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Clinicaltrials

by io.github.pipeworx-io

Ct Recent Updates

ct_recent_updates
Read-onlyIdempotent

Track recent ClinicalTrials.gov activity by a specific event class within a verifiable date window. Three DISTINCT recency events, chosen via date_type: "last_update" (any edit to a study, ~5k/week — the default), "first_posted" (a study newly REGISTERED, ~1.3k/week), or "results_posted" (RESULTS first posted, ~140/week). Pass since (and optional until, YYYY-MM-DD) to bound a window — e.g. the last 7 days — and the returned total_count is the exact number of studies in that window so you can verify a weekly count from the output. Every study returns all THREE dates separately (first_posted_date, last_update_post_date, results_first_post_date) plus the design fields a screen needs (study_type, primary_purpose, phase, status, enrollment, conditions, interventions, sponsor). Sorted by the chosen event date, newest first. Use for "new trials registered this week", "trials that posted results in the last month", "recently updated diabetes studies".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results per page (1-100, default 20). total_count reports the full window size regardless of limit.
queryNoOptional search term (condition/drug/keyword) to narrow results.
sinceNoWindow start (inclusive), YYYY-MM-DD. e.g. for a weekly review pass 7 days ago. Applies to the chosen date_type.
untilNoOptional window end (inclusive), YYYY-MM-DD. Omit for open-ended (up to today).
statusNoOptional overall-status filter (validated; e.g. RECRUITING, or a comma-union like "RECRUITING,COMPLETED").
date_typeNoWhich recency event to track/sort/window by: "last_update" (any edit, default), "first_posted" (new registration), "results_posted" (results first posted). These are different event classes with very different volumes — pick deliberately.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
studiesYesList of formatted trial summaries
total_countYesTotal recently updated trials

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, open-world, non-destructive behavior. Description adds details: total_count verifies exact window size, all three dates returned, sorting by chosen event date. No contradictions with annotations.

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?

Single paragraph with front-loaded purpose, logical flow from events to parameters to return structure to examples. Every sentence adds value, no redundancy or fluff. Efficiently packs substantial information.

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?

Covers all key aspects: event types, parameter usage, return fields, example scenarios. Could mention pagination or error cases, but given output schema exists and annotations are rich, the description is sufficiently complete for an AI agent to use 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 descriptions cover all 6 parameters (100% coverage). Description adds significant value: explains date_type event volumes and deliberate selection, clarifies since/until window bounding, and notes limit vs total_count distinction. Goes well beyond schema alone.

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 tracks recent ClinicalTrials.gov activity by event class, with explicit distinctions between three recency events and their volumes. It differentiates itself from siblings like ct_search or ct_count_by_condition by focusing on date-windowed activity tracking.

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?

Provides concrete use cases ('new trials registered this week', 'trials that posted results in the last month') and explains when to use each date_type. Does not explicitly name sibling alternatives or state when not to use, but the examples make the context clear.

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