A
licenseB
qualityC
maintenanceEnables AI agents to verify technical claims against supplied evidence, identify unsupported assumptions and contradictions, and recommend the smallest next check before acting.
5
MIT
Zetesis Scientific Due Diligence
Server Details
Scientific due diligence on a claim: reviewer questions, failure patterns, dated evidence.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- reutavidan/zetesis
- GitHub Stars
- 0
- Server Listing
- zetesis
Available Tools
4 toolsevaluate_claimEvaluate a scientific or AI claimARead-onlyInspect
REQUIRES A TOKEN. This tool refuses without one. If you do not have a Zetesis token, do not call it: use zetesis_scope then zetesis_evidence instead, which need no token, run no model, return immediately, and let you read the sources at full depth. A token can be requested at https://api.zetesis.science/request-access.
Run Zetesis's own graded reading of a scientific, biomedical, clinical, or AI/ML claim.
SLOW: this 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. It is not
the default route.
For almost every case, call zetesis_scope and then zetesis_evidence instead. Those
return immediately, run no model, cost nothing, hand you the same dated evidence
and the same grading rubric, and let you read the sources at full depth in your own
context rather than at abstract level.
Reach for this tool only when someone explicitly wants Zetesis's own verdict as the
artifact, rather than your analysis of the evidence Zetesis retrieves. It grades
each evidence dimension and returns an overall reading with sources cited by hard
id (PMID, DOI, NCT, NIH grant, SEC filing). Works for drug, omics,
target-validation, diagnostic, and AI-model claims. 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.ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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. | |
| documents | No | Optional source text to evaluate against, such as a deck, abstract or paper. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses critical operational behavior: it requires a token and 'refuses without one,' it is slow and may trigger client timeouts or backgrounding, it runs the hosted engine end to end, and its output cites sources by hard IDs. This goes well beyond what the annotations convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The definition is clearly structured and front-loads the most important warnings, but it is repetitive: the guidance to prefer zetesis_scope and zetesis_evidence appears twice in nearly identical form, and the no-token / fast / no-model benefits are restated. Several sentences could be merged without losing information.
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, authenticated, slow, hosted-engine tool, the description is remarkably complete: it covers prerequisites, failure modes, latency, alternatives, supported claim domains, output characteristics, and optional parameter behavior. The presence of an output schema means detailed return-value documentation is not required here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters. The description's Args section mostly repeats the schema text rather than adding new semantics; the only mild addition is explaining why as_of improves retrieval. This is adequate but not additive beyond the baseline.
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-oriented statement: 'Run Zetesis's own graded reading of a scientific, biomedical, clinical, or AI/ML claim.' This clearly distinguishes the tool from the sibling evidence-retrieval tools by identifying the unique artifact it produces: Zetesis's verdict.
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 gives explicit when-to-use and when-not-to-use guidance: 'If you do not have a Zetesis token, do not call it,' 'For almost every case, call zetesis_scope and then zetesis_evidence instead,' and 'Reach for this tool only when someone explicitly wants Zetesis's own verdict.' This is model-actionable routing advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_attestationVerify a Zetesis attestationARead-onlyInspect
Verify a Zetesis attestation, confirming an evaluation's claim, evidence, and conclusion have not been altered since it was signed. Use when someone has a Zetesis dossier or attestation and wants to independently re-check it.
Args:
attestation_json: the full attestation object as JSON text.ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| attestation_json | Yes | The full Zetesis attestation object, as JSON text. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and non-destructive, and the description adds meaningful behavioral context by explaining the integrity-check semantics: it confirms that the evaluation's claim, evidence, and conclusion have not been altered since signing. This goes beyond the annotations and clarifies what verification actually checks.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the core purpose and verification semantics appear first, followed by a clear usage condition and a single parameter definition. Every sentence earns its place, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema and read-only annotations, the description covers the essential context: what the tool does, when to use it, and what input is required. It could be slightly more complete by noting what a successful verification result looks like, but the output schema likely covers the return shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description's Args section essentially restates the schema's parameter documentation ('the full attestation object as JSON text'). The description adds no new meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
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 uses a specific verb and resource: it verifies a Zetesis attestation and precisely states what is confirmed—that claim, evidence, and conclusion have not been altered since signing. This clearly differentiates it from sibling tools like evaluate_claim, zetesis_evidence, and zetesis_scope, which serve different purposes.
