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Nuget

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. Added

TDQS

A4.2/5.0
Behavior4/5

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

Adds valuable context about orchestration behavior (probing each entity, ranking) beyond the readOnly/idempotent annotations. No contradiction with 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?

Concise at three sentences, front-loaded with main purpose, every sentence adds value without unnecessary 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?

Explains return format (ranked list with score, confidence, signal density) and usage context. Lacks details on potential errors or pagination, but sufficient for the complexity.

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 coverage is 100% and the schema already provides detailed parameter descriptions. The tool description does not add significant new meaning beyond what is in the schema, though it repeats the subject treatment of the first entity.

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 specific verbs like 'Compare', 'Probes', 'ranks', and distinguishes from sibling tool 'ai_visibility_check' by explaining it aggregates results from that tool.

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?

Provides clear context for competitive AI-marketing audits with a concrete example question. However, does not explicitly state when not to use or list alternatives beyond referencing ai_visibility_check.

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
Disambiguation2/5

Several tools are near-identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same function, ask_pipeworx_grounded is the same router with a different response mode, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all overlap on prediction-market edge detection. search vs search_within vs discover_tools also blur discovery boundaries. An agent would struggle to pick the right tool without reading every long description.

Naming Consistency3/5

All names are snake_case and individually readable, so there's no chaotic style mixing. However, the pattern is inconsistent: bare verbs (search, recall, forget, subscribe), verb_noun (get_package, resolve_entity, scan_dependency), noun phrases (latest_version, recent_alerts), and compound prefixes (pipeworx_*, polymarket_*). The server is named 'Nuget' but the vast majority of tools carry pipeworx_ or polymarket_ prefixes, making the namespace feel like a grab-bag.

Tool Count2/5

35 tools is far too many for a server ostensibly named 'Nuget' — only ~5 tools relate to NuGet package lookup (search, get_package, list_versions, latest_version, scan_dependency), and even scan_dependency is npm-only. The remaining ~30 tools belong to an unrelated Pipeworx research/markets/memory platform. The count is inflated by redundant variants (ask_pipeworx trio, six polymarket tools) rather than distinct functionality.

Completeness2/5

Judged by the server's stated purpose (NuGet), the surface is thin and has dead ends: search and version metadata are covered, but there's no package owner/publisher info, no readme/description body fetch, no download stats beyond totals, and scan_dependency targets the wrong ecosystem (npm). Judged by the actual dominant domain (Pipeworx), coverage is excessive and sprawling. The tool set fails to deliver a coherent, complete surface for either apparent purpose.

Resources