Context¶

::: enrichmcp.context.EnrichContext options: show_source: true show_bases: true show_root_heading: true

Overview¶

The EnrichContext class provides request-scoped utilities such as the cache system. It can be extended for additional dependencies as needed.

Current State¶

from enrichmcp import EnrichContext

# Current implementation is minimal
context = EnrichContext()

Capabilities¶

The context exposes a cache attribute for storing values across the request, user, or global scopes.

LLM Integration¶

Use ask_llm() (or the sampling() alias) to request completions from the client-side LLM. See the Server-Side LLM guide for more details:

from enrichmcp import prefer_fast_model, prefer_medium_model, prefer_smart_model

result = await ctx.ask_llm(
    "Summarize our latest sales numbers",
    model_preferences=prefer_fast_model(),
    max_tokens=200,
)
print(result.content.text)

User Elicitation¶

Call ask_user() when you need additional input from the client. It wraps the underlying MCP elicit() API:

class BookingPreferences(BaseModel):
    alternativeDate: str | None
    checkAlternative: bool = False

result = await ctx.ask_user(
    message="No tables available. Try another date?",
    schema=BookingPreferences,
)

Extending Context¶

For now, if you need context functionality, you can extend the base class:

from enrichmcp import EnrichContext


class MyContext(EnrichContext):
    """Custom context with database."""

    def __init__(self, db):
        super().__init__()
        self.db = db


# Use in resources
@app.retrieve
async def get_data() -> dict:
    ctx = app.get_context()
    # Use your custom context
    return await ctx.db.fetch_data()

Note: Dependency injection is no longer supported; always call app.get_context() to access the current request context.