The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Zetesis listing page.
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.
The hosted server is at https://api.zetesis.science/mcp, over Streamable HTTP.
No account, key or token is required.
Claude Code:
Claude Desktop (claude_desktop_config.json):
Any other MCP client:
| Client | How |
|---|---|
| Microsoft Copilot Studio | Tools, then Add a tool, then Model Context Protocol. Server URL, auth None. |
| ChatGPT | Settings, then Connectors, then Developer mode. Add the URL. |
| Gemini CLI | gemini mcp add --transport http zetesis https://api.zetesis.science/mcp |
For Gemini's settings.json, use httpUrl rather than url; the latter is SSE and will not
connect. Full setup notes: https://api.zetesis.science/docs
zetesis_scope 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.
zetesis_evidence 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.
evaluate_claim produces Zetesis's own graded reading server-side. Slower, and only needed
when the assessment itself is the deliverable rather than the evidence.
verify_attestation 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.
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.
Then ask the same question without the year and compare. The difference is the point.
The evidence tools send nothing to a model provider. evaluate_claim processes claim text through
a model sub-processor, named along with retention terms and hosting region in the
privacy policy. Claim text is not logged; only metadata
(the routed class, depth, counts) is kept.
This repository also publishes a thin stdio MCP client to PyPI, which predates the hosted server
and exposes an older tool set (evaluate_claim, check_evaluation, verify_attestation,
account_status). It holds no keys and runs no model; every call is proxied to the hosted engine,
and it needs a token.
Prefer the hosted endpoint above. It needs no token and carries the current tools. The client remains for existing stdio setups:
Tokens: https://api.zetesis.science/request-access
ZETESIS_TOKEN sets the token (verification works without one)ZETESIS_API overrides the API base, default https://api.zetesis.scienceReport vulnerabilities privately to avidan.r@zetesis.science. See SECURITY.md.
MIT licensed. The hosted engine is a separate service.
mcp-name: io.github.reutavidan/zetesis