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Sairajiv-13

Clinical Trials MCP Server

by Sairajiv-13

Eligibility Check

eligibility_check

Check whether a patient meets a clinical study's structured age and sex eligibility criteria using an NCT ID, returning a verdict and reason for human review.

Instructions

Check the deterministic eligibility criteria of a study for a patient.

Only age range and sex are checked, because those are the fields the registry reports in structured form. The verdict is 'meets', 'does_not_meet', or 'cannot_determine', with a reason per check. The full free-text criteria are included in the result for human review; they are never interpreted by this tool, and the verdict is not a final eligibility decision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageNoPatient age in years. Fractional values are fine (0.5 = 6 months).
sexNoPatient sex: 'female' or 'male'.
nct_idYesNCT number, e.g. 'NCT04354155'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOnly age and sex are checked here, because they are the fields the API reports in a structured form. The remaining criteria are free text and are returned in criteria_text for a person to review. This is not a final eligibility decision.
checksNo
nct_idYes
verdictYes
criteria_textNoFull free-text criteria from the study, for human review.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does it well: it discloses the exact verdict vocabulary ('meets', 'does_not_meet', 'cannot_determine'), that each check carries a reason, that free-text criteria are returned but never interpreted, and that the result is not a final decision. What it omits is behavior when age/sex are left null (presumably 'cannot_determine') and any failure/error behavior.

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?

Four compact sentences, front-loaded with purpose and scope, then verdict semantics, then the disclaimer. Nothing is padded, though the closing clause about free-text criteria and non-finality could be tightened slightly.

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?

An output schema exists, so return-value shape need not be re-described, and the description still adds the crucial verdict semantics and interpretation limits that the schema cannot express. It is complete for correct invocation except for the unspecified behavior when age and sex are omitted.

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 defines age (years, fractional allowed) and sex ('female'/'male'), establishing the baseline of 3. The description adds a mild semantic link by stating these are the only two fields actually checked, but gives no guidance on omitting them or on how null values are treated.

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?

Specific verb+resource ('Check the deterministic eligibility criteria of a study for a patient') with explicit scope narrowing: only age range and sex are evaluated. This is unmistakably distinct from siblings search_trials, get_trial, and recruiting_sites, so an agent can route correctly without opening any schema.

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 clearly establishes the context of use — deterministic age/sex screening against a study, with the verdict explicitly not a final eligibility decision — which tells the agent this is a preliminary screening step. It never names an alternative tool or states a when-not condition, so it stops short of full routing guidance.

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