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Glama

Medicare Coverage

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate safe, idempotent, non-destructive. Description adds that it probes each entity with ai_visibility_check, returns ranked list with score, confidence, signal density. No contradictions; provides 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?

Three sentences, front-loaded with main purpose, no redundant words. Efficiently communicates core function, usage scenario, and output.

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 no output schema, description adequately describes return (ranked list with metrics). Parameter descriptions cover all needed info. Could mention entity count (2-8) but schema already does. Slightly light on error handling, but annotations compensate with idempotent hint.

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

Parameters3/5

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

Schema has 100% coverage with clear descriptions for all 4 parameters. Description adds that the first entity is treated as 'subject' for narrative, which is helpful. Otherwise, schema already explains models, _apiKey, context, entities well. Minor additional value.

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?

Describes comparing AI visibility across multiple entities side-by-side, ranking by score, and surfacing most/least recognized. Distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the exact probe method and output.

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?

Explicitly gives a use case: competitive AI-marketing audits with example question. Implies that for single entity, use ai_visibility_check. Provides nuance that first entry is the 'subject' for narrative. Lacks explicit 'when not to use', but 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

A3.5/5.0
Disambiguation2/5

The server mixes Medicare-specific tools with many unrelated general-purpose tools (e.g., bet_research, polymarket_arbitrage, remember), and there are multiple similar ask_pipeworx variants. This makes it difficult for an agent to distinguish which tool is appropriate for a given task without confusion.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use a medicare_ prefix with underscores, others use generic verbs like forget, recall, or compound names like ask_pipeworx, deep_research. There is no uniform verb_noun or noun_verb structure.

Tool Count2/5

57 tools is excessive for a server ostensibly focused on 'Medicare Coverage'. Many tools (e.g., bet_research, polymarket_edge_tracker, scan_dependency) are unrelated to Medicare and should be in separate servers, inflating the count and diluting focus.

Completeness4/5

The Medicare-specific tools cover a broad range: NCDs, LCDs, NCAs, enrollment, DME, Part D, hospital, outpatient, post-acute, and provider data. Minor gaps include Medicare Advantage (Part C) and Medicare Supplement, but the coverage is largely comprehensive.

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