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
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- 0
- Server Listing
- zetesis
Available Tools
4 toolsevaluate_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 | ||
| claim | Yes | ||
| context | No | ||
| documents | No |
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 readOnlyHint and openWorldHint annotations, the description discloses consequential behaviors: it requires a token and refuses without one, it is slow and may cause clients to time out, and it returns a graded reading with sources cited by hard IDs. These details are not available from annotations or schema and materially affect whether an agent should invoke it.
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 long but well structured, with upfront warnings about tokens and latency before the function's purpose. It contains some repetition about the alternative tools and their benefits, but each paragraph earns its place by addressing authentication, speed, when to use, arguments, and output characteristics.
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 one required parameter, an output schema, and high behavioral complexity, the description is complete. It covers the required token, latency profile, fallback sibling workflow, exact argument semantics, supported claim domains, and expected output format, so an agent has enough context to decide and invoke correctly.
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?
Schema description coverage is 0%, so the description carries the full burden for parameter semantics. It compensates completely: claim is defined as 'the claim to evaluate, in one or two sentences'; context, documents, and as_of all receive concrete meanings, including the key temporal behavior of as_of restricting evidence to what existed in that year.
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 clearly states the tool's function: 'Run Zetesis's own graded reading of a scientific, biomedical, clinical, or AI/ML claim.' It uses a specific verb and resource, and it distinguishes itself from siblings by positioning zetesis_scope and zetesis_evidence as the default alternatives while this tool provides Zetesis's own 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 guidance: call this tool only when someone explicitly wants Zetesis's verdict as an artifact, and otherwise use zetesis_scope then zetesis_evidence. It also states a hard precondition: if no Zetesis token is available, do not call it. This leaves little ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_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 |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the readOnlyHint annotation by specifying the exact integrity check performed: confirming the claim, evidence, and conclusion have not been altered since signing. This adds meaningful behavioral context without contradicting the annotations. It does not describe the output format, but an output schema exists to cover that.
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 first sentence states the core function, the second gives the usage trigger, and the third documents the sole parameter. No unnecessary details or repetition are present.
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 single-parameter tool with an output schema and annotations already covering read-only and non-destructive behavior, the description is complete. It states what the tool does, when to use it, and what input to provide.
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?
With 0% schema description coverage, the description's 'Args' section is essential. It explains that attestation_json must be 'the full attestation object as JSON text', clarifying both completeness and format, which the bare schema does not convey.
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 verb and resource: 'Verify a Zetesis attestation', and explains exactly what is checked — that claim, evidence, and conclusion have not been altered since signing. This clearly differentiates it from sibling tools like evaluate_claim or zetesis_evidence, which address evaluation or evidence gathering rather than attestation integrity.
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 provides a clear use case: 'Use when someone has a Zetesis dossier or attestation and wants to independently re-check it.' This gives the agent context for when to invoke it, though it does not explicitly name alternatives or exclusions relative to the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zetesis_evidenceARead-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 | ||
| queries | Yes | ||
| include_capital | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare read-only/open-world/destructive false; the description adds substantial behavior: deduplication, hard public ids, inclusion of the grading rubric, as_of date fencing, suppression of later-leaking trial status and FDA labels, and 'Runs no model, costs nothing, and returns immediately'. No contradiction with the annotations exists.
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 front-loads purpose and behavioral facts before diving into Args, and the structure cleanly separates narrative guidance from parameter guidance. The longer query guidance earns its length because it teaches the agent how to avoid commentary instead of primary reports.
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?
With an output schema available, the description does not need to enumerate every return field. It covers sources, id guarantee, rubric inclusion, as_of behavior, cost/performance expectations, and pipeline integration with zetesis_scope, giving an agent enough context to invoke the tool correctly.
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?
Schema description coverage is 0%, but the Args section fully compensates. queries gets detailed construction rules, count, ordering, and a no-entity strategy with concrete examples; as_of is tied to cutoff semantics and zetesis_scope; include_capital is explained as funding/public-filing signal.
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 by identifying the tool as 'Step 2 of a Zetesis evaluation' and states exactly what it does: 'Runs the search queries against public evidence sources' and returns a deduplicated bundle with hard public ids. It names the concrete sources and sets up the relationship to zetesis_scope, making its role unmistakable.
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?
It explicitly instructs the agent to 'Use zetesis_scope first to choose the queries' and calls the pair 'the preferred route into Zetesis', which is strong sequencing guidance. However, it does not contrast the tool with evaluate_claim or verify_attestation, so exclusion/alternative guidance is incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zetesis_scopeARead-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 | ||
| claim | Yes | ||
| context | No | ||
| claim_class | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false; the description adds genuinely valuable behavioral context beyond that: 'Runs no model, costs nothing, and returns immediately.' It also discloses the routing behavior (the response lists every claim class so one can be passed back if routing picked wrongly) and the as_of semantics (grading how the claim was derived at the time, not how it turned out). This exceeds the annotation baseline without contradicting it.
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 longer than average but well organized into purpose, usage, returns, behavioral comparison, and Args sections with a strong front-loaded hook. Some repetition exists — the source catalog and rubric are mentioned in both the opening and the returns paragraph — but for a system entry point with four parameters and three siblings, the density is justified.
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?
The description covers the full workflow (scope, then call zetesis_evidence, then grade), summarizes the three return groups, explains every optional parameter's purpose, and documents the alternative path (evaluate_claim) with its trade-offs. An output schema exists to carry return-value detail, and annotations carry the safety profile, so nothing an agent needs to invoke this tool correctly 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?
Schema description coverage is 0%, so the description carries the full burden — and it fully compensates. The Args block explains all four parameters: claim gets a length guideline ('one or two sentences'), context gets examples ('stage, field, the decision at hand'), as_of gets rich temporal semantics, and claim_class gets an override workflow ('pass one back if the routing picked wrongly, or if nothing routed'). Every parameter gains meaning beyond its schema type.
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 for any scientific, biomedical, clinical, or AI/ML claim' and states a specific job: return the source catalog, grading rubric, and scoping apparatus so the agent can decide what to search for. It explicitly differentiates from siblings by naming zetesis_evidence as the follow-up call and favoring this route over evaluate_claim. The verb ('scope', 'returns') plus resource ('source catalog and grading rubric') is unambiguous.
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 guidance: 'Use it whenever a claim needs assessing, screening, sanity-checking, or due diligence' and even mandates it 'before citing a paper or statistic.' It directly compares against evaluate_claim ('preferred over evaluate_claim on every axis... instant rather than minutes, reads the sources at full depth') and routes onward to zetesis_evidence, so an agent knows exactly when to pick this tool over its alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
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/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
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Claim ownership of the server listing
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
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Discussions
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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.