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Openverse

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

A4.5/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: default model is free, Anthropic requires user-provided key with pass-through to api.anthropic.com, returns per-model and combined results, and mentions cost implication for Anthropic calls. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is moderately sized with key action front-loaded. Every sentence is informative, though the second sentence is long and packs multiple details. Remains efficient without unnecessary words.

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?

Covers tool purpose, default/modified behavior, return format (per-model and combined view), and use cases. With no output schema, the description compensates well. Does not mention limits or pagination, but adequate for an agent to use 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% (baseline 3), but description adds value by providing examples for entity, listing supported models, explaining _apiKey's bypassing cost structure, and clarifying context role in disambiguation. This raises the score above baseline.

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 probes LLMs for knowledge about an entity and scores visibility (0-100) per model. It specifies default model, optional Anthropic with API key, and return format. This distinguishes it from siblings like 'ask_pipeworx' which answer questions, as this tool provides a visibility score.

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?

Explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains when to use _apiKey for Anthropic. Does not directly compare to siblings but implies context. Lacks explicit when-not-to-use statements.

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

A3.9/5.0
Disambiguation2/5

Several tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions from Pipeworx data, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market tools also heavily overlap in purpose, as do ai_visibility_check and scan_competitor_ai_presence. Only the Openverse media tools and memory/subscription tools are cleanly distinguishable.

Naming Consistency3/5

All names are lowercase snake_case, but the naming conventions are mixed: verb_noun tools like search_images and resolve_entity coexist with bare verbs like remember, recall, forget, and subscribe, plus noun compounds like entity_profile, polymarket_edges, and bet_research. Subfamilies such as polymarket_* and the audio/image tools are internally consistent, but there is no single predictable pattern across the full set.

Tool Count2/5

37 tools exceeds the 25+ threshold and feels inflated for the surface, especially since several could be consolidated: there are three ask_pipeworx variants and five overlapping prediction-market tools. The Openverse-specific core is only 6 tools, with 31 mostly unrelated Pipeworx and utility tools attached, making the server feel like a grab-bag rather than a purpose-built Openverse integration.

Completeness4/5

Each embedded subdomain covers its main lifecycle well: Openverse has search/get/related for images and audio, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and research has ask, grounded, deep_research, entity_profile, compare_entities, resolve_entity, and validate_claim. Minor gaps exist—notably no Openverse video/collection tooling and no explicit memory update—but agents can work around them without hitting dead ends.

Resources