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Clinicaltrials

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about returning live tool catalog mappings and the behavior difference with/without topic. No contradictions, but the simple nature of the tool means limited extra disclosure needed.

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 a single paragraph that front-loads example user questions and provides essential behavior details. Every sentence serves a purpose, though it could be slightly shortened without losing meaning.

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?

No output schema exists, but the description adequately explains the return format: category-bucketed example questions with tool and argument shape. It also mentions the live catalog of thousands of tools, giving agents sufficient context to use 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% (all parameters described). The description adds value by listing example topic values ('finance', 'pharma', etc.) and explaining the behavior change when topic is omitted vs provided, going beyond the schema definition.

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 returns category-bucketed example questions with exact tool mappings, and positions it as the onboarding entry point. The title 'What Can I Ask Pipeworx?' directly conveys its purpose, distinguishing it from siblings like ask_pipeworx or entity_profile.

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 this FIRST when you do not yet know what Pipeworx can do' and describes two usage modes: no arguments for full spread or topic parameter to focus. It also mentions meta-tools like ask_pipeworx as alternatives for further interaction.

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

A3.9/5.0
Disambiguation3/5

Tool families overlap in purpose—ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded, ct_search/ct_count_by_condition/ct_competitive_landscape, and ct_sponsor_trials/ct_sponsor_pipeline/ct_sponsor_activity all present multiple plausible entry points. The very detailed, cross-referenced descriptions help, but an agent still has to read carefully to avoid misselection.

Naming Consistency4/5

Nearly all tool names follow lowercase snake_case with recognizable prefixes like ct_, polymarket_, and pipeworx_, giving the set a strong overall pattern. The main deviation is noun-phrase names such as recent_changes, entity_profile, and pipeworx_trending instead of a more uniform verb-first convention.

Tool Count2/5

44 tools is far too many for a server named Clinicaltrials: only 13 are ct_* tools, while the other 31 are broad Pipeworx utilities covering prediction markets, memory, subscriptions, npm scanning, and AI visibility. The clinical-trial module itself is well-sized, but the server bundles substantial unrelated surface area.

Completeness4/5

The clinical-trial workflow is nearly complete: search, study details, results, counts, sponsor comparison and pipeline, location lookup, update tracking, and landscape mapping are all covered. Minor conveniences like saved searches or export are missing, but agents can work around them; there are no dead ends in the registry domain.

Resources