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Glama

Arcgis Abbotsford

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?

The description discloses that each entity is probed via ai_visibility_check and results are ranked by score, with output including confidence and signal density. This adds behavior beyond the annotations (readOnly, idempotent) by describing the orchestration and return shape. It does not contradict 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: purpose, mechanism, return and use case. No repetition or filler, every sentence contributes.

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 a 100%-coverage schema and annotations indicating a safe, read-only operation, the description still provides essential output context by listing score, confidence, and signal density. It explains the probe-and-rank workflow. It lacks caveats about multiple sub-calls or API-key requirements, but these are covered by the schema.

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 already provides detailed descriptions for all four parameters, including entity semantics, model options, and context. The description only loosely refers to 'your brand + N competitors,' which duplicates the schema's explanation of the first entry as the subject. Therefore, it adds little beyond the schema.

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 identifies a specific action: comparing AI visibility across multiple entities side-by-side. It mentions the sub-tool ai_visibility_check and the ranking behavior, distinguishing it from single-entity probes. The competitive marketing audit use case adds further clarity.

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 states the tool is 'useful for competitive AI-marketing audits' and implies comparison across entities, but it does not explicitly exclude alternatives like ai_visibility_check for single entities. The mention of probing with ai_visibility_check indirectly points to that sibling for one-off checks.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all provide data lookup/research. The PolyMarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also creates boundary confusion despite varied signals.

Naming Consistency3/5

Most tools use snake_case, but the naming pattern is mixed: some are imperative verb phrases (query_layer, validate_claim), while others are noun phrases (entity_profile, recent_alerts, layer_info). Descriptions are readable overall, but there is no consistent verb_noun convention across the set.

Tool Count2/5

34 tools is high and the vast majority are unrelated to the server's stated ArcGIS Abbotsford purpose. The set appears to be a generic Pipeworx data/prediction-market toolkit with only a few GIS-specific tools, making the count excessive for the declared scope.

Completeness2/5

For the ArcGIS Abbotsford domain, the surface is severely incomplete: only search_datasets, query_layer, and layer_info cover GIS functionality, lacking update/delete/create operations or broader dataset management. For the actual Pipeworx domain, coverage is decent, but that does not match the server name.

Resources