# How to integrate Semanticscholar MCP with Atomic Agent

```json
{
  "title": "How to integrate Semanticscholar MCP with Atomic Agent",
  "toolkit": "Semanticscholar",
  "toolkit_slug": "semanticscholar",
  "framework": "Atomic Agent",
  "framework_slug": "atomic-agent",
  "url": "https://composio.dev/toolkits/semanticscholar/framework/atomic-agent",
  "markdown_url": "https://composio.dev/toolkits/semanticscholar/framework/atomic-agent.md",
  "updated_at": "2026-08-11T13:14:26.089Z"
}
```

## Introduction

Atomic Agent is an open-source AI agent that runs on your machine and supports local models through llama.cpp. Connect it to 1,500+ apps through Composio, including Semanticscholar.
This guide uses Atomic Agent's built-in Composio integration. You need Atomic Agent v0.5.6 or later, your own Composio API key, and a Semanticscholar account. See the [Atomic Agent documentation](https://atomicagent.io/docs/?cta_placement=atomic-agent-docs) for installation instructions.

## Also integrate Semanticscholar with

- [OpenAI Agents SDK](https://composio.dev/toolkits/semanticscholar/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/semanticscholar/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/semanticscholar/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/semanticscholar/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/semanticscholar/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/semanticscholar/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/semanticscholar/framework/hermes-agent)
- [Google ADK](https://composio.dev/toolkits/semanticscholar/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/semanticscholar/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/semanticscholar/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/semanticscholar/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/semanticscholar/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/semanticscholar/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/semanticscholar/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/semanticscholar/framework/autogen)

## TL;DR

### How Atomic Agent connects to Semanticscholar
Atomic Agent connects to Composio's Tool Router over Streamable HTTP MCP. It discovers the tools needed for your request, and Composio handles account authentication.
Execution and account-connection tools follow Atomic Agent's configured approval policy. Review any approval prompt before allowing an action on your connected account.

## Connect Semanticscholar to Atomic Agent

### Connect Semanticscholar to Atomic Agent
- Sign up or sign in to the [Composio dashboard](https://dashboard.composio.dev/?cta_placement=atomic-agent-api-key). Copy your API key.
- In Atomic Agent, open the Integrations tab and select Composio. Select API key, press e, paste the key, and press Enter. The tools become available immediately, without a restart.
- Ask Atomic Agent to do something in Semanticscholar. It searches Composio for the right tool.
- On first use, open the sign-in link returned in the chat. Connect your Semanticscholar account, then return to Atomic Agent to continue.
### Headless setup
Add the following line to ~/.atomic-agent/.env, replacing YOUR_COMPOSIO_API_KEY with your key. Start Atomic Agent after saving the file.

```bash
COMPOSIO_API_KEY=YOUR_COMPOSIO_API_KEY
```

