 0.0 (deterministic) and 2.0 (creative). Default is 0.7."
  3. First observedv1.0.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: it makes an external HTTP call, consumes account token balance proportional to prompt and completion length, requires an sk-wc- prefixed API key, and returns generated text plus usage metrics. It stops short of error/failure behavior, rate limits, or what happens on insufficient balance.

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?

Three compact sentences with zero filler, front-loaded with the core action before cost, auth, and return-value details. Every sentence carries distinct information.

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?

With no output schema, the description usefully states that generated text and token usage metrics are returned. It covers cost and authentication, which is what an agent most needs before invoking a billable call, though failure modes and billing-exhaustion behavior are left unaddressed.

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 model, apiKey, prompt, maxTokens, temperature, and systemPrompt. The description adds no parameter-level detail beyond what the schema provides, which is the expected baseline.

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?

The description gives a specific verb and resource ('Send a chat completion prompt to a selected WarungCyber AI model') and scopes it as an external HTTP call. The action is unambiguous and clearly distinct from list_models, check_balance, and get_setup_guide, though no sibling is named explicitly, so full sibling differentiation credit is withheld.

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?

Usage context is implied rather than stated: the agent learns an active API key is required and that balance is consumed, which frames when the tool is usable. However, there is no explicit when-to-use/when-not-to-use guidance and no routing to alternatives such as check_balance before spending tokens.

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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