Check the antibodies in a paper — with your own AI
Connect the Only Good Antibodies database to Claude, ChatGPT, or any MCP-capable
assistant. Then, when you ask it to read or summarise a manuscript, preprint, or
methods section, it checks every antibody the paper cites against independent,
knockout-controlled characterisation data from YCharOS (produced to community
consensus protocols) — and tells you how each one performed, application by
application.
⚗️ This is an early prototype. It works today and we use it ourselves,
but the tools, wording, and results may change as we improve it. It reports only what
YCharOS has independently characterised with knockout controls. Read the paper yourself
as well. Feedback is very welcome.
Results are based on consensus protocols. Antibody performance is protocol and sample dependent, and these results do not validate or invalidate experiments in other assay systems or sample types. Read the protocols.
The connector URL
Add this once in your assistant's connector settings. It runs on your own
usage.
https://oga-mcp.onrender.com/readonly/mcp
How to check a paper
Once the database is connected (steps below), you drive it in plain language:
Give your AI a paper. Paste the text or methods section, attach a
PDF, or share a link.
Ask it to read, summarise, or review it — or just say
“check the antibodies in this paper against OGA.” Your assistant picks the reagents
out of the paper and looks each one up, so you don't have to list them yourself.
The paper itself stays with your assistant — only the reagents it found are sent
to OGA.
Read the result. Every antibody it can identify (by catalogue
number or RRID) comes back grouped as recommended / not recommended / not
tested / not in the dataset, each with its per-application result
(WB / IP / IF / FC), RRID, and report DOI. Genes named in the paper are checked too.
Try this:“Summarise this preprint, then use
the OGA tools to check whether the antibodies it uses are knockout-validated: [paste
the text or a link].”
What it does
Covers the whole manuscript. Your assistant reads the paper and
passes on every catalogue number, RRID, and gene it finds; OGA checks each against
YCharOS's independent, knockout-controlled characterisation data.
Results per application. A western-blot pass tells you nothing
about IF, IP, or FC, so each application is reported separately.
Grounded in the data. It reports only what YCharOS has
independently characterised. “Not in the dataset” means nobody has tested
it, not that it is a bad antibody.
Add it to your assistant
The OGA database is a custom connector (MCP server). Add it once in
your assistant's settings, sign in when prompted, and it appears as a set of OGA data
tools you can use in any chat.
Claude (claude.ai)
Open Settings → Connectors.
Click Add custom connector and paste the URL above.
Sign in when prompted.
In any chat, paste a paper and ask: “Summarise this and use the OGA tools
to check its antibodies.”
ChatGPT
Open Settings → Connectors (or Apps & Connectors).
Choose Add / Create connector and paste the URL above.
Sign in when prompted to authorise the connection.
Enable it in a chat and ask the same question.
Any MCP-capable assistant works. The OGA database is a standard
remote MCP server, so it also connects to Claude Code, Claude Desktop, and other MCP
clients — paste the same URL wherever you add connectors.
Not ready to connect an assistant? You can still use OGA in your browser:
browse any gene's independently validated antibodies across the site, or use the
Selection Tool to pick an antibody and its
controls for your target — no connector needed.
More tools for your own experiments
Free tools to plan validation, choose the right controls, and keep a record:
