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Medical Codes

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it probes via ai_visibility_check and returns a ranked list with specific fields (score, confidence, signal density), providing behavioral context beyond 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?

Two sentences: first states core functionality, second gives use case and output structure. No wasted words, 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?

Description covers what the tool does, how it works (via ai_visibility_check), what output to expect (ranked list with score, confidence, signal density), and a concrete use case. Adequate for the complexity without output schema.

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 covers 100% of parameters. The description adds extra semantics for the entities parameter (first entry is the subject, rest are competitors) and explains the purpose of models and _apiKey, adding value beyond the schema.

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, uses ai_visibility_check, ranks scores, and outputs a ranked list. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities.

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 gives a clear use case (competitive AI-marketing audits) and implies that for single entity probes one should use ai_visibility_check. It does not explicitly state when not to use, but the context is sufficient.

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

B3.4/5.0
Disambiguation3/5

While most tools have detailed descriptions, the large number of similar data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, etc.) and overlapping domains (Polymarket, company research, medical) create ambiguity for an agent.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check, ask_pipeworx), camelCase absent, some with 'pipeworx' prefix, others not (bet_research, compare_entities). No consistent verb_noun pattern.

Tool Count1/5

33 tools for a server named 'Medical Codes' is excessive and misaligned. The vast majority of tools cover unrelated domains (finance, prediction markets, general research), making the count inappropriate for the stated purpose.

Completeness1/5

For medical coding, only three tools exist (search_icd10, search_loinc, search_medical_terms). The rest are tangential or unrelated, leaving severe gaps in medical code coverage.

Resources