The CiteAb MCP for faster reagent selection: CiteAb’s data, within the AI tools you already use
5
Min Read
In this blog:
- What is the CiteAb MCP?
- Learn how the MCP can accelerate reagent selection
- Is using our MCP an improvement to AI alone?
We are excited to share more on our new solution: the CiteAb MCP.
If you’re a researcher who’s:
- Tired of spending hours searching manually for reagents
- Fed up of switching between supplier data sheets, pubmed, third-party validations, internal databases and more to collect the evidence you need
- Frustrated by having to repeat searches when finding multiple reagents
Then our MCP can help you! Learn more about it below.
What is the CiteAb MCP?
An MCP acts as a ‘universal connector’ between AI applications and databases and tools.
Our MCP can be plugged into Claude, ChatGPT, or other compatible/ in-house LLMs. This enables you to search the CiteAb reagent database – alongside internal data – within the tools you are already using.
The MCP gives complete, scalable access to our citation-ranked searching, experimental filters, and citation and image data, in real-time. This is all indispensable information for reagent identification.
What makes the MCP different to CiteAb explore?
CiteAb Explore is our platform for searching reagents, backed by citation data, third-party validations and published images. The MCP is powered by the same high-quality data, but is designed for teams who want to search alongside internal data, integrate with existing AI workflows, or run high-volume reagent queries.
How can I use the CiteAb MCP?
Let’s delve into three real world examples of how you can use the CiteAb MCP to accelerate your research.
Faster reagent discovery
Ask: “What’s the most validated antibody for ErbB2 in western blot in mouse?”
Result: Manually, that search could take 2-6 hours. With the MCP, you get results in minutes – ranked by citation count, sourced across hundreds of supplier catalogues, and backed by images and experimental data.
An independent top 5 pharma evaluation estimated a 45% time to shortlist reduction from using CiteAb.
High-throughput reagent selection
Ask: ‘Which are the best antibodies to use in my IHC assay against Er-alpha, PR and Her2’
Result: The MCP outputs the same reliable, citation-backed results whether you’re querying reagents for one target or a thousand, simultaneously.
Scale reagent selection for your IHC, Flow cytometry and other experiments, by specifying exactly the information you need and even exporting results in desired formats.
Smarter reagent selection
Ask. ‘Give me five potential antibodies for our IHC assay, based on CiteAb data and our internal use.”
Result: The MCP can work alongside your internal data and other connected databases, surfacing organisational-level context alongside citation evidence.
You can also use natural language prompts to filter down results to your specification; whether that’s validation method, application, reactivity or more.
This means more informed decisions, fewer experimental failures, and significantly less manual literature review and procurement admin.
See it in action:
Is it actually better than using an AI tool alone?
The short answer: yes.
The CiteAb team ran a structured analysis comparing outputs from Claude with and without the MCP. We used popular reagent queries, each repeated five times with no prior instructions or conversation history.
Without the MCP, the AI consistently fell short in three areas:
- Missing Data
- LLMs regularly omitted SKUs and Clone IDs, leaving more work for the researcher to find this information.
- Outdated or inconsistent outputs
- Outputs showed inconsistent, sometimes contradictory, information.
- Lacking citation evidence
- The LLM alone lacked data-driven reasoning. Using the MCP enabled transparent selection based on use in the peer reviewed literature.
You can read more about the analysis by downloading the slide deck at the bottom of this blog, where we outline the methodology and two examples of outputs generated, as well as more information on the MCP.
Overall, with the MCP connected, outputs were accurate, consistent, and grounded in real citation and image data.
This matters in research. Hallucinated or outdated reagent information doesn’t just waste time – it can compromise experimental outcomes.
Get in touch to try it out
To try out this new solution, reach out to the CiteAb team here.
You can also download the slides from the Digi-Tech Meeting below. In this deck, we outline the MCP impact analysis, and give more technical details on the benefits of the CiteAb MCP.
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