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Pixabay

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

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior. The description adds valuable context beyond that: it discloses cost implications ('you pay Anthropic directly'), explains the default model, and specifies the return structure per model. This is more than minimal and builds on the 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 sentences, front-loaded with the core purpose, then model details, then return format and use cases. Every sentence earns its place with no redundancy or filler.

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?

The description covers the return format (per-model fields + combined view) despite no output schema, and communicates the key behavioral nuances (free default, cost-bearing Anthropic option). It's missing deeper interpretation of 'confidence' and 'signals', but for a probe tool with annotations, this is a reasonably complete picture.

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 all parameters are described. The description adds extra semantics by explaining the functional relationship between models and _apiKey (default vs. optional Anthropic trigger), and clarifies the entity scope. This goes beyond the schema's field-level descriptions, earning 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 uses a specific verb ('Probe') and resource ('one or more LLMs') and clearly states the output (visibility score 0-100 per model). It distinguishes itself from siblings like ask_pipeworx by focusing on visibility measurement rather than question-answering, and from scan_competitor_ai_presence by applying to any entity, not 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?

Explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains model selection and API key usage (default free Workers AI, optional Anthropic with BYO key). It doesn't explicitly name alternative tools for when not to use it, but the sibling list and context make the intended scenarios clear.

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

B3.3/5.0
Disambiguation2/5

The tool set contains multiple near-duplicate lookups: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all do the same routing with minor variations, while deep_research and validate_claim also overlap in information retrieval. The two Pixabay search tools are distinct, but the abundance of overlapping data-lookup tools creates real ambiguity.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some are verb_noun (search_images, validate_claim), others are noun-based (entity_profile, pipeworx_feedback), and variants like ask_pipeworx_beta/grounded introduce ad-hoc suffixing. The naming does not follow a single predictable convention.

Tool Count1/5

With 33 tools, the count is far excessive for a Pixabay server. Only 2 tools (search_images, search_videos) actually relate to Pixabay; the remaining 31 are unrelated Pipeworx data, Polymarket, memory, and subscription tools. The scope is a severe mismatch.

Completeness1/5

For a server named Pixabay, the surface is severely incomplete: only basic image/video search is provided, with no tool for fetching details, downloading, managing collections, or any other lifecycle operation. Meanwhile, the Pipeworx domain is over-covered, but that is irrelevant to the server's stated purpose.

Resources