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Arcgis Princewilliam

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent, and the description adds valuable non-obvious context: the default model (Llama-3.3-70b), BYO key cost implications, return shape, and combined view. This goes well beyond the annotations and schema.

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 tightly written sentences: action, default, return shape, and use cases. Every sentence earns its place with no filler. Front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description explicitly defines the return format. It covers purpose, usage, default behavior, cost, and applicability. Given the tool's moderate complexity and rich parameter descriptions, this is fully self-contained.

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 covers 100% of parameters, setting a baseline of 3. The description adds the specific default model identifier and clarifies the billing nuance for _apiKey, which slightly enriches understanding of models and _apiKey beyond the schema text.

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 identifies the action ('Probe one or more LLMs'), the resource (knowledge about a business/brand/product/topic), and the output (visibility score 0-100). It distinguishes this from sibling tools by focusing on per-model visibility scoring rather than general Q&A or research.

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?

It provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains default vs extended usage (adding Anthropic via _apiKey). It does not name specific alternatives or state when not to use it, but the context is clear.

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

A3.7/5.0
Disambiguation3/5

The set mixes several overlapping clusters: three ask_pipeworx variants (beta is explicitly identical to stable right now), six Polymarket tools with similar opportunity-scanning purposes, and two AI-visibility tools that duplicate each other. However, the descriptions are detailed enough that an agent can usually pick correctly, so the ambiguity is moderate rather than severe.

Naming Consistency2/5

Tool names follow no single convention — some are verb_noun (query_layer, validate_claim), some noun_noun (entity_profile, layer_info), some company-prefixed clusters (pipeworx_*, polymarket_*), and a few standalone verbs (forget, recall). While snake_case is consistent, the absence of a uniform verb_noun pattern across the set makes it unpredictable.

Tool Count2/5

At 34 tools, the server is oversized for its apparent purpose, and the count is even more problematic because most tools belong to a general Pipeworx/data platform while only 3 serve the 'Arcgis Princewilliam' GIS theme. The set feels like two unrelated servers merged, with many tools earning no clear place in a unified product.

Completeness2/5

The GIS side is a read-only stub (search, schema, and query) with no editing or feature-level retrieval, and the broader Pipeworx side has a notable dead end: tools return pipeworx:// citation URIs but no tool is provided to fetch those resources. The result is a surface that is simultaneously over-built in prediction markets and under-built in its namesake domain.

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