# Choose how to use Signals attributes
> Choose how your application, agent, or model reads Signals data: services, agentic contexts, the Signals MCP server, interventions, or training datasets.
> Source: https://docs.snowplow.io/docs/signals/applications/
> Documentation index: https://docs.snowplow.io/llms.txt

Once you've [defined and published attribute groups](/docs/signals/attributes/), several kinds of consumer can read what Signals calculates. Applications can pull values when they need them, or Signals can push an intervention when a user meets your criteria. [Pull and push](/docs/signals/concepts/pull-and-push/) compares the two patterns.

Use this table to find the page for what reads the data:

| What reads it                                            | What it gets                                                                    | Use                                                                                                                                                                     | Interfaces                                  |
| -------------------------------------------------------- | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------- |
| Application code                                         | Calculated attribute values for a user or session                               | [Services](/docs/signals/applications/services/), then [retrieve attributes](/docs/signals/applications/retrieve-attributes/). Services are recommended for production. | Python SDK, Node.js SDK, Signals API        |
| An agent that needs recent session activity and a prompt | The session's latest events, up to 100 events or 1 hour, as JSON or a narrative | [Agentic contexts](/docs/signals/applications/agentic-contexts/), scoped to `domain_sessionid`                                                                          | Python SDK, Node.js SDK, Signals MCP server |
| An agent that chooses what to read at runtime            | Published attribute groups and agentic contexts, as tools                       | [Signals MCP server](/docs/signals/applications/mcp-server/)                                                                                                            | Any MCP client                              |
| An application that reacts when criteria are met         | An intervention payload, pushed as soon as the criteria are met                 | [Define interventions](/docs/signals/interventions/), then [subscribe to them](/docs/signals/applications/subscribe/)                                                   | Python SDK, browser plugin, Signals API     |
| Offline model training                                   | Point-in-time attribute values for labeled anchors, in a warehouse table        | [Build training datasets](/docs/signals/ml-training-datasets/)                                                                                                          | Python SDK                                  |

## Agents

An agent can read Signals data in two ways. To ground it in what the user is doing in the current session, retrieve an [agentic context](/docs/signals/applications/agentic-contexts/) and pass it to the model. To let the agent list and read published attribute groups and agentic contexts itself, add the [Signals MCP server](/docs/signals/applications/mcp-server/) to its tools.

For context about a logged-in user across sessions, use an attribute group keyed on `user_id` or `domain_userid`; see [identity resolution](/docs/signals/attributes/identity-resolution/).