ORS vs MCP

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Overview

• Purpose: Connect LLMs to tools, data sources, and workflows
• Focus: General-purpose tool access
• Use case: Extending LLM capabilities with external APIs, databases, file systems

• Purpose: Connect agents to reinforcement learning environments
• Focus: RL training and agent evaluation
• Use case: Training agents with reward signals, structured evaluation benchmarks

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Key Differences

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Detailed Comparison

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Tool Calling

{
  "name": "read_file",
  "description": "Read contents of a file",
  "inputSchema": {
    "type": "object",
    "properties": {
      "path": {"type": "string"}
    }
  }
}

{
  "name": "read_file",
  "description": "Read contents of a file",
  "input_schema": {
    "type": "object",
    "properties": {
      "path": {"type": "string"}
    }
  }
}

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Tool Responses

{
  "content": [
    {"type": "text", "text": "File contents here"}
  ]
}

{
  "blocks": [
    {"type": "text", "text": "File contents here"}
  ],
  "reward": 0.0,
  "finished": false
}

• reward - For RL training feedback
• finished - For episode termination

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Episode Structure

• Session = RL episode
• Episode continues until finished: true
• One complete trajectory through environment
• Clear start (task) and end (finished signal)

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Task Organization

• Tasks: Individual problems to solve
• Splits: tasks grouped into splits, e.g. train/val/test

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Next Steps