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Clinicaltrials Find Eligible

clinicaltrials_find_eligible
Read-onlyIdempotent

Match patient demographics and conditions to eligible recruiting clinical trials. Provide age, sex, conditions, and location to find studies with matching eligibility criteria, contact information, and recruiting locations. Results are re-ranked so studies whose own condition matches a requested condition surface above tangential matches from ClinicalTrials.gov's fuzzy condition search. Each candidate returns only the sites matching the requested location (capped by locationLimit), not the study's full registered site list — a large trial can register hundreds of sites worldwide. When none of a candidate's matched sites is recruiting, its nearest recruiting site is added, so an enrollable site is never hidden behind a closer closed one. Fetch a study's complete record with clinicaltrials_get_study_record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageYesPatient age in years.
sexYesPatient's biological sex. Use 'ALL' to include studies regardless of sex restrictions.
locationYesPatient location as `{ country (required), state?, city? }`. Country is required; state/city narrow the match. For radius-based geographic search, use clinicaltrials_search_studies with geoFilter.
conditionsYesMedical conditions or diagnoses, e.g. ["Type 2 Diabetes", "Hypertension"]. Each entry is matched as a condition (multi-word entries match as a phrase); multiple entries are combined with OR, so studies for any listed condition qualify. Returned studies are re-ranked so those whose own condition list names a requested condition rank above tangential matches the upstream fuzzy search pulls in via the MeSH umbrella.
maxResultsNoMaximum results to return.
locationLimitNoCap on the sites returned per candidate. Each candidate keeps only the sites matching the requested location at the narrowest level that matched (city, else state, else country), capped at this many; the rest of the study's registered sites are omitted. The cap governs those matched sites — when none of them is recruiting, the candidate's nearest recruiting site is added on top of it, so a candidate can carry one site more than this. Raise it to see more nearby sites, or fetch the complete site list with clinicaltrials_get_study_record. Each candidate reports totalLocations / matchedLocations / locationsTruncated / nearestRecruitingSiteAdded in locationSummary only when the bound actually dropped sites.
recruitingOnlyNoOnly include actively recruiting studies.
healthyVolunteerNoWhether the patient is a healthy volunteer. When true, only studies accepting healthy volunteers are queried.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
funnelNoMatch counts at each filter stage. Shows where the funnel collapsed — e.g., conditionMatched=298 but demographicsMatched=2 means age/sex/status are the constraint.
noticeNoRecovery guidance when no studies matched — identifies which filter stage collapsed and suggests how to broaden. Absent when results are returned.
studiesNoMatching studies with eligibility and location fields. Each candidate's protocolSection.contactsLocationsModule.locations is BOUNDED to the sites matching the requested location (capped at locationLimit) plus, when none of those is recruiting, the candidate's nearest recruiting site — not the study's full registered site list. A candidate whose sites were bounded also carries a top-level locationSummary object — { totalLocations, matchedLocations, locationsTruncated, nearestRecruitingSiteAdded?, retrieveFullStudyWith } — absent when nothing was dropped; nearestRecruitingSiteAdded is present only when that extra site was added. Fetch a study's complete record and site list with clinicaltrials_get_study_record.
totalCountNoTotal matching studies from the API.
searchCriteriaNoNormalized search criteria applied to this eligibility query, including the exact upstream query strings needed to reproduce the full match set via clinicaltrials_search_studies (replay with includeUnknownEnrollment=true, which find_eligible always sets).

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint, openWorldHint, and idempotentHint annotations, the description discloses several non-obvious behaviors: results are re-ranked so direct condition matches surface above fuzzy MeSH matches; each candidate returns only location-matched sites capped by locationLimit; and when no matched site is recruiting, the nearest recruiting site is added. These details materially change how an agent interprets the results.

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 information-dense with no filler. It front-loads the primary purpose, then explains ranking and site-filtering behavior, and closes with an alternative routing. The paragraph is structured logically, though slightly verbose; a tighter split into a few bullet-like sentences would be ideal, but it remains efficient.

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 tool's complexity (8 params, nested location object, output schema), the description covers everything an agent needs to call it correctly: matching logic, ranking, site selection rules, the cap behavior, fallback to nearest recruiting site, and even mentions the locationSummary fields. It also references the alternative tool for complete records, making the choice context complete.

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?

The input schema already describes each parameter, but the description adds critical semantics: conditions are matched as phrases and OR'd together, the location object's narrowing precedence (city > state > country), the exact behavior of locationLimit (cap plus the possibility of one extra site), and the meaning of locationSummary fields. This goes well beyond the schema descriptions.

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: 'Match patient demographics and conditions to eligible recruiting clinical trials.' It names the core inputs (age, sex, conditions, location) and explicitly contrasts itself with siblings like clinicaltrials_get_study_record and clinicaltrials_search_studies, making the purpose unambiguous.

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 alternatives for fetching a full record ('Fetch a study's complete record with clinicaltrials_get_study_record') and for radius-based search ('For radius-based geographic search, use clinicaltrials_search_studies with geoFilter'). It implies when to use this tool (patient matching) but does not state a formal when-not-to-use condition, so it falls short of the top tier.

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

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TDQS

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct action: finding eligible trials, resolving field names, discovering field values, counting studies, fetching full records, fetching results, and searching. Even the count tool is clearly separated from search by its lightweight, no-data purpose. No two tools have ambiguous boundaries.

Naming Consistency5/5

All tools follow a strict 'clinicaltrials_' prefix followed by a descriptive verb_noun pattern (find_eligible, get_study_record, search_studies). The naming is uniform in snake_case and clearly indicates the operation and object. This consistency makes the tool set predictable.

Tool Count5/5

Seven tools is well within the ideal range for a focused MCP server. Each tool covers a necessary aspect of the ClinicalTrials.gov API (search, retrieval, counting, field exploration, results, eligibility matching) without bloat or redundancy. The scope feels complete for a read-only clinical trials interface.

Completeness5/5

The tool set covers the full read-only lifecycle: discovering the data model, searching, counting, fetching protocol records, fetching results, and matching patients to trials. There are no obvious gaps like missing location details or result statistics (both handled via parameters). The server fulfills its stated purpose without dead ends.