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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. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context beyond annotations: it names the default model, clarifies that Anthropic calls are BYO-key with direct billing, and describes the per-model return payload. No contradiction with 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-sentence description front-loads the core action, then efficiently covers default behavior, billing, return structure, and use cases. No redundancy; every sentence contributes necessary information.

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 tool with 4 parameters, 100% schema coverage, and no output schema, the description adequately covers purpose, parameters, output shape, and use cases. It does not explain the meaning of 'signals' or 'confidence', but overall it gives an agent enough context to select and invoke the tool correctly.

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% and parameter descriptions are solid. The description adds extra meaning by identifying the specific default model (Workers AI Llama-3.3-70b), framing `_apiKey` as a BYO key with direct billing, and implying that omitting `models` uses the free default. These details are not fully present in the schema.

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?

Description uses specific verb 'probe' and names the resource (LLMs) plus the output (visibility score 0-100 per model). It clearly conveys the tool's function, but does not explicitly distinguish from sibling 'scan_competitor_ai_presence', so it lacks direct sibling differentiation.

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?

Provides clear usage context via use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and describes the default/free option. However, it does not mention alternatives or when-not-to-use scenarios, so it stops short of explicit guidance.

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
Disambiguation4/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim have overlapping functionality in answering factual questions. The detailed descriptions help differentiate them, though some confusion may still arise.

Naming Consistency5/5

All tool names follow a consistent snake_case convention with a verb_noun pattern (e.g., compare_entities, resolve_entity). No mixing of camelCase or other styles, making the naming predictable and uniform.

Tool Count3/5

34 tools is on the higher side, but many are meta-tools (discover, feedback, subscriptions) and some are redundant (ask_pipeworx vs grounded). While the scope is broad, the count could be trimmed for tighter focus.

Completeness4/5

The server covers a wide range of domains (SEC, FDA, FRED, prediction markets, etc.) with strong read and analysis capabilities. Missing update/delete operations and direct trading, but comprehensive for data retrieval and analysis.

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