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Uk Gazette

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value by disclosing cost implications (free model vs. BYO Anthropic key), that it returns per-model data including score, confidence, signals, and raw_response, and that it's a read-only probe. 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?

The description is a single, well-structured paragraph. It front-loads the main purpose (probe LLMs, score visibility), then adds usage details (default model, BYO key) and output format. No redundant sentences; every sentence adds value.

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?

Given the moderate complexity (4 parameters, all documented) and no output schema, the description provides sufficient context by explaining the return format (per-model results including score, confidence, signals, raw_response). It covers why you'd use this tool and how the parameters work, making it complete for an AI agent.

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 meaning beyond the schema: clarifies 'entity' examples, specifies supported models ('workers-ai' and 'anthropic'), explains that '_apiKey' is passed directly to Anthropic, and that 'context' helps disambiguate. This provides useful operational guidance beyond parameter names and descriptions.

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 probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies a default model (Workers AI Llama-3.3-70b) and optional Anthropic probing. This purpose is distinct from siblings like 'ask_pipeworx' which focus on specific queries rather than visibility scoring.

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 lists use cases: AI-marketing audits, pre-launch brand checks, and competitive monitoring. It explains when to provide an API key (to also probe Anthropic) and notes the default is free. It does not explicitly state when NOT to use, but the context is clear enough.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all serve similar query routing). Tools from unrelated domains (UK Gazette, Polymarket betting, AI visibility checks) are mixed together, making it hard for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

Naming is chaotic: some tools use descriptive phrases with underscores (gazette_deceased_estates, polymarket_arbitrage), others use generic verbs (remember, recall, forget), and some include version or mode indicators (ask_pipeworx_beta, scan_competitor_ai_presence). No consistent pattern across the set.

Tool Count2/5

With 36 tools, the server is overstuffed for its purported focus on the UK Gazette. The majority of tools (Polymarket, Pipeworx general, AI visibility, etc.) are unrelated to the server's name, making it feel like a bundling of many services into one, which is excessive for a coherent tool set.

Completeness2/5

For a server named 'Uk Gazette', there are only a handful of Gazette-specific tools (gazette_search_notices, gazette_insolvency_notices, etc.), while the rest cover unrelated domains. This leaves obvious gaps for Gazette-related tasks (e.g., no tool for searching particular notice types or filtering by edition), despite the large tool count.

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