Notion
Let an agent search and change content in a Notion workspace. Notion gives the agent every tool Notion’s hosted MCP server offers, including tools that make changes. The agent acts with the permissions of the Notion user it connects as.
While Pydantic AI Harness is on 0.x releases, the API may change between minor releases; when it does, deprecation warnings and release-note migration guidance tell you (or your agent) exactly how to upgrade. See the version policy.
pip install "pydantic-ai-harness[notion]" "pydantic-ai-slim[openai]"
uv add "pydantic-ai-harness[notion]" "pydantic-ai-slim[openai]"
Set NOTION_ACCESS_TOKEN to a Notion OAuth access token, or pass auth= a token. On your own machine, auth='oauth' signs you in through the browser instead. See the provider setup.
from pydantic_ai import Agent
from pydantic_ai_harness.notion import Notion
agent = Agent('openai:gpt-6.1-sol', capabilities=[Notion()])
result = agent.run_sync('Summarize the resources I can access')
print(result.output)
auth decides which Notion account each run uses:
auth | Account used |
|---|---|
Not set, None, or '' | NOTION_ACCESS_TOKEN. If that is not set either, creating the agent raises an error. |
| An access token | That token, for every run. |
'oauth' | The account you sign in to through the browser. This only works on your own machine. |
| A function | Called at the start of each run, and can be async. The token it returns is used for that run. If it returns None or '', that run has no Notion tools. A function never uses NOTION_ACCESS_TOKEN, and must not return 'oauth'. |
A fixed token or NOTION_ACCESS_TOKEN suits a script or an agent on your own machine, where every run is the same account.
In an app where each user connects their own Notion account, one agent serves all of them, so the token cannot be fixed when the agent is created. Pass a function that reads the current user’s token from the run’s deps:
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
from pydantic_ai_harness.notion import Notion
@dataclass
class Deps:
notion_token: str | None
def notion_token(ctx: RunContext[Deps]) -> str | None:
return ctx.deps.notion_token
agent = Agent('openai:gpt-6.1-sol', deps_type=Deps, capabilities=[Notion(auth=notion_token)])
Each run connects as its own user, so concurrent runs never share an account. read_only applies to every user.
Your app gets each user’s token, stores it, and refreshes it. For example, a “Connect Notion” button that signs them in with Notion OAuth and saves the access token to their account. Before each run, put it in the deps, or load it in an async auth function.
With durable execution such as Temporal, read the credential from the run’s deps rather than from a global, since the function may run in another process. The capability’s id defaults to notion, so defer_loading=True works without one. To add more than one Notion to an agent, give each a distinct id and tool_prefix; two that share an id but differ raise an error.
Connect with a Notion OAuth access token. Notion integration tokens are a different kind of credential and do not work with the hosted MCP server. Notion decides which pages and tools the user can reach. Some search and connected-source tools need a matching Notion plan and permissions.
Notion has no default prefix, unlike most hosted MCP capabilities: the server’s tool names already start with the service, as in notion-search, so a default prefix would repeat it (notion_notion-search). The agent sees the names as they are. Set tool_prefix to put a prefix and _ in front of them, for example tool_prefix='work' gives work_notion-search.
read_only selects tools by what the server marks as read-only, not by name, so the prefix does not change what it keeps. A toolset wrapper on capability.get_toolset(), such as filtered(), sees the names the agent sees, prefix included.
read_only=True keeps only the tools the server marks as read-only. If the server does not mark its read tools, the agent gets none and emits an MCPReadOnlyNoToolsWarning. The credential is still what controls access.
To filter tools or require approval in your application, wrap the toolset with the existing toolset wrappers. For example, this asks for approval before every tool call:
from pydantic_ai import Agent
from pydantic_ai.messages import DeferredToolRequests
from pydantic_ai_harness.notion import Notion
capability = Notion()
agent = Agent(
'openai:gpt-6.1-sol',
toolsets=[capability.get_toolset().approval_required()],
output_type=[str, DeferredToolRequests],
)
Handle the approval requests with the deferred tools workflow. To cap the size of tool output, add Tool Output Limits.
Pass client when you need control of the connection itself: your own FastMCP client or transport, for example one with a different authentication scheme, a proxy, or MCP handlers. The client then owns the URL and authentication, so passing client together with auth raises an error. read_only still applies. include_instructions=False stops the server’s instructions from reaching the model.
A client is one connection shared by every run; see Per-user credentials to connect each user separately. To use two connections, give each a distinct id and tool_prefix.