add mcp-for-research blog post #3021
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Co-authored-by: Aritra Roy Gosthipaty <[email protected]>
thanks for the review! |
merveenoyan
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Aug 13, 2025
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| Academic research involves frequent **research discovery**: finding papers, code, related models and datasets. This typically means switching between platforms like [arXiv](https://arxiv.org/), [GitHub](https://github.com/), and [Hugging Face](https://huggingface.co/), manually piecing together connections. | ||
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| The [Model Context Protocol (MCP)](https://huggingface.co/learn/mcp-course/unit0/introduction) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can use research tools through natural language requests, automating platform switching and cross-referencing. |
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we could add a bit of research tracker MCP Space here, it's too abstract otherwise
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Yes, I agree! We could even show a gif with an example from Cursor.
| <gradio-app theme_mode="light" space="dylanebert/research-tracker-mcp"></gradio-app> | ||
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| ## Learn More | ||
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one sentence call for action would be great
pcuenca
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Aug 13, 2025
| title: "MCP for Research: How to Connect AI to Research Tools" | ||
| author: dylanebert | ||
| thumbnail: /blog/assets/mcp-for-research/thumbnail.png | ||
| date: Aug 11, 2025 |
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Reminder to update before release
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| At the lowest level of abstraction, researchers search manually and cross-reference by hand. | ||
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| ```bash |
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Maybe enclose in html <xmp> / </xmp> tags to avoid weird syntax highlighting. Or use > to blockquote.
| results = gather_research_info("https://arxiv.org/abs/2103.00020") | ||
| ``` | ||
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| The [research tracker](https://huggingface.co/spaces/dylanebert/research-tracker) demonstrates systematic research discovery built from these types of scripts. |
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Perhaps you could briefly explain somewhere that you've been using this approach to keep track of research in the 3D space, just to make it more relatable.
| - Include performance benchmarks when available | ||
| ``` | ||
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| The AI orchestrates multiple tools, fills information gaps, and reasons about results: |
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| The AI orchestrates multiple tools, fills information gaps, and reasons about results: | |
| Because the LLM knows the details of what each tool can do, it's able to orchestrate calls to multiple tools, fill information gaps, reason about results and collate them for the user: |
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| user: "Find papers related to vision transformers with available code and models" | ||
| ai: # Combines multiple tools to gather complete information | ||
| ``` |
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It could be cool to show a summary of an actual conversation, or a link to it, or a video.
| - Quality depends on the implementation | ||
| - Understanding the lower layers (both manual and scripted) leads to better implementations | ||
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| ## Setup and Usage |
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Up to this point it's been a more or less theoretical discussion. Perhaps we could segue into this by reminding the reader that we used a set of custom scripts that evolved into a set of MCP tools that anyone can use, build upon or use for inspiration.
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| ### Quick Setup | ||
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| For setup instructions, see the [Research Tracker MCP](https://huggingface.co/spaces/dylanebert/research-tracker-mcp) space, then click `Use via MCP or API` at the bottom of the page. This will provide instructions for adding the server with SSE, i.e.: |
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| For setup instructions, see the [Research Tracker MCP](https://huggingface.co/spaces/dylanebert/research-tracker-mcp) space, then click `Use via MCP or API` at the bottom of the page. This will provide instructions for adding the server with SSE, i.e.: | |
| For setup instructions, see the [Research Tracker MCP](https://huggingface.co/spaces/dylanebert/research-tracker-mcp) space, then click `Use via MCP or API` at the bottom of the page. This will provide instructions for adding the tools as a server you can integrate with your preferred IDE, such as Cursor or Claude Code: |
| <gradio-app theme_mode="light" space="dylanebert/research-tracker-mcp"></gradio-app> | ||
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| ## Learn More | ||
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| Academic research involves frequent **research discovery**: finding papers, code, related models and datasets. This typically means switching between platforms like [arXiv](https://arxiv.org/), [GitHub](https://github.com/), and [Hugging Face](https://huggingface.co/), manually piecing together connections. | ||
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| The [Model Context Protocol (MCP)](https://huggingface.co/learn/mcp-course/unit0/introduction) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can use research tools through natural language requests, automating platform switching and cross-referencing. |
There was a problem hiding this comment.
Yes, I agree! We could even show a gif with an example from Cursor.
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| The [Model Context Protocol (MCP)](https://huggingface.co/learn/mcp-course/unit0/introduction) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can use research tools through natural language requests, automating platform switching and cross-referencing. | ||
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| ## Research Discovery: Three Layers of Abstraction |
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| ## Research Discovery: Three Layers of Abstraction | |
| This post shows how we built a set of MCP tools for research, how you can use them for your own research projects, or as inspiration for new tools adapted to your needs. | |
| ## Research Discovery: Three Layers of Abstraction |
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Added blog post for Research Tracker MCP philosophy, how-to, and pointing toward more resources