# How to connect Semanticscholar to Muse ```json { "title": "How to connect Semanticscholar to Muse", "toolkit": "Semanticscholar", "toolkit_slug": "semanticscholar", "framework": "Muse", "framework_slug": "muse", "url": "https://composio.dev/toolkits/semanticscholar/framework/muse", "markdown_url": "https://composio.dev/toolkits/semanticscholar/framework/muse.md", "updated_at": "2026-10-04T16:46:44.144Z" } ``` ## Introduction Muse is Meta's AI assistant. It can connect to outside tools through custom connectors, which use the Model Context Protocol (MCP). This guide explains how to connect Semanticscholar to Muse using Composio Connect, which handles OAuth, token refresh, and reliability so you do not have to. You add Composio as a custom connector once, then ask Muse to work with Semanticscholar in plain language. ## Also integrate Semanticscholar with - [Claude Code](https://composio.dev/toolkits/semanticscholar/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/semanticscholar/framework/claude-cowork) - [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) - [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) - [Atomic Agent](https://composio.dev/toolkits/semanticscholar/framework/atomic-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 - Cross-app workflows across 1,500+ apps from a single plugin layer. - On-demand tool loading so Muse only loads the tools needed for the current task. - Programmatic tool calling through a remote workbench for complex, multi-step workflows. - Multi-account support so users can connect and switch between multiple accounts for the same app. - Shared account access so teams or agents can work through common business accounts and service connections. - Managed authentication with OAuth handling, credential storage, and token refresh managed by Composio. - Safer credential handling so app credentials stay outside the model context and tool responses. - User-level account isolation so actions run against the correct connected account instead of mixing user access. ## Connect Semanticscholar to Muse ### Connecting Semanticscholar to Muse You add Composio to Muse once as a custom connector, then connect Semanticscholar from the chat. ## 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. ## 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 ### 1. Why doesn't Muse show a button to add my API key? Muse shows the button only when you ask it to open the secure credentials store. - Start a new chat. - Send the message from step 1 exactly as written. - If Muse asks for the key in the chat, do not paste it. Ask Muse to open the secure credentials store. ### 2. Why does the Composio connection fail after I add my API key? The key or the connector settings are not correct. - Copy the full key from the Composio dashboard. It starts with ck_. - Make sure the URL is https://connect.composio.dev/mcp and the header is x-consumer-api-key. - Remove the Composio connector in Muse settings, then do steps 1 to 3 again. ### 3. Why doesn't Muse use the Composio tools for Semanticscholar? Muse may not pick the Composio connector for the request. - Ask Muse to list its Composio tools. - Name Composio and Semanticscholar in your request, for example: Use Composio to connect to Semanticscholar. - If it still does not work, start a new chat and try again. ### 4. Why does Muse ask me to connect Semanticscholar again? Your Semanticscholar connection expired or was removed. - Open the link Muse gives you and sign in to Semanticscholar again. - Check that the Semanticscholar connection shows as ACTIVE in the Composio dashboard. ### 5. What do I do if I pasted my API key into the chat? Treat the key as exposed. - Create a new API key in the Composio dashboard and delete the old one. - Add the new key in Muse's secure credentials store, not in the chat. ## Creating MCP Server - Stand-alone vs Composio SDK Once connected, Muse can access the Semanticscholar MCP server through Composio to run the actions you authorize, using plain language in the chat. ## Complete Code None listed. ## Conclusion ### Way Forward Now that Semanticscholar is connected, extend your setup by connecting the other apps you already use every day, so Muse can run true cross-app workflows end to end. - Connect Calendar to turn threads into scheduled meetings automatically. - Connect Slack or Teams to post summaries, approvals, and alerts where your team works. - Connect Notion, Linear, Jira, or Asana to convert requests into tickets, tasks, and docs. - Connect Drive, Dropbox, or OneDrive to fetch, file, and share attachments without manual steps. Start with one workflow you do repeatedly, then keep adding apps as you find new handoffs. With everything behind a single connection, Muse can coordinate multiple tools safely and reliably in one conversation. ## How to build Semanticscholar MCP Agent with another framework - [Claude Code](https://composio.dev/toolkits/semanticscholar/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/semanticscholar/framework/claude-cowork) - [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) - [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) - [Atomic Agent](https://composio.dev/toolkits/semanticscholar/framework/atomic-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. - [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. - [BotMape](https://composio.dev/toolkits/botmape) - BotMape is an API platform for building AI agents, knowledge bases, conversations, and messages. Use it to manage agent workflows and track usage analytics from one place. ## Frequently Asked Questions ### Does Muse support MCP? Yes. Muse supports custom connectors that use the Model Context Protocol (MCP). Composio connects as one custom connector over remote streamable HTTP at https://connect.composio.dev/mcp, and gives Muse access to Semanticscholar and 1,500+ other apps. ### Where do I get the API key for the Composio connector? Copy it from the Composio dashboard. It starts with ck_. Enter it only in Muse's secure credentials store, never in the chat. ### Can I connect more than one Semanticscholar account to Muse? Yes. Composio supports multiple connected accounts for the same app. Give each Semanticscholar connection a name, such as work or personal, and tell Muse which one to use. ### How safe is my Semanticscholar data with Composio? Tokens, keys, and configuration are encrypted at rest and in transit. Your Composio API key stays in Muse's secure credentials store, not in the chat or the model context. Composio is SOC 2 Type 2 compliant. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)