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SEMANTICSCHOLAR_DETAILS_ABOUT_AN_AUTHOR` | 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. By default returns authorId and name only. Use 'fields' parameter for additional data: name, url, affiliations, homepage, externalIds, paperCount, citationCount, hIndex, papers (supports nested fields like papers.title, papers.year). Limit: 10 MB per request. |
| `SEMANTICSCHOLAR_DETAILS_ABOUT_AN_AUTHOR_S_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. This endpoint is particularly useful for conducting literature reviews, analyzing an author's body of work, or tracking a researcher's publications over time. It provides a comprehensive view of an author's contributions to their field of study, including all papers where the author is listed as an author regardless of their authorship position. The response may be paginated for authors with a large number of publications, and additional API calls might be necessary to retrieve the complete list of papers. Use the offset and limit parameters to control pagination. |
| `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER` | Details about a paper | Examples: https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b Returns a paper with its paperId and title. https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b?fields=url,year,authors Returns the paper's paperId, url, year, and list of authors. Each author has authorId and name. https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b?fields=citations.authors Returns the paper's paperId and list of citations. Each citation has its paperId plus its list of authors. Each author has their 2 always included fields of authorId and name. Limitations: Can only return up to 10 MB of data at a time. |
| `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_AUTHORS` | 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. This endpoint returns detailed author information including authorId and name (returned by default), and optionally: url, affiliations, homepage, paperCount, citationCount, hIndex, and papers (with subfields). Use the 'fields' parameter to request additional author fields beyond the defaults. The response is paginated and includes offset/limit parameters for retrieving large author lists. This tool is ideal for exploring paper collaborations, identifying author affiliations, or building author networks. It accepts various paper ID formats including Semantic Scholar IDs, DOI, ARXIV, PMID, and others. |
| `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_CITATIONS` | Details about a paper s citations | Retrieves a list of citations for a specific academic paper using its unique Semantic Scholar paper ID. This endpoint is useful for researchers and developers who want to explore the impact and connections of a particular academic work within the broader scientific literature. It provides information about other papers that have cited the specified paper, allowing users to trace the influence of research and discover related works. The endpoint should be used when analyzing the reception and impact of a specific paper, building citation networks, or conducting bibliometric studies. It does not provide the full text of citing papers or detailed information about the citations beyond basic metadata. |
| `SEMANTICSCHOLAR_DETAILS_ABOUT_A_PAPER_S_REFERENCES` | Details about a paper s references | Retrieves the list of references cited by a specific paper in the Semantic Scholar database. This endpoint allows users to explore the scholarly context of a publication by accessing its bibliography. It's particularly useful for understanding the foundation of a paper's research, tracing the development of ideas, or conducting literature reviews. The tool returns details about the cited papers, which may include their titles, authors, publication dates, and Semantic Scholar IDs. It should be used when analyzing a paper's sources or investigating the connections between different academic works. Note that this endpoint only provides outgoing references (papers cited by the specified paper) and not incoming citations (papers that cite the specified paper). |
| `SEMANTICSCHOLAR_GET_DATASET` | Get dataset download links | Tool to get download links for a specific dataset within a release. Use when you need to download Semantic Scholar dataset files from S3. Returns pre-signed URLs for all dataset partitions. |
| `SEMANTICSCHOLAR_GET_DATASET_DIFFS` | Get dataset diffs | Get download links for incremental diffs between dataset releases. Returns a list of diffs required to update a dataset from start_release to end_release, enabling efficient dataset synchronization. Use when you need to update a local dataset copy without re-downloading the entire dataset. |
| `SEMANTICSCHOLAR_GET_DETAILS_FOR_MULTIPLE_AUTHORS_AT_ONCE` | Get details for multiple authors at once | Retrieves detailed information for multiple authors from Semantic Scholar in a single API call. This endpoint allows users to efficiently fetch data for a batch of authors by providing their unique Semantic Scholar IDs. It's particularly useful for applications that need to gather information on multiple authors simultaneously, reducing the number of individual API calls required. The endpoint accepts a list of author IDs and returns comprehensive details for each author, which may include their publications, citations, and other relevant academic information. While the exact response structure is not specified in the given schema, users can expect rich metadata about the requested authors. |
| `SEMANTICSCHOLAR_GET_DETAILS_FOR_MULTIPLE_PAPERS_AT_ONCE` | 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. This endpoint efficiently fetches data for up to 500 papers at once, significantly reducing the number of individual API requests needed. Key features: - Accepts multiple paper ID formats (Semantic Scholar ID, CorpusId, DOI, ArXiv, PMID, etc.) - Customizable field selection to retrieve only needed data - Papers not found return null in the corresponding array position - Results maintain the same order as input IDs - Supports nested field queries (e.g., authors.name, citations.title) Use this endpoint when you have a list of known paper IDs and want to retrieve their details simultaneously, rather than making individual requests for each paper. |
| `SEMANTICSCHOLAR_GET_PAPER_RECOMMENDATIONS` | Get paper recommendations | Tool to get paper recommendations based on positive and negative example papers. Use when you need to find papers similar to ones you like (positive examples) and optionally dissimilar to ones you don't like (negative examples). The recommendation engine analyzes the provided examples and returns relevant papers from the Semantic Scholar database. |
| `SEMANTICSCHOLAR_GET_RECOMMENDATIONS_FOR_PAPER` | Get recommendations for paper | Tool to get recommended papers for a single positive example paper. Use when you need to find papers similar to a given paper based on Semantic Scholar's recommendation algorithm. |
| `SEMANTICSCHOLAR_GET_RELEASE` | Get dataset release information | Tool to retrieve metadata for a specific Semantic Scholar dataset release. Returns release information including available datasets with their descriptions. Use when you need to discover what datasets are available in a release or get release documentation. |
| `SEMANTICSCHOLAR_LIST_RELEASES` | List available dataset releases | Tool to list all available dataset releases from Semantic Scholar. Use when you need to discover available release dates for downloading datasets. |
| `SEMANTICSCHOLAR_PAPER_TITLE_SEARCH` | 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. Examples: https://api.semanticscholar.org/graph/v1/paper/search/match?query=Construction of the Literature Graph in Semantic Scholar Returns a single paper that is the closest title match. Each paper has its paperId, title, and matchScore as well as any other requested fields. https://api.semanticscholar.org/graph/v1/paper/search/match?query=totalGarbageNonsense Returns with a 404 error and a "Title match not found" message. Limitations: Will only return the single highest match result. |
| `SEMANTICSCHOLAR_SEARCH_BULK_PAPERS` | Search Bulk Papers | Tool to perform bulk search for academic papers. Intended for bulk retrieval of basic paper data without search relevance scoring. Use when you need to retrieve large sets of papers with optional text filtering and various criteria. Supports token-based pagination for efficient fetching of up to 10 million papers (use Datasets API for larger needs). |
| `SEMANTICSCHOLAR_SEARCH_FOR_AUTHORS_BY_NAME` | Search for authors by name | Search for academic authors in the Semantic Scholar database by name. This action searches for authors using plain-text name queries. The search is case-insensitive and supports partial name matches (e.g., "Smith" will match "John Smith", "Adam Smith", etc.). Use cases: - Find authors by their name to get their author ID - Discover authors in a specific research area by searching common names - Retrieve author metadata including publications, affiliations, citation counts, and h-index - Build author directories or research networks The response includes pagination metadata (total, offset, next) to help retrieve large result sets. Use the 'fields' parameter to customize which author attributes are returned, and use 'offset' and 'limit' for pagination through result sets larger than 1000 authors. Note: Results are paginated with a maximum of 1000 results per request. Use the 'next' field in the response to determine the offset for the next page. |
| `SEMANTICSCHOLAR_SEARCH_PAPERS` | Search papers by relevance | Tool to search for academic papers by relevance in the Semantic Scholar database. Use when searching for papers on specific topics, keywords, or research areas. Returns papers ordered by relevance score with support for extensive filtering by publication type, date, venue, field of study, and citation metrics. |
| `SEMANTICSCHOLAR_SUGGEST_PAPER_QUERY_COMPLETIONS` | Suggest paper query completions | Get autocomplete suggestions for paper queries. Returns a list of papers matching the partial query string, useful for interactive search experiences. Each suggestion includes the paper ID, title, and authors with publication year. Example: For query "machine learning", returns papers like "Machine learning - a probabilistic perspective" by Murphy, 2012. |
| `SEMANTICSCHOLAR_TEXT_SNIPPET_SEARCH` | Text snippet search | Search for text snippets (~500 words) within academic papers that match your natural language query. Returns relevant excerpts from papers' titles, abstracts, and body text, ranked by relevance score. Each result includes: snippet text, location in paper, citation references, and paper metadata (title, authors, corpus ID). Supports filtering by authors, publication date, venue, field of study, citation count, and specific paper IDs. Results sorted by relevance (highest score first). Use limit=10 (default, max 1000) to control result count. |

