> For the complete documentation index, see [llms.txt](https://aesoperator.gitbook.io/aesoperator/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aesoperator.gitbook.io/aesoperator/features/mcp-protocol.md).

# MCP Protocol

Anthropic proposed a novel standard for AI agents to access data sources called the Model Context Protocol (MCP) You can read more about it [from Anthropic's blog here](https://docs.anthropic.com/en/docs/model-context-protocol). Aesoperator implements MCP to give its operators direct access to your systems and data while maintaining security and control.

## How MCP Works in Aesoperator

```python
from aesoperator import MCPServer, MCPClient

# Create an MCP server to expose your data
server = MCPServer(
    name="github-connector",
    data_sources=["repos", "issues", "pull_requests"],
    auth_config={"type": "oauth2"}
)

# Connect Aesoperator as an MCP client
client = MCPClient(
    server_url="http://localhost:8000",
    credentials={"access_token": "..."}
)
```

### Key Components

1. **MCP Servers**: Expose your data sources through a standardized API

* Code repositories (Git, GitHub)
* Documentation (Google Drive, Notion)
* Databases (Postgres, MongoDB)
* Web apps (via Puppeteer)

2. **MCP Clients**: AI agents that connect to MCP servers

* Aesoperator operators act as MCP clients
* Can access multiple data sources
* Maintain context across interactions

3. **Context Management**:

* Persistent memory across sessions
* Knowledge graph of relationships
* Semantic search capabilities

## Benefits of MCP in Aesoperator

1. **Universal Data Access**

* Single protocol for all data sources
* No custom integrations needed
* Standardized authentication

2. **Context Awareness**

* Operators maintain state across calls
* Can reference previous interactions
* Build knowledge over time

3. **Security & Control**

* Fine-grained access control
* Audit logging
* Rate limiting

## Example: GitHub Integration

```python
# Setup GitHub MCP server
github_server = MCPServer(
    name="github",
    repo="username/repo",
    access_token="..."
)

# Create Aesoperator task with MCP context
task = aesop.Task(
    name="code_review",
    mcp_context=[github_server],
    inputs={
        "pr_number": 123,
        "review_type": "security"
    }
)

# Operator can now access GitHub data
result = aesop.execute_task(task)
```

## Example: Database Integration

```python
# Setup Postgres MCP server
db_server = MCPServer(
    name="analytics_db",
    connection_string="postgresql://...",
    allowed_tables=["users", "events"]
)

# Analyze data with MCP context
task = aesop.Task(
    name="user_analysis",
    mcp_context=[db_server],
    inputs={
        "metric": "retention",
        "date_range": "last_30_days"
    }
)

result = aesop.execute_task(task)
```

## Available MCP Actions

```python
# Query data source
mcp_query(
    server: str,
    query: str,
    params: Dict = None
) -> Dict

# Update data
mcp_update(
    server: str,
    operation: str,
    data: Dict
) -> None

# Stream changes
mcp_subscribe(
    server: str,
    event_type: str,
    handler: Callable
) -> None
```

## Best Practices

1. **Security**

* Use minimal access permissions
* Rotate credentials regularly
* Monitor usage patterns

2. **Performance**

* Cache frequently accessed data
* Use efficient queries
* Implement rate limiting

3. **Reliability**

* Handle connection errors
* Implement retries
* Monitor server health

## Learn More

* [MCP Specification](https://mcp.dev/spec)
* [Building MCP Servers](https://github.com/banbera/gitbook/blob/master/docs/features/servers.md)
* [MCP Security Guide](https://github.com/banbera/gitbook/blob/master/docs/features/security.md)
* [Example Implementations](https://github.com/banbera/gitbook/blob/master/docs/features/examples.md)


---

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