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Clinicaltrials

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that it probes each entity with 'ai_visibility_check' and returns a ranked list. It does not add contradictory behavioral info. With annotations covering safety, the description provides useful context about internal 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?

The description is two sentences long, front-loads the purpose, then provides a use case example, and finally describes the output. Every sentence adds value with no redundancy or fluff.

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?

Given the tool has no output schema, the description adequately describes the return format: 'ranked list with score, confidence, signal density per entity'. It also notes that 'models' can be omitted. The description is complete enough for the agent to understand what the tool does and what it returns.

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 explaining that the first entity in the array is treated as the 'subject' for narrative and the rest as competitors, which is not in the schema. This extra context helps the agent understand the intended ordering.

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 it compares AI visibility across multiple entities, using 'ai_visibility_check', ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like 'ai_visibility_check' (single entity) and 'compare_entities' (generic) by specifying the exact comparison mechanism and use case.

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?

The description explicitly states the tool is useful for competitive AI-marketing audits and gives an example question ('does Claude know about us as well as our competitors?'). It implies the tool should be used when side-by-side comparison is needed. However, it does not explicitly state when not to use it or mention alternatives.

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

A3.9/5.0
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