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 explicit context: use it when someone has a Zetesis dossier or attestation and wants to independently re-check it. It does not mention explicit exclusions or alternative sibling tools, but the stated use case is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zetesis_evidenceRetrieve dated public evidence for a claimARead-onlyInspect
Step 2 of a Zetesis evaluation. Runs the search queries against public evidence sources (Europe PMC, ClinicalTrials.gov, openFDA, and optionally NIH RePORTER and SEC EDGAR) and returns a deduplicated bundle where every source carries a hard public id, followed by the Zetesis grading rubric so you can grade the dimensions yourself.
With as_of set, retrieval is fenced to sources published, registered, or filed on or
before 31 December of that year, and two fields that leak later outcomes are
suppressed: a trial's present-day status, and FDA labels effective after the cutoff.
That makes it possible to judge a claim on what was actually knowable at the time.
Runs no model, costs nothing, and returns immediately. Use zetesis_scope first to
choose the queries. Together the two make up the preferred route into Zetesis.
Args:
queries: 3 to 6 short keyword phrases. Query 1 should be the bare name of the
thing claimed about (a drug, compound code, model, or gene); query 2 that
name plus at most two outcome words. Longer phrases retrieve commentary
rather than the primary report.
If the claim names no agent at all (a behaviour, diet, procedure, exposure
or policy), anchor on the field's own technical term instead of the lay one
("time restricted eating", not "intermittent fasting"), pair it with the
outcome as the field measures it, and allow one design word such as
crossover or randomized in one later query. Without an entity name that
design word is the only thing separating a trial report from a review of
trial reports.
as_of: optional cutoff year, as used in zetesis_scope.
include_capital: also retrieve funding and public-filing signal.ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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. | |
| queries | Yes | Three to six short keyword phrases. Query 1 should be the bare name of the thing claimed about; query 2 that name plus at most two outcome words. | |
| include_capital | No | Also retrieve funding and public filing signal from NIH RePORTER and SEC EDGAR. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description greatly exceeds annotations by explaining that the tool 'runs no model, costs nothing, and returns immediately', deduplicates results, requires hard public IDs, and suppresses later-dated fields when as_of is set. This gives the agent a precise behavioral model of what will happen and what will be returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every section earns its place: it front-loads the tool's role and sourcing, then covers behavior, routing, and argument semantics in a structured and readable way. The complexity of query construction justifies 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 tool with three parameters, no nested objects, and an output schema, the description covers the agent's needs: when to use it, what it returns, how the as_of parameter changes behavior, and how to formulate queries. Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds substantial value: query construction rules for entity-named claims versus non-entity claims, the rationale for design words, and as_of semantics are all explained in ways the schema does not. This is exactly the kind of guidance an agent needs to call the tool correctly.
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 verb and resource: it 'Runs the search queries against public evidence sources' and 'returns a deduplicated bundle'. It is clearly positioned as 'Step 2 of a Zetesis evaluation', which distinguishes it from zetesis_scope and from the sibling evaluation 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?
Explicitly instructs the agent to 'Use zetesis_scope first to choose the queries' and states that together they form 'the preferred route into Zetesis'. It also explains when to set as_of and why, giving clear operational context beyond the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zetesis_scopeScope a claim for evaluationARead-onlyInspect
START HERE for any scientific, biomedical, clinical, or AI/ML claim.
This is the default entry point to Zetesis. It returns the source catalog and the
grading rubric so you can work out which evidence dimensions actually matter for
THIS claim and what to search for, then you call zetesis_evidence with the queries
you chose and grade what comes back.
Use it whenever a claim needs assessing, screening, sanity-checking, or due
diligence: a research claim, a study, a paper, an abstract, a preprint, a grant, or
a pitch. Also use it before citing a paper or statistic in your own answer.
Returns three things: the source catalog (where to look), the diligence
apparatus for this claim's class (what to ask, and the failure patterns that
have caught comparable claims before), and the scoping rubric.
