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Glama

Arcgis Fairfield

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context by explaining the multi-probe process ('Probes each entity with ai_visibility_check'), ranking behavior, and return format ('ranked list with score, confidence, signal density per entity').

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 three sentences with no fluff: it opens with a clear headline, explains the process and use case, and states the output format. Every sentence contributes 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?

Despite having no output schema, the description explicitly lists return fields ('score, confidence, signal density'). Combined with rich annotations and a comprehensive input schema, it gives a complete picture 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 coverage is 100%, with all parameters having detailed descriptions (e.g., entities being first entry as subject). The tool description adds minimal parameter-level meaning 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 clearly states the tool's function: 'Compare AI visibility across multiple entities side-by-side.' It names the exact resource (AI visibility) and distinguishes from siblings by explicitly mentioning it probes each entity with ai_visibility_check and ranks results, which is distinct from the single-entity sibling tool.

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 a clear use case: 'Useful for competitive AI-marketing audits' with a concrete example query. It implies when to use this tool over a single-entity check but does not explicitly name alternatives or 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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and also multiple Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage). Descriptions try to differentiate but the boundaries are unclear, causing confusion.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ask_pipeworx, query_layer), some use descriptive phrases (entity_profile, recent_changes), and there is no consistent verb_noun pattern. The variety makes it hard to predict tool names.

Tool Count1/5

33 tools is far too many for a server named 'Arcgis Fairfield', as most tools are unrelated to GIS or Fairfield (e.g., npm dependency checks, prediction markets, AI visibility). The tool count severely mismatches the server's purported scope.

Completeness1/5

The server's stated purpose is ArcGIS Fairfield, but only 3 tools (layer_info, query_layer, search_datasets) relate to that domain. Critical GIS operations like updating features or managing services are missing, while the vast majority of tools are for other domains.

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