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How to integrate Semanticscholar MCP with Mastra AI

Connect Mastra AI to Semanticscholar MCP. Find the latest papers on graph neural networks, list citations for a specific research paper, and more using natural language, with authentication handled for you.

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Semantic Scholar is an AI-powered academic search engine for scientific literature. It helps researchers quickly discover, analyze, and understand research papers across disciplines.

20 Tools

Introduction

This guide walks you through connecting Semanticscholar to Mastra AI using the Composio tool router. By the end, you'll have a working Semanticscholar agent that can search papers by title or relevance, look up authors, trace citations and references, find paper recommendations, and search text snippets through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Semanticscholar with

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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 Semanticscholar tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Semanticscholar 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 Semanticscholar 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 Semanticscholar MCP server, and what's possible with it?

The Semantic Scholar MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Semantic Scholar account. It provides structured and secure access to academic papers, author profiles, citations, recommendations, and dataset releases, so your agent can search literature, examine research, explore authors, find related papers, and retrieve dataset information on your behalf.

  • Academic paper discovery: Have your agent search papers by relevance, run bulk literature searches, find a paper by title, complete partial queries, or locate matching text snippets within papers.
  • Paper details and authorship: Let the agent retrieve details about one or more papers, identify their authors, and review key information about each work.
  • Citations and references: Direct your agent to inspect the papers that cite a study and the references used by that study.
  • Author research: Instruct your agent to search for researchers by name, review author profiles and publication statistics, or examine papers written by selected authors.
  • Recommendations and research datasets: Have your agent find related papers from positive and negative examples, list dataset releases, review release details, and retrieve dataset downloads or incremental updates.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

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
2

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 Semanticscholar through MCP.
3

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
4

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
5

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
6

Create a Tool Router session for Semanticscholar

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

  const composioMCPUrl = session.mcp.url;
  console.log("Semanticscholar MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "semanticscholar" for Semanticscholar access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

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 Semanticscholar toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "semanticscholar-mastra-agent",
    instructions: "You are an AI agent with Semanticscholar 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
9

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: {
        semanticscholar: 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 Semanticscholar 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 Semanticscholar 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: ["semanticscholar"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "semanticscholar-mastra-agent",
    instructions: "You are an AI agent with Semanticscholar 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: { semanticscholar: 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 Semanticscholar 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
TOOLS

Supported Tools

Every Semanticscholar action and event your agent gets out of the box.

Details about an author

Retrieve detailed information about an author from Semantic Scholar, including name, affiliations, publication statistics (paperCount, citationCount, h-index), external IDs (ORCID, DBLP), and optionally papers.

Details about an author s papers

Retrieves a list of papers authored or co-authored by a specific researcher identified by their unique Semantic Scholar author ID.

Details about a paper

Retrieve details for a paper by its ID, with selectable fields.

Details about a paper s authors

Retrieve a paginated list of authors for a Semantic Scholar paper, identified by a supported ID such as a Semantic Scholar ID, DOI, or arXiv ID.

Details about a paper s citations

Retrieve a paginated list of papers citing a specified paper.

Details about a paper s references

Retrieves the list of references cited by a specific paper in the Semantic Scholar database.

Get dataset download links

Tool to get download links for a specific dataset within a release.

Get dataset diffs

Get download links for incremental diffs between dataset releases.

Get details for multiple authors at once

Retrieve details for up to 1,000 Semantic Scholar authors by ID in one batch, with selectable author and paper fields.

Get details for multiple papers at once

Retrieve detailed information for multiple academic papers in a single API call using the Semantic Scholar paper batch endpoint.

Get paper recommendations

Tool to get paper recommendations based on positive and negative example papers.

Get recommendations for paper

Tool to get recommended papers for a single positive example paper.

Get dataset release information

Tool to retrieve metadata for a specific Semantic Scholar dataset release.

List available dataset releases

Tool to list all available dataset releases from Semantic Scholar.

Paper title search

Behaves similarly to /paper/search, but is intended for retrieval of a single paper based on closest title match to given query.

Search Bulk Papers

Tool to perform bulk search for academic papers.

Search for authors by name

Search for academic authors in the Semantic Scholar database by name.

Search papers by relevance

Tool to search for academic papers by relevance in the Semantic Scholar database.

Suggest paper query completions

Get autocomplete suggestions for paper queries.

Text snippet search

Search for text snippets matching a plain-text query in academic papers.

FRAMEWORKS

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Hermes logo

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LangChain

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Vercel AI SDK logo

Vercel AI SDK

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LlamaIndex logo

LlamaIndex

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CrewAI

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Pydantic AI logo

Pydantic AI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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