Zetesis Scientific Due Diligence

Scope a claim for evaluation

zetesis_scope

Read-only

Scope a scientific, biomedical, clinical, or AI/ML claim for evaluation.

Returns three things: the source catalog, meaning where the evidence for this
claim class lives; the diligence apparatus for that class, meaning the questions
a domain reviewer asks and the failure patterns that have caught comparable
claims before; and the scoping rubric used to work out which evidence dimensions
bear on this particular claim and what to search for.

Applies to a research claim, a study, a paper, an abstract, a preprint, a grant or
a pitch, and to checking a paper or statistic before relying on it.

Runs no model, costs nothing, and returns immediately. The queries worked out from
its output are what zetesis_evidence takes as input. With as_of set, the apparatus
and rubric are framed for judging the claim as it stood at the end of that year.

Args:
    claim: the claim to evaluate, in one or two sentences.
    context: optional background (stage, field, the decision at hand).
    as_of: optional year. Set it to evaluate the claim as it stood at the end of that
        year, grading how it was derived at the time rather than how it turned out.
    claim_class: optional override for the routed claim class. The response lists
        every class; pass one back if the routing picked wrongly, or if nothing
        routed.

Input Schema

TableJSON Schema

Name	Required	Description
`as_of`	No	Year the claim was made. Set it for any claim that is not brand new: it restricts evidence to what existed then, which keeps later outcomes out of the reading and sharply improves what is retrieved.
`claim`	Yes	The claim to evaluate, in one or two sentences.
`context`	No	Optional background: stage, field, or the decision at hand.
`claim_class`	No	Optional override for the routed claim class. The response lists every class; pass one back if the routing picked wrongly.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 5, 2026

addedInput schema / properties / as_of / description

Added value: +"Year the claim was made. Set it for any claim that is not brand new: it restricts evidence to what existed then, which keeps later outcomes out of the reading and sharply improves what is retrieved."

addedInput schema / properties / claim / description

Added value: +"The claim to evaluate, in one or two sentences."

addedInput schema / properties / claim_class / description

Added value: +"Optional override for the routed claim class. The response lists every class; pass one back if the routing picked wrongly."

addedInput schema / properties / context / description

Added value: +"Optional background: stage, field, or the decision at hand."

First observedSep 1, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint and non-destructive behavior, and the description adds meaningful behavioral context: 'Runs no model, costs nothing, and returns immediately.' It also explains how as_of changes the framing of the returned apparatus and rubric, which goes beyond the structured annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, outputs, applicability, and cost, then uses a labeled Args section for parameter details. It is somewhat redundant with the schema's parameter descriptions, but the structure is scannable and every major section earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, 100% schema coverage, and four well-documented parameters, the description is complete enough for an agent to select and invoke the tool correctly. It covers what the tool returns, what inputs matter, the as_of behavior, and how the outputs feed into zetesis_evidence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers all parameters at 100%, so the baseline is 3. The description adds useful nuance beyond the schema: as_of is described as grading 'how it was derived at the time rather than how it turned out,' and claim_class includes the fallback 'or if nothing routed.' This extra guidance justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Scope a scientific, biomedical, clinical, or AI/ML claim for evaluation') and explicitly lists three concrete return artifacts: source catalog, diligence apparatus, and scoping rubric. It clearly establishes the tool as the front-end stage before zetesis_evidence, distinguishing it from the sibling evaluation and verification tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear applicability list ('a research claim, a study, a paper, an abstract, a preprint, a grant or a pitch') and pipeline guidance ('The queries worked out from its output are what zetesis_evidence takes as input'). It does not explicitly name alternatives like evaluate_claim or verify_attestation, but the sequencing and cost note make the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Zetesis Scientific Due Diligence tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.