Zetesis Scientific Due Diligence MCP server

Zetesis

Available on Smithery

Scientific due diligence on a claim, from inside Claude, Copilot, or any MCP host.

Give Zetesis a claim, an abstract, a paper, a grant or a deck. It routes the claim to its scientific class, then returns the questions a domain reviewer would ask, the failure patterns that caught comparable claims before, and the public evidence bearing on it, with a PMID, DOI, NCT number, NIH grant number or SEC filing reference on every source. Every identifier it hands back was retrieved. None are generated.

It can also evaluate a claim as it stood in an earlier year, restricting evidence to what existed by then, so a claim is judged on what was knowable at the time rather than on how it turned out.

Connect it

The hosted server is at https://api.zetesis.science/mcp, over Streamable HTTP. No account, key or token is required.

Claude Code:

claude mcp add --transport http zetesis https://api.zetesis.science/mcp

Claude Desktop (claudedesktopconfig.json):

{
  "mcpServers": {
    "zetesis": {
      "type": "http",
      "url": "https://api.zetesis.science/mcp"
    }
  }
}

Any other MCP client:

For Gemini's settings.json, use httpUrl rather than url; the latter is SSE and will not connect. Full setup notes:

Tools

zetesisscope** routes the claim and returns the diligence apparatus for its class: the questions a reviewer would ask, structured by substrate, methods, cohort and risk of bias, a failure-pattern taxonomy carrying the companies each pattern was derived from, and the edge cases where those patterns were wrong. A checklist that only ever fires positive teaches over-rejection, so the counterexamples ship alongside it.

zetesisevidence** runs the searches and returns a deduplicated bundle from Europe PMC, ClinicalTrials.gov, openFDA, NIH RePORTER and SEC EDGAR, every source carrying a hard public identifier, followed by the grading rubric so you grade the evidence yourself in context.

Neither of those calls a language model. They return in under a second, cost nothing to run, and send nothing to a model provider. That is usually the answer a security reviewer is looking for.

evaluateclaim** produces Zetesis's own graded reading server-side. Slower, and only needed when the assessment itself is the deliverable rather than the evidence.

verifyattestation** re-checks a signed Zetesis record to confirm its claim, evidence and conclusion have not been altered since signing. Needs no account.

Claim classes: genomics and Mendelian randomisation, single-cell, bulk omics, CRISPR screens, clinical trials, real-world evidence, AI clinical decision support, diagnostics, preclinical models, cell and gene therapy, structural biology.

Why the year fence matters

Ask a general model about a 2020 claim today and it answers with years of hindsight; the publication that mattered at the time is buried under everything published since.

Measured on a control claim: unfenced retrieval missed the pivotal publication entirely and scored 35% evidence coverage. Fenced to the claim's own year, the same query set retrieved it and coverage rose to 79%. So the fence is not only about honesty in retrospect. It is a retrieval precision feature.

Set as_of to the year a claim was made for anything that is not brand new.

Try it

What did the published evidence actually support about aducanumab and cognitive
decline at the end of 2019, using only sources available by then?

Shortened. The full README is on GitHub.