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Arcgis Puyallup

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds that it 'Probes each entity ... with ai_visibility_check, ranks by score, surfaces which is most/least recognized' and returns a ranked list with specific fields. This discloses the internal workflow and output structure, going well beyond the 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?

Three sentences, each with a distinct purpose: the primary action, the mechanism, and the use case. No redundancy. The description is front-loaded with the core purpose and remains compact.

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 the return format (ranked list with score, confidence, signal density), the relationship to the sibling 'ai_visibility_check', and the intended use case. With no output schema, this compensates effectively. The schema covers all parameters with clear descriptions, and annotations cover safety. It is complete for its complexity.

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% with well-documented parameters (e.g., 'Array of 2-8 entities', 'First entry treated as the subject'). The description itself does not add parameter-specific semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

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 states a specific verb ('Compare') and resource ('AI visibility across multiple entities'), and distinguishes itself from the sibling 'ai_visibility_check' by emphasizing side-by-side comparison and ranking. It also clearly states the output (ranked list with score, confidence, signal density).

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 says 'Useful for competitive AI-marketing audits' and gives an example question ('does Claude know about us as well as our competitors?'), which clearly implies when to use it for multi-entity comparison. It does not explicitly contrast with alternatives like 'ai_visibility_check' for single-entity checks, but the context is clear and the sibling list makes the distinction obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Puyallup toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve research questions through the same router, and the five polymarket_* tools plus bet_research form a dense prediction-market cluster. Even with detailed descriptions, an agent choosing between these near-synonyms would frequently need extra reasoning or make the wrong pick.

Naming Consistency2/5

Names mix product-prefixed verbs (ask_pipeworx, polymarket_arbitrage), generic verbs (remember, forget, recall, subscribe), and noun phrases (entity_profile, layer_info, recent_alerts). There is no consistent verb_noun or prefix convention across the set, making the tool surface feel patchwork rather than systematically named.

Tool Count2/5

With 34 tools, the count is already on the heavy side, but it is especially mismatched with the server name 'Arcgis Puyallup': only search_datasets, query_layer, and layer_info actually belong to that GIS domain. The rest are a broad Pipeworx research and prediction-market platform, so the set feels bloated and off-scope for the apparent purpose.

Completeness3/5

The ArcGIS read-only surface is minimally reasonable: search, schema inspection, and querying cover basic open-data consumption. The Pipeworx side is quite rich, with memory, subscriptions, lookups, grounded verification, and discovery, but the named GIS domain is thinly served and lacks obvious capabilities like listing all datasets or browsing layers without a keyword. Overall, coverage is uneven and hard to evaluate cleanly because the server mixes two unrelated purposes.

Resources