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Censtatd Hk

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that the tool probes each entity with 'ai_visibility_check', ranks by score, and surfaces most/least recognized. This enriches 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?

The description is four sentences, dense with information, and front-loads the core purpose. Every sentence earns its place without 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 the tool's complexity (ranking multiple entities) and absence of an output schema, the description covers purpose, usage, and return fields (score, confidence, signal density). This is sufficient though slightly abstract about the ranking algorithm.

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% so baseline is 3. The description adds value by explaining that 'entities' array's first entry is treated as the subject and rest as competitors, which is not clear from the schema alone.

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 ('compare', 'probes', 'ranks', 'surfaces') and clearly identifies the resource ('AI visibility across multiple entities'). It distinguishes itself from the sibling 'ai_visibility_check' by emphasizing multi-entity side-by-side comparison and ranking.

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 explicitly states usefulness for 'competitive AI-marketing audits' and gives a concrete example question. It implies single-entity checks should use 'ai_visibility_check', but does not explicitly list when not to use or mention all alternatives.

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

A4/5.0
Disambiguation4/5

Most tools target a distinct action or resource, and the long routing descriptions make choices like ask_pipeworx vs ask_pipeworx_grounded vs deep_research clear. The main weak spots are ask_pipeworx_beta being currently identical to ask_pipeworx and the six overlapping Polymarket tools, but each has a discernible workflow.

Naming Consistency3/5

Naming has internally consistent subfamilies such as censtatd_*, ask_pipeworx*, and polymarket_*, but overall it mixes verb-first names (get_table, validate_claim, subscribe), noun-first names (entity_profile, bet_research, pipeworx_feedback), and bare imperatives (remember, forget, recall). The set is readable but does not follow one convention.

Tool Count2/5

35 tools exceeds the 25+ threshold and feels heavy, especially since many tools (generate_llms_txt, scan_dependency, pipeworx_feedback, pipeworx_trending) are unrelated to the HK Census core implied by the server name. The broad Pipeworx scope explains the width, but the surface is still large for an agent to navigate efficiently.

Completeness4/5

For a read-only research/data-access gateway, coverage is strong: generic lookup, grounded verification, deep research, entity profile/compare/change, entity resolution, memory, and subscription lifecycle are all represented. Minor gaps exist, such as no subscription update, no explicit bulk/export path, and fewer HK C&SD convenience wrappers, but agents can work around them.

Resources