RanchHand

openai_chat_completions

Generate AI chat responses using OpenAI-compatible APIs. Send messages to AI models for conversational interactions, content creation, and automated responses through the RanchHand MCP server.

Instructions

Create chat completion (POST /v1/chat/completions).

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": "openai_chat_completions"
  }
}

Input Schema

Name	Required	Description	Default
`max_tokens`	No
`messages`	Yes
`model`	No
`stream`	No
`temperature`	No
`top_p`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 4, 2025

TDQS

D1.7/5.0

Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('create chat completion') without mentioning any behavioral traits such as authentication requirements, rate limits, cost implications, response format, or error handling. This is inadequate for a tool that likely involves API calls with significant operational considerations.

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?

The description is extremely concise with a single sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it front-loaded and efficient in structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of an OpenAI chat completions API tool with 6 parameters, no annotations, and no output schema, the description is severely incomplete. It fails to explain the tool's behavior, parameter usage, or expected outcomes, leaving critical gaps for an AI agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning none of the 6 parameters (max_tokens, messages, model, stream, temperature, top_p) are documented in the schema. The description adds no information about what these parameters mean, their expected formats, or how they affect the chat completion. This leaves all parameters semantically undefined.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create chat completion (POST /v1/chat/completions)' restates the name/title with minimal elaboration. It specifies the verb 'create' and resource 'chat completion', but lacks specificity about what a chat completion entails or how it differs from sibling tools like embeddings creation or model listing. This is a tautological description that provides little additional insight beyond the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like openai_embeddings_create or openai_models_list. It does not mention any context, prerequisites, or exclusions for usage. This absence of guidance leaves the agent without direction on appropriate tool selection.

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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