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Clinicaltrials

by io.github.pipeworx-io

Ct Compare Sponsors

ct_compare_sponsors
Read-onlyIdempotent

Compare 2–5 NAMED lead sponsors on ClinicalTrials.gov under identical condition, recruitment-status, and phase filters. Returns full matching counts, a ranked comparison, and a small study sample for each sponsor. Use for questions like "who has more recruiting Phase 3 obesity trials, Novo Nordisk or Eli Lilly?" For an open "who are the TOP sponsors of X trials" question with no names given: the registry API has no group-by, so a true ranking is not computable — NEVER invent a candidate list to fake one; say the registry cannot rank sponsors and offer to compare specific named sponsors. Counts use the registered lead-sponsor field; registry records do not establish asset ownership, pipeline value, or probability of success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
phaseNoOptional phase or comma-separated phase union, such as PHASE3.
statusNoOptional status or comma-separated status union, such as RECRUITING.
sponsorsYesTwo to five sponsor names to compare under the same filters.
conditionNoOptional condition or disease, such as "obesity".
sample_limitNoRepresentative studies per sponsor (0-10, default 3).

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, so the description adds valuable nuance: counts use the registered lead-sponsor field, the registry cannot compute true rankings, and the tool does not establish asset ownership or pipeline value. It also warns against fabricating a candidate list, which is critical behavioral disclosure beyond structured 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?

Four sentences with no filler: the first defines function, second gives a concrete use case, third covers exclusion and alternative, fourth discloses caveats. Every sentence earns its place, and key info is front-loaded.

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 tool with 5 parameters and no output schema, the description covers the return shape (matching counts, ranked comparison, study sample), scope (lead-sponsor field), limitations (no registry group-by), and data semantics (no ownership/pipeline implications). This is fully complete for an agent to invoke and interpret the tool confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by clarifying that filters are 'identical' across all sponsors, and that the output includes a 'ranked comparison' and 'small study sample'—which maps to sample_limit. This goes beyond the schema's per-parameter descriptions, earning a 4.

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 ('Compare') and precise resource ('2–5 NAMED lead sponsors on ClinicalTrials.gov') under identical filters, immediately distinguishing it from sibling tools like ct_search or ct_sponsor_activity. The example question ('who has more recruiting Phase 3 obesity trials, Novo Nordisk or Eli Lilly?') further anchors the purpose.

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?

It explicitly states when to use the tool ('Use for questions like...') and when not to ('For an open... who are the TOP sponsors... with no names given... NEVER invent...'), providing a clear alternative (offer to compare named sponsors). This is exemplary guidance on tool selection.

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