What you get
Your draft back, claim by claim, with a real citation attached to each one — journal, year, DOI, and a link to the paper on Consensus. Claims the literature does not support come back flagged rather than quietly cited to something adjacent. This is the most common thing people do with Consensus MCP. Researchers writing with an AI assistant report going from citations they could not verify at all to a draft where nearly every claim checks out.Who it’s for
Anyone drafting with an LLM in the loop: PhD students writing a thesis chapter, clinicians writing a discussion section, postdocs turning results into a manuscript. Works in whatever you already write with — Claude, ChatGPT, Claude Code.The prompt
Paste your draft section after this. Works as-is in any MCP client.How it works
1
Claims get isolated before anything is searched
Most bad citation matching happens because a sentence carrying three claims gets one citation. Forcing each claim into a standalone proposition with a named population, exposure, and outcome is what makes the search specific enough to be checkable.
2
Several phrasings per claim
A single phrasing is one search. Three or more across synonyms and field-specific terminology is the difference between “no evidence found” and finding the paper that exists.
3
Design tier is recorded, not just the citation
Knowing you ended up citing an observational study rather than a trial changes how you should word the sentence. Ask for the tier and you get that for free.
4
Four verdicts, not two
The
PARTLY SUPPORTED bucket is where most of the value is — it turns an overclaim into a defensible sentence instead of forcing a binary keep-or-cut decision.The API equivalent
If you are building this into a writing tool rather than running it in a chat, search per extracted claim and keep the design tier explicit:title, authors, journal_name, publish_year, doi, citation_count, and url. On paid plans, study_type and takeaway come back too — takeaway is what you show the writer as the evidence quote.
What to check before you trust it
NO EVIDENCE FOUNDis a real answer. If you get none of them across a whole section, the prompt is being too accommodating — the model is stretching papers to fit. Re-run and insist on the distinction.- Check one citation by hand per section. Open the
urland confirm the paper says what the table claims. Calibrate once, then trust the pattern. - Watch for topic-level matches. A paper about the right subject that never tested your specific claim is the most common failure. The requirement to quote a supporting sentence is what catches it.
- The tier matters for wording. A claim supported only by observational evidence should not be written as though a trial established it.
Related
Verify that every citation is real and supports its claim
The other half of the job: verify the references you already have.
Build a reusable literature-review workflow
Make this repeatable instead of pasting the prompt every time.
Best practices
Fan-out, the filter ladder, and the rest of the primitives.
All use cases
Browse the gallery by persona.