302.AI ACTION
Chat using Functions
Enable your 302.AI model to invoke user-defined functions. Useful for conditional logic, workflow orchestration, and tool invocation within conversations. See documentation
- Action
- Writes data
- API key
- SDK
- MCP
IMPLEMENTATION
Call this tool
Connect a user's 302.AI account once, then configure and run Chat using Functions from your backend or agent.
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-chat-using-functions",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
_302_ai: { authProvisionId: "apn_xxxxxxx" },
modelId: "Model",
input: "Chat Input",
},
})
console.log(result)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-chat-using-functions",
"configured_props": {
"_302_ai": { "authProvisionId": "apn_xxxxxxx" },
"modelId": "Model",
"input": "Chat Input"
}
}'// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{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-chat-using-functions",
arguments: {
modelId: "Model",
input: "Chat Input",
},
})SCHEMA
Inputs
Pipedream supplies the connected account. Your application provides the operation-specific values below. Dynamic inputs are resolved against that user's account.
| Property | Type | Description |
|---|---|---|
modelId Model | string | The ID of the model to use for chat completions Required Dynamic |
input Chat Input | string | Text input to the model used to generate a response Required |
functions Functions | string | A valid JSON array of tools/functions using the OpenAI function schema definition. Each tool must have a type property set to "function" and a function object with name, description, and parameters. Required |
instructions Instructions | string | System instructions for the model Optional |
toolChoice Tool Choice | string |
Optional |
parallelToolCalls Parallel Function Calling | string | Allow or prevent the model to call multiple functions in a single turn Optional |
maxTokens Max Tokens | string | The maximum number of tokens to generate in the completion. Optional |
temperature Temperature | string | 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. Optional |
responseFormat Response Format | string |
Optional |
REFERENCE
Tool details
Behavior hints are published with the component in the Pipedream registry and surface as MCP tool annotations, so an agent can reason about a tool before it calls it.
- Registry key
- _302_ai-chat-using-functions
- Version
- 0.0.1
- App
- 302.AI
- Authentication
- API key
- Read-only
- No
- Destructive
- No
- Open world
- Yes
- Source
- View on GitHub ↗