Protocol Features

This page covers cross-cutting MCP protocol features.

MCP Primitives

The MCP protocol defines three core primitives that servers can implement:

Primitive	Control	Description	Example Use
Prompts	User-controlled	Interactive templates invoked by user choice	Slash commands, menu options
Resources	Application-controlled	Contextual data managed by the client application	File contents, API responses
Tools	Model-controlled	Functions exposed to the LLM to take actions	API calls, data updates

Server Capabilities

MCP servers declare capabilities during initialization:

Capability	Feature Flag	Description
`prompts`	`listChanged`	Prompt template management
`resources`	`subscribe`
`listChanged`	Resource exposure and updates
`tools`	`listChanged`	Tool discovery and execution
`logging`	-	Server logging configuration
`completions`	-	Argument completion suggestions

Ping

Both clients and servers can send ping requests to check that the other side is responsive:

python

# From a client
result = await session.send_ping()

# From a server (via ServerSession)
result = await server_session.send_ping()

Both return an EmptyResult on success. If the remote side does not respond within the session timeout, an exception is raised.

Cancellation

Either side can cancel a previously-issued request by sending a CancelledNotification:

python

import mcp.types as types
from mcp import ClientSession


async def cancel_request(session: ClientSession) -> None:
    """Send a cancellation notification for a previously-issued request."""
    await session.send_notification(
        types.ClientNotification(
            types.CancelledNotification(
                params=types.CancelledNotificationParams(
                    requestId="request-id-to-cancel",
                    reason="User navigated away",
                )
            )
        )
    )

Full example: examples/snippets/clients/cancellation.py

The CancelledNotificationParams fields:

requestId (optional): The ID of the request to cancel. Required for non-task cancellations.
reason (optional): A human-readable string describing why the request was cancelled.

Capability Negotiation

During initialization, the client and server exchange capability declarations. The Python SDK automatically declares capabilities based on which callbacks and handlers are registered:

Client capabilities (auto-declared when callbacks are provided):

sampling -- declared when sampling_callback is passed to ClientSession
roots -- declared when list_roots_callback is passed to ClientSession
elicitation -- declared when elicitation_callback is passed to ClientSession

Server capabilities (auto-declared when handlers are registered):

prompts -- declared when a list_prompts handler is registered
resources -- declared when a list_resources handler is registered
tools -- declared when a list_tools handler is registered
logging -- declared when a set_logging_level handler is registered
completions -- declared when a completion handler is registered

After initialization, clients can inspect server capabilities:

python

capabilities = session.get_server_capabilities()
if capabilities and capabilities.tools:
    tools = await session.list_tools()

Protocol Version Negotiation

The SDK defines LATEST_PROTOCOL_VERSION and SUPPORTED_PROTOCOL_VERSIONS in mcp.shared.version:

python

from mcp.shared.version import LATEST_PROTOCOL_VERSION, SUPPORTED_PROTOCOL_VERSIONS

# LATEST_PROTOCOL_VERSION is the version the SDK advertises during initialization
# SUPPORTED_PROTOCOL_VERSIONS lists all versions the SDK can work with

During initialization, the client sends LATEST_PROTOCOL_VERSION. If the server responds with a version not in SUPPORTED_PROTOCOL_VERSIONS, the client raises a RuntimeError. This ensures both sides agree on a compatible protocol version before exchanging messages.

JSON Schema (2020-12)

MCP uses JSON Schema 2020-12 for tool input schemas, output schemas, and elicitation schemas. When using Pydantic models, schemas are generated automatically via model_json_schema():

python

from pydantic import BaseModel, Field


class SearchParams(BaseModel):
    query: str = Field(description="Search query string")
    max_results: int = Field(default=10, description="Maximum results to return")


# Pydantic generates a JSON Schema 2020-12 compatible schema:
schema = SearchParams.model_json_schema()
# {
#     "properties": {
#         "query": {"description": "Search query string", "type": "string"},
#         "max_results": {
#             "default": 10,
#             "description": "Maximum results to return",
#             "type": "integer",
#         },
#     },
#     "required": ["query"],
#     "title": "SearchParams",
#     "type": "object",
# }

Full example: examples/snippets/servers/json_schema_example.py

For FastMCP tools, input schemas are derived automatically from function signatures. For structured output, the output schema is derived from the return type annotation.

Pagination

For pagination details, see:

Server-side implementation: Low-Level Server - Pagination
Client-side consumption: Low-Level Server - Client-side Consumption