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.

Chat in the terminal

uv tool install zetesis     # or: pipx install zetesis
zetesis

zetesis opens a chat in the terminal with the hosted Zetesis agent. Paste a claim, or drop in a PDF, DOCX or PPTX path, and watch it scope the claim, search the dated record and grade each dimension. Follow-up questions use the same evidence. /help lists the commands (/attach, /sources, /new, /web). No account or key is needed.

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 (claude_desktop_config.json):

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

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

Tools

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.

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?

Then ask the same question without the year and compare. The difference is the point.

Privacy

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.

The Python client

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:

claude mcp add zetesis --env ZETESIS_TOKEN=zk_... -- uvx --from zetesis zetesis-client

Run from a terminal, zetesis opens the chat above. Launched by an MCP host over stdio, it runs this client, so zetesis and zetesis-client both work in MCP configs.

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.science

Security

Report vulnerabilities privately to avidan.r@zetesis.science. See SECURITY.md.

MIT licensed. The hosted engine is a separate service.

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mcp-name: io.github.reutavidan/zetesis