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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

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by detailing the return structure (per-model {score, confidence, signals, raw_response} + combined view), pricing (free default model, BYO key for Anthropic), and the authorization note for `_apiKey`. No contradictions.

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 concise (approx. 150 words) and well-structured: it starts with the action and output, then adds parameter details, and ends with usage scenarios. Every sentence adds value 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?

No output schema is provided, but the description adequately explains the return values (per-model fields and combined view). Combined with comprehensive annotations and schema coverage, the description is complete for an agent to understand tool behavior and invocation.

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

Parameters5/5

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

Schema description coverage is 100%. The description enriches parameter meaning beyond the schema: explains default model for `models`, clarifies `_apiKey` is required for Anthropic probes, and provides examples for `entity` and `context` parameters to illustrate usage.

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' and resource 'LLMs', specifying the purpose: to check what AI models know about an entity and score visibility (0-100) per model. It distinguishes itself from sibling tools (e.g., ask_pipeworx, entity_profile) by focusing on AI visibility scoring across multiple models.

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 clear usage context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains the default model and optional API key for Anthropic. However, it does not explicitly state when not to use this tool or mention alternative sibling tools.

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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (the beta is explicitly the same right now); five polymarket_* tools all surface opportunity/edge information; entity_profile, compare_entities, and recent_changes all cover company research. The descriptions are detailed, but an agent can easily misselect between similar tools.

Naming Consistency4/5

All tool names use consistent snake_case and are descriptive, with clear prefix patterns for prediction-market tools (polymarket_*) and the router variants (ask_pipeworx_*). Some names are verb-noun while others are noun-phrases, but the convention is uniformly underscore-separated, with no camelCase or other mixing.

Tool Count2/5

33 tools is excessive for a coherent server, and the scope is sprawled across EMDB access, Pipeworx data routing, prediction markets, memory, subscriptions, and miscellaneous utilities. Even though each tool has a defined role, the sheer breadth and number make it feel like several servers' worth of functionality crammed into one.

Completeness2/5

The server's name suggests it should be focused on EMDB, but only two tools (get_map, search_maps) cover that domain — no browsing, filtering, or extended metadata beyond basic fields. Meanwhile, the bulk of the surface is devoted to unrelated Pipeworx/platform features. For the stated purpose, the coverage is severely incomplete.

Resources