Skip to main content
Glama

Evaluate a scientific or AI claim

evaluate_claim
Read-only

Run Zetesis's graded reading of a scientific, biomedical, clinical, or AI/ML claim.

Grades each evidence dimension and returns an overall reading, with every source
cited by hard public id (PMID, DOI, NCT, NIH grant, SEC filing). Covers drug,
omics, target-validation, diagnostic, and AI-model claims.

Runs the hosted engine end to end and typically takes a couple of minutes, long
enough that some clients background or time out the call. The output is Zetesis's
own reading as a finished artifact. zetesis_scope and zetesis_evidence return the
same dated sources and the same grading rubric immediately and without running a
model, at full source depth rather than abstract level.

A signed, independently re-checkable dossier is available by request at the
Zetesis site.

Args:
    claim: the claim to evaluate, in one or two sentences.
    context: optional background (stage, field, the decision at hand).
    documents: optional source text (a deck, abstract, or paper).
    as_of: year the claim was made. Set it for any claim that is not brand new.
        It restricts evidence to what existed then, which both keeps later
        outcomes out of the reading and sharply improves what is retrieved: left
        open, a pivotal trial report competes with every paper published since
        and gets buried.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoYear 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.
claimYesThe claim to evaluate, in one or two sentences.
contextNoOptional background: stage, field, or the decision at hand.
documentsNoOptional source text to evaluate against, such as a deck, abstract or paper.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • 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 / context / description
      Added value: +"Optional background: stage, field, or the decision at hand."
    • addedInput schema / properties / documents / description
      Added value: +"Optional source text to evaluate against, such as a deck, abstract or paper."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses that this runs an end-to-end hosted engine, can take minutes, and may be backgrounded or timed out by clients. It also clarifies that the output is Zetesis's own finished reading and adds the as_of temporal-restriction behavior, aligning with openWorldHint.

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, then alternates context, alternatives, and parameter guidance in a logical flow. It is longer than average, and the Args section partly repeats schema descriptions, but the extra latency warning and sibling differentiation justify the length.

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?

For a complex tool with an output schema and rich annotations, this description is remarkably complete: it covers what the tool returns, how long it takes, when to use sibling tools instead, and how to set as_of correctly. Nothing essential for selecting and invoking the tool is missing.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds value beyond the schema. It explains claim length, gives concrete examples for context and documents, and enriches as_of with a vivid retrieval-burial example about pivotal trial reports being drowned out by later publications.

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 opens with a specific action and resource: 'Run Zetesis's graded reading of a scientific, biomedical, clinical, or AI/ML claim.' It then clarifies what the tool produces—graded evidence dimensions, an overall reading, and hard public IDs for every source—so an agent can immediately distinguish it from the sibling evidence/scope tools.

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

Usage Guidelines5/5

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

The description explicitly names zetesis_scope and zetesis_evidence as alternatives that return the same grading rubric and dated sources immediately and without running a model. The latency warning ('typically takes a couple of minutes') further tells an agent when it might prefer those siblings or background the call.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.