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Glama

Arcgis Pflugerville

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the read-only/idempotent annotations, the description adds that Anthropic calls require a BYO key and direct payment, and it details the per-model return structure. This is valuable behavioral context regarding cost and external API usage, justifying a 4.

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 tight sentences: action, model/cost specifics, and return format plus use cases. Every sentence earns its place with no fluff or repetition, making it highly concise and well-structured.

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?

For a read-only probe tool with four documented parameters and no output schema, the description covers what it does, how to invoke it, cost implications, return structure, and suitable scenarios. Minor gaps like undefined 'signals' and no rate limits keep it from a 5.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning by naming the default model (Workers AI Llama-3.3-70b) and explaining that `_apiKey` is for optionally probing Anthropic with direct cost implications, pushing it to a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes one or more LLMs and scores visibility (0-100) per model, which is a specific verb+resource+output. It does not explicitly distinguish from sibling tools like scan_competitor_ai_presence, so it falls short of a 5.

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 provides concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and clarifies model selection and API key usage. While it gives clear context, it lacks explicit when-not-to-use or alternative tool references, matching a 4.

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

The tool set contains multiple overlapping tools for data querying (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, suggest_questions) and prediction market analysis (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread), making it difficult for an agent to distinguish which tool to use. The GIS-specific tools are few and could be confused with general data tools.

Naming Consistency2/5

Tool names follow no consistent pattern: some use verb_noun or noun_verb (ask_pipeworx, compare_entities, search_datasets), while others are longer phrases (generate_llms_txt, scan_competitor_ai_presence, polymarket_kalshi_spread). Mixed conventions and lack of uniformity reduce predictability.

Tool Count2/5

With 34 tools, the count is high for a server ostensibly focused on ArcGIS Pflugerville. Many tools are unrelated to GIS (e.g., prediction market tools, general Pipeworx utilities), making the tool surface feel bloated and poorly scoped.

Completeness2/5

For an ArcGIS server, the coverage is minimal: only three tools (search_datasets, layer_info, query_layer) directly support GIS operations. Missing typical GIS capabilities like geocoding, spatial analysis, or editing. The inclusion of many non-GIS tools does not compensate for the lack of depth in the core domain.

Resources