How to integrate Semanticscholar MCP with Autogen

This guide walks you through connecting Semanticscholar to AutoGen using the Composio tool router. By the end, you'll have a working Semanticscholar agent that can find the latest papers on graph neural networks, list citations for a specific research paper, summarize an author’s recent publications through natural language commands. This guide will help you understand how to give your AutoGen 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.

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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 AutoGen using the Composio tool router. By the end, you'll have a working Semanticscholar agent that can find the latest papers on graph neural networks, list citations for a specific research paper, summarize an author’s recent publications through natural language commands.

This guide will help you understand how to give your AutoGen 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.

Also integrate Semanticscholar with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Semanticscholar
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Semanticscholar tools
  • Run a live chat loop where you ask the agent to perform Semanticscholar operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The Semanticscholar 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 scholarly data, so your agent can search for academic papers, retrieve detailed author profiles, analyze citations, and explore references or publication histories on your behalf.

  • Comprehensive literature search and discovery: Let your agent search for academic papers by topic, author, or relevance and retrieve lists of matching publications with rich metadata.
  • In-depth paper and author insights: Ask your agent to fetch detailed information about specific papers—including titles, abstracts, authors, and publication years—or get complete profiles for researchers and their entire body of work.
  • Citation and reference analysis: Enable your agent to trace the impact of a paper by pulling its citations or explore the foundational research it builds upon by listing its references.
  • Batch retrieval for large-scale research: Efficiently gather details on multiple papers or authors at once, streamlining reviews and bibliometric analyses across large datasets.
  • Bulk and relevance-based queries: Use advanced bulk search and filtering to identify up to thousands of papers at a time, making it easy for your agent to support systematic literature reviews and academic data exploration.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Semanticscholar account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Semanticscholar via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Semanticscholar connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Semanticscholar session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["semanticscholar"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Semanticscholar tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Semanticscholar assistant agent with MCP tools
    agent = AssistantAgent(
        name="semanticscholar_assistant",
        description="An AI assistant that helps with Semanticscholar operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Semanticscholar tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Semanticscholar related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Semanticscholar tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Semanticscholar and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Semanticscholar session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["semanticscholar"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Semanticscholar assistant agent with MCP tools
        agent = AssistantAgent(
            name="semanticscholar_assistant",
            description="An AI assistant that helps with Semanticscholar operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Semanticscholar related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Semanticscholar through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Semanticscholar, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

Examples: https://api.

Details about a paper s authors

Retrieves the list of authors for a specific paper identified by its unique paper_id in the Semantic Scholar database.

Details about a paper s citations

Retrieves a list of citations for a specific academic paper using its unique Semantic Scholar paper ID.

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

Retrieves detailed information for multiple authors from Semantic Scholar in a single API call.

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 (~500 words) within academic papers that match your natural language query.

FRAMEWORKS

How to build Semanticscholar MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Semanticscholar MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Semanticscholar MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Semanticscholar MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Semanticscholar MCP with Claude Cowork

Codex logo

Codex

Use Semanticscholar MCP with Codex

OpenClaw logo

OpenClaw

Use Semanticscholar MCP with OpenClaw

Hermes logo

Hermes

Use Semanticscholar MCP with Hermes

CLI logo

CLI

Use Semanticscholar MCP with CLI

Google ADK logo

Google ADK

Use Semanticscholar MCP with Google ADK

LangChain logo

LangChain

Use Semanticscholar MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Semanticscholar MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Semanticscholar MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Semanticscholar MCP with LlamaIndex

CrewAI logo

CrewAI

Use Semanticscholar MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Semanticscholar tools.

Yes, absolutely. You can configure which Semanticscholar 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.

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 Semanticscholar data and credentials are handled as safely as possible.

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