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Arcgis Lakecountyil

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context beyond that: the default model is free (Workers AI Llama-3.3-70b), passing _apiKey incurs direct Anthropic costs, and the per-model response structure is outlined. No contradictions found.

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 dense sentences carry all necessary information with no fluff. The first sentence front-loads the core function and output, the second adds use cases and cost caveats. Every phrase earns its place.

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?

For a tool with 4 parameters and no output schema, the description covers all essentials: default model options, key handling, return format, and typical use cases. It gives an agent enough to invoke the tool correctly and interpret results. No significant gaps.

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 description coverage is 100% (all 4 params documented), so baseline is 3. The description adds extra semantic value by clarifying the default model, explaining that _apiKey is a BYO key with direct payment implications, and that context helps disambiguate. These details go beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: probing LLMs for knowledge and scoring visibility (0-100). The verb 'probe' and resource 'LLMs' are specific, and the output format is defined. However, it does not explicitly distinguish itself from the sibling tool 'scan_competitor_ai_presence', which could serve a similar purpose, so it misses the full mark for sibling differentiation.

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 clear use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives context for when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks exclusions.

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
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, validate_claim, and entity_profile all perform data lookups with only subtle differences. discover_tools and suggest_questions also serve similar onboarding/exploration roles.

Naming Consistency4/5

Tool names are uniformly snake_case and generally follow a verb_noun or noun_phrase pattern (e.g., ask_pipeworx, query_layer, entity_profile, remember). There is a slight mix between verb-first and noun-first names but no chaotic conventions like camelCase or inconsistent verb tense.

Tool Count2/5

At 34 tools, the count is well above the typical 15-25 range for a coherent server. More critically, the server is named 'Arcgis Lakecountyil' but only 3 tools (search_datasets, layer_info, query_layer) actually relate to ArcGIS; the remaining 31 tools are a broad Pipeworx data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.

Completeness2/5

For the ArcGIS domain implied by the server name, the surface is barely complete: it offers search, schema inspection, and querying, but no create, update, delete, or management capabilities. Conversely, the Pipeworx side is relatively rich, but that doesn't match the server's stated purpose, leaving the overall set incomplete for its apparent intended use.

Resources