PICA MCP Integration with Mastra

PICA provides a unified API platform that connects AI agents to third-party services (CRMs, email, calendars, databases, etc.) through MCP tool calling.

PICA MCP Server

PICA exposes its capabilities through an MCP server distributed as @picahq/mcp. It uses stdio transport — it runs as a local subprocess via npx.

MCP Configuration

{
  "mcpServers": {
    "pica": {
      "command": "npx",
      "args": ["@picahq/mcp"],
      "env": {
        "PICA_SECRET": "your-pica-secret-key"
      }
    }
  }
}

Package: @picahq/mcp (run via npx, no install needed)
Auth: PICA_SECRET environment variable (obtain from the PICA dashboard https://app.picaos.com/settings/api-keys)
Transport: stdio (standard input/output)

Environment Variable

Always store secrets in environment variables, never hardcode them:

PICA_SECRET=sk_test_...
OPENAI_API_KEY=sk-...

Add them to .env.local (or equivalent) and document in .env.example. Mastra auto-reads provider API keys from environment (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.).

Using PICA with Mastra

Mastra has first-class MCP support via the @mastra/mcp package. The MCPClient auto-detects transport type from your config — provide command/args for stdio, or url for HTTP/SSE.

Required packages

pnpm add @mastra/core @mastra/mcp

Before implementing: look up the latest docs

The Mastra API may change between versions. Always check the latest docs first:

Docs: https://mastra.ai/docs
MCP guide: https://mastra.ai/docs/mcp/overview
MCPClient reference: https://mastra.ai/reference/tools/mcp-client
GitHub: https://github.com/mastra-ai/mastra

Integration pattern

Create an MCPClient with command: "npx", args: ["@picahq/mcp"] — transport is auto-detected as stdio
List tools via await mcp.listTools() — returns tools in Mastra's format
Create an Agent with the tools and a model string ("provider/model-name")
Stream via agent.stream(messages, { maxSteps: 5 }) — the agent loop handles tool calls automatically
Iterate fullStream for typed chunks (text-delta, tool-call, tool-result) — all data lives on chunk.payload
Disconnect the MCP client when done via await mcp.disconnect()

When passing environment variables, spread process.env so the subprocess inherits PATH and other system vars:

env: {
  ...(process.env as Record<string, string>),
  PICA_SECRET: process.env.PICA_SECRET!,
}

Minimal example

import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";

const mcp = new MCPClient({
  id: "pica-mcp",
  servers: {
    pica: {
      command: "npx",
      args: ["@picahq/mcp"],
      env: {
        ...(process.env as Record<string, string>),
        PICA_SECRET: process.env.PICA_SECRET!,
      },
    },
  },
});

const tools = await mcp.listTools();

const agent = new Agent({
  id: "pica-assistant",
  name: "PICA Assistant",
  model: "openai/gpt-4o-mini",
  instructions: "You are a helpful assistant.",
  tools,
});

// Non-streaming
const result = await agent.generate("List my connected integrations");
console.log(result.text);

// Streaming
const stream = await agent.stream("List my connected integrations", {
  maxSteps: 5,
});

for await (const chunk of stream.fullStream) {
  if (chunk.type === "text-delta") {
    process.stdout.write(chunk.payload.text);
  } else if (chunk.type === "tool-call") {
    console.log("Tool called:", chunk.payload.toolName, chunk.payload.args);
  } else if (chunk.type === "tool-result") {
    console.log("Tool result:", chunk.payload.toolName, chunk.payload.result);
  }
}

await mcp.disconnect();

Streaming SSE events for a chat UI

When building a Next.js API route, stream responses as SSE events using a ReadableStream. Emit events in this format for compatibility with the PythonChat frontend component:

{ type: "text", content: "..." } — streamed text chunks
{ type: "tool_start", name: "tool_name", input: "..." } — tool execution starting
{ type: "tool_end", name: "tool_name", output: "..." } — tool execution result
{ type: "error", content: "..." } — error messages
data: [DONE] — stream finished

Key stream chunk types

Mastra's fullStream yields typed chunks where all data lives on chunk.payload:

Chunk Type	Payload Fields	Description
`text-delta`	`payload.text`	Streamed text content
`tool-call`	`payload.toolName`, `payload.toolCallId`, `payload.args`	Tool invocation
`tool-result`	`payload.toolName`, `payload.toolCallId`, `payload.result`, `payload.isError`	Tool output
`step-finish`	`payload.stepResult`, `payload.output`	Agent step completed
`finish`	`payload.stepResult`, `payload.output`, `payload.messages`	Stream finished
`error`	`payload.error`	Error occurred

Important: Chunk data is always on chunk.payload, not directly on the chunk. For example, use chunk.payload.text (not chunk.textDelta).

Model string format

Mastra uses "provider/model-name" strings — no separate provider packages needed:

model: "openai/gpt-4o-mini"
model: "anthropic/claude-4-5-sonnet"
model: "google/gemini-2.5-flash"

Static vs dynamic tools

Static (at agent creation): Merge MCP tools into the agent's tools:

const agent = new Agent({
  tools: await mcp.listTools(),
});

Dynamic (per request): Inject via toolsets at call time:

const result = await agent.generate("...", {
  toolsets: await mcp.listToolsets(),
});

Use dynamic toolsets when the MCP client is created per-request (e.g., with user-specific credentials).

Checklist

When setting up PICA MCP with Mastra:

pica-mastra