> ## Documentation Index
> Fetch the complete documentation index at: https://openrewardstandard.io/llms.txt
> Use this file to discover all available pages before exploring further.

# ORS vs MCP

> Comparing Open Reward Standard and Model Context Protocol

The Model Context Protocol (MCP) and Open Reward Standard (ORS) are both protocols for connecting language models to external systems, but they serve different purposes.

## Overview

**Model Context Protocol (MCP)**:

* 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

**Open Reward Standard (ORS)**:

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

## Key Differences

| Feature                 | MCP                           | ORS                               |
| ----------------------- | ----------------------------- | --------------------------------- |
| **Primary Purpose**     | Tool access, data integration | RL training environments          |
| **Episode Termination** | No concept                    | `finished` signal                 |
| **Rewards**             | No concept                    | Numeric feedback for RL           |
| **Tasks**               | No concept                    | Organised problems to solve       |
| **Splits**              | No concept                    | Tasks organised into splits       |
| **Session Management**  | Basic                         | Episode-centric (RL trajectories) |
| **Tool Calling**        | Yes                           | Yes Yes                           |
| **Protocol**            | JSON-RPC over stdio/SSE       | HTTP/REST + SSE                   |

## Detailed Comparison

### Tool Calling

Both protocols support tool calling with similar interfaces:

**MCP Tool Spec**:

```json theme={null}
{
  "name": "read_file",
  "description": "Read contents of a file",
  "inputSchema": {
    "type": "object",
    "properties": {
      "path": {"type": "string"}
    }
  }
}
```

**ORS Tool Spec**:

```json theme={null}
{
  "name": "read_file",
  "description": "Read contents of a file",
  "input_schema": {
    "type": "object",
    "properties": {
      "path": {"type": "string"}
    }
  }
}
```

ORS intentionally aligns with MCP's tool specification format.

### Tool Responses

**MCP Response**:

```json theme={null}
{
  "content": [
    {"type": "text", "text": "File contents here"}
  ]
}
```

**ORS Response**:

```json theme={null}
{
  "blocks": [
    {"type": "text", "text": "File contents here"}
  ],
  "reward": 0.0,
  "finished": false
}
```

ORS adds:

* `reward` - For RL training feedback
* `finished` - For episode termination

### Episode Structure

**MCP**: No concept of episodes. Stateless or loosely stateful tool calls.

**ORS**: Episodes are first-class:

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

### Task Organization

**MCP**: No built-in task organization.

**ORS**: Tasks and splits:

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

## Next Steps

<CardGroup cols={2}>
  <Card title="ORS Quick Start" icon="rocket" href="/quickstart">
    Build your first ORS server
  </Card>

  <Card title="ORS Specification" icon="book" href="/specification/overview">
    Deep dive into ORS protocol
  </Card>

  <Card title="MCP Documentation" icon="external-link" href="https://modelcontextprotocol.io">
    Learn about Model Context Protocol
  </Card>

  <Card title="Implementation Guide" icon="code" href="/guides/implementing-server">
    Implement an ORS server
  </Card>
</CardGroup>

***

**Key Takeaway**: MCP and ORS solve different problems. MCP connects LLMs to tools. ORS connects agents to RL training environments. Both are valuable, and they can work together in sophisticated systems.