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Clinicaltrials

by io.github.pipeworx-io

Ct Search

ct_search
Read-onlyIdempotent

Search ClinicalTrials.gov for clinical trials — find trials and look up a NAMED trial by keyword, condition, drug/therapy, status (e.g. 'Recruiting'), or phase (e.g. 'Phase 2'). Use for "clinical trials for <disease/drug>", or to locate a specific study like "the FLOW trial", "semaglutide kidney outcomes trial" (use ct_get_study for its full design/results). Returns NCT IDs, titles, status, enrollment, and sponsor info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results (1-100, default 10)
phaseNoFilter by phase: EARLY_PHASE1, PHASE1, PHASE2, PHASE3, PHASE4 (or NA). Roman numerals ("Phase III") and comma-separated unions ("PHASE2,PHASE3") are accepted.
queryYesThe SUBJECT of the search — a condition, drug, or keyword (e.g., "GLP-1 receptor agonist", "breast cancer immunotherapy"). Prefer the topic alone over a full sentence: put status in `status` and phase in `phase` rather than in the text. Conversational framing is stripped automatically, and the term actually searched is echoed back as `query_used`.
statusNoFilter by overall status: RECRUITING, ACTIVE_NOT_RECRUITING, COMPLETED, TERMINATED, WITHDRAWN, ENROLLING_BY_INVITATION, SUSPENDED, NOT_YET_RECRUITING. Several may be combined as a comma-separated union, e.g. "RECRUITING,NOT_YET_RECRUITING".
sponsorNoFilter by the trial's registered LEAD sponsor (e.g., "Pfizer", "Novo Nordisk"). Widen to trials the company only partners on with sponsor_match: "lead_or_collaborator".
sponsor_matchNoWhich sponsor role the name must fill. "lead" (the default) returns only trials whose registered LEAD sponsor is that company. "lead_or_collaborator" also returns trials led by someone else that list the company as a collaborator — typically academic trials of the company's drug. Every returned study carries sponsor_match_field naming which one matched.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
studiesYesList of formatted trial summaries
total_countYesTotal number of matching trials

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds the return field set (NCT IDs, titles, status, enrollment, sponsor) and the query normalization behavior (conversational framing stripped, echoed as query_used), which sets useful expectations. No contradiction.

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?

Two dense sentences with front-loaded purpose, concrete usage examples, an explicit alternative, and a concise return summary. Every sentence earns its place with no filler.

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 read-only search tool with a rich output schema, fully documented parameters, and annotations covering safety, the description leaves no critical gaps. It also covers query normalization and sponsor matching behavior, making the tool easy to invoke correctly.

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 coverage is 100% with rich descriptions for every parameter, including enums, union syntax, default limit, and sponsor_match semantics. The main description adds example phrasings but does not meaningfully go beyond what the schema already provides, so the baseline 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 opens with a specific verb and resource ('Search ClinicalTrials.gov for clinical trials') and immediately scopes the search dimensions (keyword, condition, drug/therapy, status, phase). It also distinguishes itself from ct_get_study, so an agent can select it correctly without opening the schema.

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 says 'Use for' with concrete query examples like 'clinical trials for <disease/drug>' and named-trial lookup, and directs full design/results queries to ct_get_study. This gives the agent clear when-to-use guidance and names an alternative.

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

A3.6/5.0
Disambiguation1/5

Several tools appear to do the same thing at the top level: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicate routing entry points, and deep_research overlaps heavily with them. Among the ct_* tools, ct_count_by_condition, ct_competitive_landscape, ct_sponsor_pipeline, and ct_compare_sponsors all provide overlapping counting/landscape functionality, making correct selection genuinely ambiguous.

Naming Consistency4/5

The overwhelming majority of tools use lowercase snake_case and mostly follow a verb_noun or domain-prefixed pattern (ct_search, ct_get_study, list_subscriptions, validate_claim, resolove_entity). Some names are noun phrases rather than verbs (ct_competitive_landscape, entity_profile, polymarket_edge_tracker) but the overall style is consistent and readable, with only minor deviations.

Tool Count2/5

44 tools is far too many for a server named 'Clinicaltrials'; only 13 tools are actually clinical-trials-specific while the rest span general data lookup, prediction markets, memory, subscriptions, and npm scanning. The count is inflated by redundant entry points (ask_pipeworx/beta/grounded) and overlapping ct tools, making the set feel heavy and unfocused.

Completeness4/5

For the clinical-trials registry domain, the surface is largely complete: search, full study details, results summaries, condition counts, sponsor pipelines, location-based lookup, recent updates, and catalyst tracking are all represented. Minor gaps exist (e.g., historical versions/protocol amendments and advanced filter combinations), but most could be worked around via the universal ask_pipeworx router.

Resources