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Clinicaltrials

by io.github.pipeworx-io

Ct Sponsor Trials

ct_sponsor_trials
Read-onlyIdempotent

List the trials a company LEADS on ClinicalTrials.gov, by sponsor or organization name. Returns status, phase, and conditions to map a research pipeline. Matches the registered lead-sponsor field, so trials another organisation runs using the company's drug are excluded unless sponsor_match asks for them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results (1-100, default 20)
phaseNoOptional phase filter
statusNoOptional status filter
sponsorYesSponsor or company name (e.g., "Pfizer", "Novo Nordisk", "Moderna"). Matched against the registered LEAD sponsor by default.
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
sponsorYesSponsor name queried
studiesYesList of formatted trial summaries
total_countYesTotal trials by sponsor

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the operation safe/read-only/idempotent, so the description adds useful behavior beyond them: matching is against the registered LEAD sponsor, collaborator trials are excluded by default, and sponsor_match_field indicates which role matched. This is meaningful, non-obvious behavior.

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?

Three sentences, each earning its place: action/target, output/purpose, and the critical edge-case behavior. It is front-loaded and concise without omitting needed nuance.

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?

With 5 parameters, output schema present, and strong annotations, the description covers purpose, output fields, and the most important matching edge case. It lacks explicit alternative-routing or pagination/rate details, but those are secondary to correct invocation for this tool.

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 parameters, defaults, and enum choices. The narrative description adds context around sponsor_match's inclusion/exclusion behavior but doesn't need to compensate for schema gaps.

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 states a specific action ('List'), a resource ('trials a company LEADS on ClinicalTrials.gov'), and the key scope (registered lead-sponsor field). It clearly distinguishes this from generalized trial search or collaborator-focused tools.

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 gives clear context: use to map a company's research pipeline by lead-sponsor status/phase/conditions, and it explicitly notes the exclusion of collaborator-run trials unless sponsor_match asks. It doesn't name sibling alternatives explicitly, but the matching-behavior guidance is strong.

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

All Clinicaltrials toolsInspect
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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.

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