How to integrate Textrazor MCP with Mastra AI

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Introduction

This guide walks you through connecting Textrazor to Mastra AI using the Composio tool router. By the end, you'll have a working Textrazor agent that can extract named entities from this news article, summarize key phrases in customer reviews, classify support tickets by topic automatically, analyze grammatical structure of this sentence through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Textrazor account through Composio's Textrazor MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Textrazor tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Textrazor tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Textrazor agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Textrazor MCP server, and what's possible with it?

The Textrazor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Textrazor account. It provides structured and secure access to advanced natural language processing features, so your agent can extract entities, classify content, analyze grammar, and understand relationships within text—all automatically and at scale.

  • Entity and relationship extraction: Enable your agent to identify and classify people, places, organizations, and relationships from any text, powering intelligent content analysis and knowledge graph building.
  • Text classification and categorization: Automatically categorize documents, articles, or snippets using built-in or custom classifiers, making it easy to sort and organize large volumes of text data.
  • Grammatical and dependency analysis: Let your agent parse sentence structure, analyze grammatical relationships, and build dependency trees to support advanced linguistic understanding and text analytics.
  • Custom dictionary and classifier management: Allow the agent to create and update custom entity dictionaries and classifiers, tailoring analysis to specialized domains or business needs.
  • Phrase extraction and sentiment detection: Extract key phrases, multi-word expressions, and even detect logical entailments or word senses, enabling deeper insights from any written content.

Supported Tools & Triggers

Tools
Get Account InformationThis tool retrieves comprehensive information about a textrazor account, providing essential details about the account's status, usage, and limits.
Classify TextThis tool will classify text into predefined categories using textrazor's classification capabilities.
Manage Custom ClassifiersThis tool manages custom classifiers in textrazor, allowing users to create, update, and manage custom classification categories.
Analyze Dependency TreesThe dependencytreesaction analyzes the grammatical relationships between words in text by creating dependency trees.
Dictionary ManagerThe textrazor dictionary manager tool allows users to create, update, and manage custom entity dictionaries in textrazor.
Extract Entailments from TextThis tool extracts entailments from text using textrazor's api.
Extract Named Entities from TextExtract named entities (people, places, companies, etc.
Extract Phrases from TextThe extractphrases action extracts meaningful phrases from input text using textrazor's phrase extraction capability.
Extract Grammatical Relations from TextThis tool extracts grammatical relations between words in the text.
Extract Word SensesThis tool performs word sense disambiguation on the input text by identifying the most likely meanings of words in context.
Spelling CorrectionThis tool performs spelling correction on the provided text using textrazor's deep spelling correction system.
Analyze Content with TextRazorA comprehensive content analysis tool that combines multiple textrazor extractors to perform a complete analysis of the input text.
Extract Topics from TextA tool to extract topics from text using textrazor's topic extraction capabilities.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Textrazor through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Textrazor

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["textrazor"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Textrazor MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "textrazor" for Textrazor access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Textrazor toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "textrazor-mastra-agent",
    instructions: "You are an AI agent with Textrazor tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        textrazor: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Textrazor toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Textrazor and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["textrazor"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      textrazor: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "textrazor-mastra-agent",
    instructions: "You are an AI agent with Textrazor tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { textrazor: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Textrazor through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

How to build Textrazor MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Textrazor MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Textrazor MCP with Claude Agent SDK

Google ADK

Google ADK

Use Textrazor MCP with Google ADK

LangChain

LangChain

Use Textrazor MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Textrazor MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Textrazor MCP with LlamaIndex

CrewAI

CrewAI

Use Textrazor MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Textrazor MCP?

With a standalone Textrazor MCP server, the agents and LLMs can only access a fixed set of Textrazor tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Textrazor and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Mastra AI?

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Textrazor tools.

Can I manage the permissions and scopes for Textrazor while using Tool Router?

Yes, absolutely. You can configure which Textrazor scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Textrazor data and credentials are handled as safely as possible.

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