## Supported Triggers

None listed.

## Troubleshooting

### Why is my API key rejected?

Copy the key directly from the Composio dashboard. Atomic Agent requires ASCII characters because it sends the key in an HTTP header. Remove any extra characters introduced when copying.

### Why are Composio tools unavailable after saving a key?

Check that you use Atomic Agent v0.5.6 or later and that Composio is enabled in your configuration. A "composio": { "enabled": false } setting disables the integration while keeping the saved key on disk.

### Does Composio have a free plan?

The Hobby plan includes 100,000 tool calls per month. Composio-managed OAuth apps have a lower included allowance. See [Composio pricing](/pricing) for the current limits that apply to your connection.

## Complete Code

None listed.

## Conclusion

### Use Semanticscholar from Atomic Agent
With your account connected, ask Atomic Agent to work with Semanticscholar in natural language. You can connect more apps through the same Composio integration as you need them.
Visit the [Atomic Agent website](https://atomicagent.io/?cta_placement=atomic-agent-site) or inspect the [Atomic Agent source code](https://github.com/AtomicBot-ai/atomic-agent?cta_placement=atomic-agent-repo).

## How to build Semanticscholar MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/semanticscholar/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/semanticscholar/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/semanticscholar/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/semanticscholar/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/semanticscholar/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/semanticscholar/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/semanticscholar/framework/hermes-agent)
- [Google ADK](https://composio.dev/toolkits/semanticscholar/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/semanticscholar/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/semanticscholar/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/semanticscholar/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/semanticscholar/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/semanticscholar/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/semanticscholar/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/semanticscholar/framework/autogen)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [AltTextLab](https://composio.dev/toolkits/alt_text_lab) - AltTextLab is an AI service that generates accessible, SEO-aware alt text for images. It helps teams make image content easier to understand, index, and publish.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apiframe](https://composio.dev/toolkits/apiframe) - Apiframe is an API platform for generating and managing AI images, videos, music, uploads, assets, jobs, and custom LoRAs. It gives developers one clean API for creative AI workflows without juggling multiple model providers.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Arize AX](https://composio.dev/toolkits/arize_ax) - Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications. Use it to debug LLM behavior, compare experiments, and improve production AI quality.
- [Artificial Analysis](https://composio.dev/toolkits/artificial_analysis) - Artificial Analysis is an independent benchmarking platform for AI models and API providers. Use it to compare model intelligence, coding, math, pricing, latency, throughput, and arena rankings.
- [AssemblyAI](https://composio.dev/toolkits/assemblyai) - AssemblyAI is a speech-to-text and audio intelligence API for uploaded or hosted media. Use it to turn audio and video into accurate transcripts, summaries, and insights.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Avoma](https://composio.dev/toolkits/avoma) - Avoma is an AI meeting assistant for recording, transcribing, analyzing, and managing meetings, calls, notes, scorecards, and conversation intelligence. It helps teams turn conversations into searchable notes, follow-ups, coaching insights, and revenue intelligence.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Bland AI](https://composio.dev/toolkits/bland_ai) - Bland AI is a conversational AI platform for building voice agents, phone calls, messaging, and communication workflows. Use it to automate calling, evaluate conversations, and run voice operations at scale.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Semanticscholar MCP?

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.

### Can I use Tool Router MCP with Atomic Agent?

Yes, you can. Atomic Agent 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.

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

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.

### 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 Semanticscholar data and credentials are handled as safely as possible.

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