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Documentation Index

Fetch the complete documentation index at: https://docs.mka1.com/llms.txt

Use this file to discover all available pages before exploring further.

Use the Responses resource when you want the MKA1 API to return text. Start with a plain string for simple prompts. Use message items when you need explicit roles or conversation state.

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Send a simple prompt

Pass a string in input for a single-turn request. The response includes generated text in output_text. 
mka1 llm responses create \
  --model auto \
  --input '"Write a one-sentence summary of the MKA1 API."' \
  -H 'X-On-Behalf-Of: <end-user-id>'
If you are not acting for an end user, omit X-On-Behalf-Of.

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Add instructions

Use instructions to define behavior before the model sees the user input. Keep instructions short and specific. 
mka1 llm responses create \
  --model auto \
  --instructions 'You are a support assistant. Reply in plain English. Keep answers under 80 words.' \
  --input '"Explain what embeddings are used for."'

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Send structured messages

Use an array of message items in input when you want explicit roles. Each message item uses type, role, and content. 
mka1 llm responses create --body '{
  "model": "auto",
  "input": [
    { "type": "message", "role": "developer", "content": "Answer as a technical writer. Keep the reply concise." },
    { "type": "message", "role": "user", "content": "Draft a short product update about faster response times." }
  ]
}'
This pattern is useful when you want the request body to carry the message history directly.

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Continue a multi-turn exchange

Use previous_response_id to continue from an earlier response without resending the full history. 
mka1 llm responses create \
  --model auto \
  --previous-response-id resp_123 \
  --input '"Now turn that into an email subject line."'
If you need a reusable conversation container, create one with the Conversations resource and then pass the conversation ID in conversation. 
# Create a conversation
mka1 llm conversations create --body '{
  "metadata": { "session_id": "web-42" }
}'

# Use the conversation in a response request
mka1 llm responses create \
  --model auto \
  --conversation conv_123 \
  --input '"What should I ask next to refine this draft?"'
See the Conversations and Responses pages in the API Reference for the full resource workflow.

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Stream text as it is generated

Set stream to true to receive server-sent events instead of waiting for the full response. 
mka1 llm responses create \
  --model auto \
  --input '"Write three release notes bullets for our docs update."' \
  --stream
Use streaming when you want to render partial output as it arrives.

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Next steps