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Glama

Arcgis Loudoun

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds meaningful behavioral context: the default model is free, passing `_apiKey` incurs direct Anthropic costs, and the response is a per-model object plus a combined view. This goes well beyond the annotations and enriches the agent's understanding.

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 compact and well-structured: it front-loads the purpose, then covers model options/cost, return format, and use cases in just three sentences. Every sentence earns its place with no redundancy.

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 tool has no output schema, but the description adequately compensates by detailing the return format. Combined with the full schema explanations and annotations, it covers input, output, model selection, cost, and use cases, making it complete for an agent to invoke 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?

The schema already describes all 4 parameters with 100% coverage. The description reinforces the relationship between `_apiKey` and `models` and mentions the free default model, but it does not add substantive meaning beyond what the schema already provides. Baseline 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: probing LLMs and producing a 0-100 visibility score per model. It also specifies the return format (score, confidence, signals, raw_response) and names concrete use cases, distinguishing it from sibling tools like scan_competitor_ai_presence by emphasizing scoring rather than mere presence scanning.

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 explicit usage scenarios (AI-marketing audits, pre-launch brand checks, competitive monitoring) and clarifies when to pass `_apiKey` to also probe Anthropic. It lacks explicit 'use this instead of X' exclusions, but the context is sufficient for an agent to select the tool appropriately.

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
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.

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