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Arcgis Washingtoncomn

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

A4.3/5.0
Behavior4/5

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

The description reveals the probing mechanism (with ai_visibility_check), ranking by score, and return of ranked list with score, confidence, and signal density. This adds process detail beyond the annotations, which already indicate read-only, open-world, and idempotent behavior.

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 compact sentences, each earning its place: main action, process, use case, and return value. It is front-loaded with 'Compare AI visibility' and avoids unnecessary fluff.

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?

The description explains what the tool does, when to use it, how it works, and what it returns. Given the detailed schema and annotations, this is complete enough for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already thoroughly documents all parameters (including the 'entities' array semantics). The description adds only a light paraphrase ('your brand + N competitors') without new details, so it doesn't exceed the baseline.

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 language: 'Compare AI visibility across multiple entities side-by-side' and explains it probes each entity, ranks by score, and surfaces recognition levels. This clearly distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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 gives a clear use case ('competitive AI-marketing audits') with an example query ('does Claude know about us as well as our competitors?') and implies the alternative of using ai_visibility_check for a single entity. It doesn't explicitly name alternatives or when not to use, so it's not a 5.

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
Disambiguation3/5

The ArcGIS tools (query_layer, layer_info, search_datasets) are clearly distinct, but the Pipeworx family has heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools can all appear as plausible entry points for a similar data lookup task. The memory and subscription tools are well separated, but the router-style tools create real ambiguity.

Naming Consistency4/5

Almost all tools use lowercase snake_case names with a verb-first pattern (ask_pipeworx, query_layer, subscribe, remember) or clear noun descriptors (entity_profile, layer_info, polymarket_edges). A few names are more cryptic (recall, forget, resolve_entity) but they still follow the same style. No mixed camelCase or inconsistent separators.

Tool Count2/5

34 tools is well beyond what the apparent ArcGIS Washington County purpose needs; only three tools actually concern GIS data. The rest are a broad Pipeworx research suite, memory, subscriptions, feedback, and AI-visibility probes. This makes the surface feel like two or three unrelated servers grafted together rather than one scoped package.

Completeness3/5

For the ArcGIS slice, you can discover, inspect, and query layers, but there is no write or create capability, no field-wise editing, no map/feature export, and no feature-level CRUD. The Pipeworx data side is more comprehensive, but the overall server confuses its purpose. The mixed-domain coverage leaves the GIS part only a thin slice of the offered features.

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