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Faa Regulations

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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) set baseline safety. Description adds probes each entity with ai_visibility_check, ranks results, returns 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?

Four sentences, front-loaded with verb and resource. No redundancy, each sentence adds value: purpose, mechanism, example, return fields.

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?

No output schema, but description covers return format (ranked list with score/confidence/signal density). Given complexity (4 params, comparison logic) and annotations covering safety/idempotency, description is complete for agent decision-making. Could mention error handling but not essential.

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%, baseline 3. Description adds that first entity is 'subject' for narrative, models default to workers-ai, and context disambiguates names. This exceeds schema detail, providing actionable usage guidance.

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 the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check probes, ranking, and surfacing most/least recognized. It distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (different comparison) by specifying the competitive audit 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 provides a clear use case: competitive AI-marketing audits. It implies when to use vs ai_visibility_check (comparison vs single) but does not explicitly state exclusions or alternative tools. Sibling list includes ai_visibility_check for inference.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,714-tool catalog with heavily overlapping purposes, and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all target prediction-market analysis and could easily be confused by an agent. The two FAA tools (faa_regulation, faa_search) are distinct, but they are buried among a dozen unrelated data-lookup and memory tools.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun or noun_verb pattern (faa_search, resolve_entity, compare_entities, validate_claim, discover_tools, unsubscribe). Minor deviations exist, such as ask_pipeworx and pipeworx_feedback lacking underscores, and the polymarket_* family mixes noun-led names, but overall the naming is readable and predictable.

Tool Count1/5

33 tools for a server named 'Faa Regulations' is a severe mismatch: only 2 of the 33 tools (faa_regulation, faa_search) relate to FAA regulations, with the rest covering general data lookups, prediction markets, SEC filings, memory storage, npm dependency checking, and llms.txt generation. The count is far too high for the stated domain, and most tools do not belong in this server at all.

Completeness2/5

The actual FAA surface is thin: faa_search provides keyword lookup and faa_regulation returns full text or a part's section list, so basic citation-lookup workflows work, but there is no update/amendment tracking, no browse-by-part navigation beyond a section list, and no related aviation data such as NOTAMs or TFRs. The dominant Pipeworx tool family is unrelated to FAA regulations, so an agent using this server for its apparent purpose would hit dead ends quickly.

Resources