Runs no model, costs nothing, and returns immediately. This two-step route is
preferred over evaluate_claim on every axis that matters: it is instant rather than
minutes, it reads the sources at full depth in your context rather than at abstract
level, and it supports evaluating a claim as it stood in an earlier 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.ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/openWorld annotations, it discloses that it 'runs no model, costs nothing, and returns immediately,' which is useful for agent expectations. It also clarifies what the response contains (source catalog, diligence apparatus, scoring rubric) and how as_of changes evaluation to time-of-claim grading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the most important guidance ('START HERE'), uses short imperative paragraphs, and each section earns its place. It is longer than average but the length is justified by the number of parameters and the need to explain the two-step workflow.
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 tool with four parameters, an output schema, and sibling tools, the description fully covers when, why, how, and what follows. It explains the workflow, return contents, and parameter overrides, leaving no ambiguity for an agent selecting or invoking it.
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 covers 100% of parameters, so the baseline is 3, but the description adds behavioral meaning for as_of ('grade how it was derived at the time rather than how it turned out') and claim_class ('pass one back if the routing picked wrongly'). Claim and context are already clear from schema, so extra credit is modest.
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 'START HERE' and explicitly says the tool returns the source catalog and grading rubric so the agent can determine which evidence dimensions matter. It names the exact verb/resource (scope a claim) and distinguishes itself from evaluate_claim and zetesis_evidence by positioning it as the entry point.
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 states this is the default entry point and says 'Use it whenever a claim needs assessing, screening, sanity-checking...' It also explains when not to use it by saying the two-step route is preferred over evaluate_claim and that zetesis_evidence is the follow-up call.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
4 tool updates
- Changed
evaluate_claim4 fields changed- added
Input schema / properties / as_of / descriptionAdded 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." - added
Input schema / properties / claim / descriptionAdded value: +"The claim to evaluate, in one or two sentences." - added
Input schema / properties / context / descriptionAdded value: +"Optional background: stage, field, or the decision at hand." - added
Input schema / properties / documents / descriptionAdded value: +"Optional source text to evaluate against, such as a deck, abstract or paper."
- Changed
verify_attestation1 field changed- added
Input schema / properties / attestation_json / descriptionAdded value: +"The full Zetesis attestation object, as JSON text."
- Changed
zetesis_evidence3 fields changed- added
Input schema / properties / as_of / descriptionAdded 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." - added
Input schema / properties / include_capital / descriptionAdded value: +"Also retrieve funding and public filing signal from NIH RePORTER and SEC EDGAR." - added
Input schema / properties / queries / descriptionAdded value: +"Three to six short keyword phrases. Query 1 should be the bare name of the thing claimed about; query 2 that name plus at most two outcome words."
- Changed
zetesis_scope4 fields changed- added
Input schema / properties / as_of / descriptionAdded 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." - added
Input schema / properties / claim / descriptionAdded value: +"The claim to evaluate, in one or two sentences." - added
Input schema / properties / claim_class / descriptionAdded value: +"Optional override for the routed claim class. The response lists every class; pass one back if the routing picked wrongly." - added
Input schema / properties / context / descriptionAdded value: +"Optional background: stage, field, or the decision at hand."
4 tool updates
- First observed
evaluate_claim - First observed
verify_attestation - First observed
zetesis_evidence - First observed
zetesis_scope
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TDQS
A4.6/5.0
Disambiguation5/5
Each tool has a distinct role: scoping, evidence retrieval, hosted evaluation, and attestation verification. The descriptions explicitly route the agent between them, so overlap is minimal.
Naming Consistency3/5
Two tools follow verb_noun naming (evaluate_claim, verify_attestation) while two follow zetesis_ + noun naming (zetesis_scope, zetesis_evidence). The set is readable and consistently snake_case, but the pattern is not predictable across all tools.
Tool Count5/5
Four tools is well-scoped for the server's purpose: scope, evidence, evaluate, and verify. Each tool earns its place and none is redundant.
Completeness5/5
The tool set covers the full due-diligence workflow from scoping and evidence retrieval to optional hosted evaluation and attestation verification. The token-gated evaluate tool is backed by a complete no-token route, so there are no critical dead ends.