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Ieee Standards

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds context about the free default model and the need for an API key to probe Anthropic, including cost implications. No contradictions 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?

The description is concise (~60 words) and well-structured: it starts with the primary action, then provides details and examples, and ends with use cases. Every sentence adds value without redundancy.

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?

Despite lacking an output schema, the description compensates by summarizing the return structure (per-model score, confidence, signals, raw_response + combined view). All parameters are covered, and the tool's behavior is adequately described for safe invocation.

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%, so parameters are already well-documented. The description adds value by explaining the default model and the optional API key usage, which goes beyond the schema's 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 uses specific verbs ("probe", "score") and clearly identifies the resource (LLM visibility for business/brand/product/topic). It distinguishes this tool from siblings by focusing on AI visibility scoring, which is unique among the listed tools.

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 specifies when to use the default model vs. paying for Anthropic, and outlines use cases like AI-marketing audits and pre-launch checks. However, it does not explicitly state when not to use this tool or name alternative tools for comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Ieee Standards toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions with overlapping routing behavior, while polymarket_edges, polymarket_arbitrage, and bet_research all surface prediction-market opportunities. Long descriptions help, but the boundaries between these clusters are genuinely unclear, and the three IEEE tools are buried in a sea of unrelated Pipeworx tools.

Naming Consistency4/5

The naming is predominantly snake_case with clear family prefixes like ieee_, ask_pipeworx_, and polymarket_, and most tools follow a readable verb_noun or noun_verb shape. Minor deviations exist (remember/recall/forget, bet_research, pipeworx_trending) but there is no camelCase/mixed-convention problem, so the overall pattern is predictable.

Tool Count1/5

A server named 'Ieee Standards' exposes 34 tools, yet only three of them (ieee_search, ieee_standard_search, ieee_article) actually serve that domain. The remaining 31 tools form a general Pipeworx data, prediction-market, memory, and subscription platform, which is an extreme scope mismatch for the stated server purpose.

Completeness4/5

For the IEEE lookup domain, the three IEEE tools cover the core read-only workflow: broad corpus search, standards-specific search, and full metadata retrieval by article number or DOI. Minor gaps exist (browsing by committee, revision/status history, full-text access) but those are workable or inherently restricted by IEEE's paywall.

Resources