Skip to main content
Glama

Arcgis Loudoun

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

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds behavioral specifics: it uses 'ai_visibility_check' for each probe, ranks by score, identifies most/least recognized, and returns a ranked list with score, confidence, and signal density. No contradiction with 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?

Two concise sentences front-load the core purpose and immediately mention the key differentiator (multi-entity side-by-side comparison). Every clause adds value, from the probe mechanism to the return format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description sufficiently communicates the return structure (ranked list with score, confidence, signal density). It also explains the internal workflow (probe with ai_visibility_check) and the intended narrative (subject vs competitors), rounding out the tool's behavior.

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?

The input schema has 100% description coverage, so the schema already explains all parameters. The description adds no new parameter details beyond referencing 'your brand + N competitors', which aligns with the schema's 'first entry treated as subject' comment.

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 a specific verb ('Compare') and resource ('AI visibility across multiple entities'), immediately distinguishing this tool from the single-entity sibling 'ai_visibility_check'. It clearly states the action (probes, ranks, surfaces) and the input/output (entities, rank list).

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?

Provides concrete use-case guidance ('competitive AI-marketing audits') with an example question. Implies the alternative of using 'ai_visibility_check' for single entities by stating it probes each entity with that tool, but does not explicitly exclude or compare with other sibling tools.

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

All Arcgis Loudoun toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap among data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and numerous Polymarket bet tools, which could cause confusion. Tool descriptions are detailed and help differentiate, but the diversity of domains requires careful reading.

Naming Consistency2/5

Tool names follow inconsistent patterns: some are snake_case verb_noun (query_layer, search_datasets), others are noun_verb (bet_research) or compound names (pipeworx_feedback, polymarket_arbitrage). There is no uniform convention, making it harder for agents to predict tool names.

Tool Count2/5

With 33 tools, the server feels overloaded for its apparent ArcGIS focus. Most tools are unrelated to ArcGIS (Polymarket, Pipeworx data, memory, subscriptions), suggesting a lack of scope. The count is high without a clear unifying purpose.

Completeness2/5

The tool surface is incomplete for any single domain. ArcGIS coverage is minimal (only query and schema), Pipeworx data tools are abundant but without a clear workflow, and Polymarket betting lacks order placement. The server tries to cover too many areas resulting in shallow coverage.

Resources