Linear

Let an agent read and change Linear issues, projects, teams, and comments. Linear gives the agent every tool Linear’s hosted MCP server offers, including tools that make changes. The credential you connect with decides what those tools can reach.

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.

Install and connect

pip

Terminal

pip install "pydantic-ai-harness[linear]" "pydantic-ai-slim[openai]"

uv

Terminal

uv add "pydantic-ai-harness[linear]" "pydantic-ai-slim[openai]"

Set LINEAR_ACCESS_TOKEN to a Linear API key or 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.linear import Linear

agent = Agent('openai:gpt-5.6-sol', capabilities=[Linear()])
result = agent.run_sync('Summarize the resources I can access')
print(result.output)

Per-user credentials

auth decides which Linear account each run uses:

auth	Account used
Not set, None, or ''	LINEAR_ACCESS_TOKEN. If that is not set either, creating the agent raises an error.
An API key or OAuth 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. The token it returns is used for that run. If it returns None or '', that run has no Linear tools. A function never uses LINEAR_ACCESS_TOKEN, and must not return 'oauth'.

A fixed token or LINEAR_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 Linear 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.linear import Linear


@dataclass
class Deps:
    linear_token: str | None


def linear_token(ctx: RunContext[Deps]) -> str | None:
    return ctx.deps.linear_token


agent = Agent('openai:gpt-5.6-sol', deps_type=Deps, capabilities=[Linear(auth=linear_token)])

Each run connects as its own user, so concurrent runs never share an account. read_only=True still applies to every run.

Your app gets each user’s token, stores it, and refreshes it. For example, a settings page where each user pastes their own Linear API key, or a “Connect Linear” button that signs them in with Linear OAuth and saves the access token to their account. Before each run, load it (this can be async) and put it in the deps; the function only reads it.

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 linear, so defer_loading=True works without one. To add more than one Linear to an agent, give each a distinct id and wrap them in PrefixTools, since their tool names are the same; two that share an id but differ raise an error.

Provider settings

read_only=True connects to Linear’s read-only endpoint instead of the full one. OAuth token scopes can limit access further.

Tool selection and approval

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.linear import Linear

capability = Linear()
agent = Agent(
    'openai:gpt-5.6-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.

Connection customization

Use auth in almost every case. Pass client only 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. With a client, read_only=True keeps only the tools the server marks as read-only. include_instructions=False stops the server’s own instructions from reaching the agent.

A client is one connection shared by every run; see Per-user credentials to connect each user separately. To use two connections whose tool names overlap, give them distinct ids and add PrefixTools.

Source