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Glama

Arcgis Tallahassee

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

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: default model, optional Anthropic probe with BYO key, and detailed return format (score, confidence, signals, raw_response). No contradictions with annotations (readOnlyHint, idempotentHint, etc.).

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 well-structured: first sentence states purpose and outcome, then details on models and return format. Every sentence adds value without 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 description covers the tool's purpose, parameters, return format, and use cases. Despite lacking an output schema, it fully explains what the agent can expect. No gaps remain.

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%, baseline 3. The description adds value by explaining practical usage: 'Omit for just workers-ai' for models, 'only needed if Anthropic is in models' for _apiKey, and example values for entity. This justifies a 4.

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 verb 'probe', the resource 'LLMs', and the outcome 'score visibility (0-100) per model'. It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on general business/brand visibility rather than just competitors.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to provide the API key. While it doesn't name alternative tools, it gives clear context for usage.

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

All Arcgis Tallahassee toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools are near-duplicates or strongly overlapping: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are only mode/version variants, and discover_tools overlaps with suggest_questions, ai_visibility_check with scan_competitor_ai_presence, and the Polymarket tools with each other. An agent would frequently need a deep read of the descriptions to know which one is truly appropriate.

Naming Consistency4/5

The naming is almost entirely snake_case and mostly follows a verb_noun or domain_noun pattern, e.g. query_layer, list_subscriptions, resolve_entity, compare_entities. Minor deviations like entity_profile, pipeworx_feedback, and polymarket_arbitrage are noun-first, but the overall pattern is still recognizable and predictable.

Tool Count2/5

34 tools for a server branded 'Arcgis Tallahassee' is far too many, especially since only a handful of them are GIS-related. The rest constitute a large general-purpose Pipeworx data platform, which at this tool count becomes unwieldy and will increase an agent's selection failure rate.

Completeness3/5

The core read-only GIS flow is covered (search_datasets → layer_info → query_layer), and the Pipeworx side is broad. However, there are notable gaps: no ArcGIS service management, no layer/feature editing, no spatial operations, and no deeper GIS functions. Because the server's stated purpose is ArcGIS-focused, the overall surface is only partially complete relative to that domain.

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