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Buzzword Density

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint as true, so the safety profile is clear. The description adds context about external API calls, default model (free), BYO key for Anthropic, and return format (score, confidence, signals, raw_response). This is sufficient beyond 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?

The description is concise (two paragraphs) with front-loaded core purpose. Every sentence adds value, no repetition or fluff. Structure is logical: function, then technical details, then use cases.

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?

No output schema, but the description explicitly mentions the return format (per-model {score, confidence, signals, raw_response} + combined view). All input parameters are explained. It covers the main aspects adequately for a moderate-complexity tool.

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 each parameter has a description. The description text adds value by explaining the _apiKey flow, default model choice, and context disambiguation, which goes beyond the schema's parameter descriptions.

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 it probes LLMs for knowledge and scores visibility (0-100) per model. It specifies the verb 'probe' and resource 'LLMs for business/brand/product/topic', and distinguishes from siblings like ask_pipeworx and scan_competitor_ai_presence through unique output and use cases.

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 use optional parameters (e.g., _apiKey for Anthropic). However, it does not explicitly contrast with sibling tools or state when not to use, leaving some ambiguity.

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

Many tools have overlapping purposes, such as multiple ask_pipeworx variants and several prediction market tools. This causes ambiguity for agents trying to select the right tool.

Naming Consistency2/5

Tool names mix verb_noun patterns (ask_pipeworx, forget) with noun phrases (entity_profile) and inconsistent prefixes (pipeworx_, polymarket_). No consistent naming convention.

Tool Count2/5

With 32 tools, the set is excessive for a server named 'Buzzword Density' and includes many redundant or overlapping tools. A more focused set of 10-15 would be more coherent.

Completeness4/5

The tool set covers a wide range of data sources and operations (retrieval, comparison, monitoring, memory), missing only minor lifecycle operations like updating stored data.

Resources