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H1b

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds detailed behavioral context: probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns ranked list with score, confidence, signal density. No contradictions.

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 well-structured sentences front-load the core purpose, then provide use case and expected output. Every sentence adds value with no redundancy.

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, the description adequately explains return values (ranked list with score, confidence, signal density per entity) and process. It covers all key aspects but could optionally detail score range or confidence calculation. Still, it is sufficiently complete for agent use.

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% with descriptions for all 4 parameters. The description adds valuable semantics beyond schema: 'First entry treated as the "subject" for narrative; rest are competitors' and explains model options ('workers-ai free default', 'anthropic requires apiKey') and context param purpose ('disambiguates common names').

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 uses specific verbs and resources: 'Compare AI visibility across multiple entities side-by-side', 'probes each entity with ai_visibility_check', 'ranks by score'. It clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (different comparison type).

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 states the use case: 'Useful for competitive AI-marketing audits' and gives an example question. It does not explicitly list when not to use or mention alternatives, but the context is clear. Slightly missing explicit exclusion criteria.

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

A4.1/5.0
Disambiguation3/5

Several overlapping clusters exist: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all handle research questions, polymarket_edges and polymarket_arbitrage both scan for trading opportunities, and discover_tools/suggest_questions serve similar discovery purposes. The long, use-case-specific descriptions help, but an agent could still easily pick the wrong tool among these near-duplicates.

Naming Consistency4/5

Most tools follow a snake_case verb_noun or noun pattern (resolve_entity, validate_claim, list_subscriptions, h1b_salary), which is fairly consistent. However, there are deviations: bare verbs like recall/remember/forget/subscribe, noun-only phrases like entity_profile and recent_changes, and the ask_pipeworx_beta suffix variant break the pattern slightly.

Tool Count2/5

34 tools is well past the 25+ threshold, and the server is named 'H1b' while only 3 of the 34 tools relate to H-1B data. The rest is a sprawling mix of data research, prediction-market analytics, memory, subscriptions, AI visibility checks, and unrelated utilities like generate_llms_txt and scan_dependency — a severe scope mismatch.

Completeness4/5

As a de facto Pipeworx research platform, the surface is nearly complete: open-ended queries, grounded evidence mode, deep multi-source research, entity resolution, comparison, claim validation, subscriptions, memory, and feedback. The H-1B sub-domain covers employer, salary, and top-sponsor lookups, though the mention of green cards is a small mismatch since only LCA data is provided.

Resources