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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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by specifying the default model (Workers AI Llama-3.3-70b, free), BYO key for Anthropic, and the return structure (per-model score, confidence, signals, raw_response, combined view). 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?

A single, well-structured paragraph that front-loads the core function, then flows naturally into model defaults, API key usage, and return format. Every sentence adds value without 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?

Despite no output schema, the description explains the return structure (per-model score, confidence, signals, raw_response, combined view) sufficiently. All parameters are documented, and the tool's read-only, idempotent nature is clear. Complete for its complexity.

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 description coverage is 100% with good descriptions for entity, models, _apiKey, and context. The description adds extra meaning beyond schema, e.g., 'Default model is Workers AI Llama-3.3-70b (free)' for models parameter and 'BYO key' for _apiKey, enhancing understanding.

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 brand visibility and scores it (0-100) per model, with specific verb 'Probe' and resource 'LLMs'. It differentiates from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring and model-specific results.

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 lists use cases (AI-marketing audits, brand checks, competitive monitoring) and explains when to use _apiKey for Anthropic. It does not explicitly state when not to use the tool or compare to alternative siblings, but the context is 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

Many tools have overlapping purposes: ask_pipeworx_beta is a literal duplicate of ask_pipeworx, and ai_visibility_check/scans overlap with each other while the many prediction-market and edge tools cover similar ground. The verbose descriptions help somewhat, but an agent would frequently struggle to pick the right tool.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (get_package, list_versions), others noun_verb (ai_visibility_check, bet_research), and several are brand-specific (pipeworx_feedback, pipeworx_trending) with no uniform verb style. The mixed patterns make it hard to predict tool names.

Tool Count1/5

35 tools is far too many for a server named Packagist: only 4-5 tools relate to the PHP/Composer registry while the vast majority concern Pipeworx data lookups, Polymarket analysis, and memory utilities. The scope is severely mismatched with the server's name and apparent purpose.

Completeness2/5

For a Packagist registry server, the core read operations (search, get, list versions, stats) are present, but the server is cluttered with unrelated functionality and offers no package management actions. The tool set is not complete for any single, coherent domain, making it feel like two different servers merged into one.

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