# Summarize Text — 302.AI

> Summarize long-form text into concise, readable output using the 302.AI Chat API. Great for reports, content digestion, and executive briefs. See documentation

- Key: `_302_ai-summarize-text`
- Type: Action (Read-only)
- Version: 0.0.1
- App: 302.AI (`_302_ai`) — https://pipedream.com/apps/302-ai.md
- This page (HTML): https://pipedream.com/apps/302-ai/actions/summarize-text
- Hints: read-only · open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/_302_ai/actions/summarize-text/summarize-text.mjs

## Description

Summarize long-form text into concise, readable output using the 302.AI Chat API. Great for reports, content digestion, and executive briefs. [See documentation](https://doc.302.ai/147522039e0)

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `modelId` | `string` | Yes | The ID of the model to use for chat completions Options are loaded from the connected account. |
| `maxTokens` | `string` | No | The maximum number of tokens to generate in the completion. |
| `temperature` | `string` | No | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. |
| `topP` | `string` | No | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. |
| `n` | `string` | No | How many completions to generate for each prompt |
| `stop` | `string[]` | No | Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. |
| `presencePenalty` | `string` | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. |
| `frequencyPenalty` | `string` | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. |
| `user` | `string` | No | A unique identifier representing your end-user, which can help monitor and detect abuse. |
| `text` | `string` | Yes | The text to summarize |
| `length` | `string` | No | The length of the summary |

## Run it

**MCP**

```ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js"
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js"
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const accessToken = await pd.rawAccessToken

const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": process.env.PIPEDREAM_PROJECT_ID!,
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "_302_ai",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
  name: "_302_ai-summarize-text",
  arguments: {
    modelId: "Model",
    maxTokens: "Max Tokens",
  },
})
```

**TypeScript**

```ts
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "_302_ai-summarize-text",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    _302_ai: { authProvisionId: "apn_xxxxxxx" },
    modelId: "Model",
    maxTokens: "Max Tokens",
  },
})

console.log(result)
```

**cURL**

```bash
curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "_302_ai-summarize-text",
    "configured_props": {
      "_302_ai": { "authProvisionId": "apn_xxxxxxx" },
      "modelId": "Model",
      "maxTokens": "Max Tokens"
    }
  }'
```

---

- App: https://pipedream.com/apps/302-ai.md · All apps: https://pipedream.com/apps