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Endoflife

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

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive. The description adds that default model is free and Anthropic requires a BYO key paid directly to Anthropic. It also outlines the return structure (per-model fields + combined view), further increasing transparency.

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 (4 sentences) and front-loaded. Every sentence adds critical information: purpose/output, model options, return structure, and use cases. No wasted 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?

Given the tool's complexity (4 params, no output schema), the description adequately covers all parameters, default behavior, optional features, and return format. It is complete enough for an AI agent to understand and use the tool 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%, so baseline is 3. The description adds value by explaining default behavior for models, API key usage (passed straight through), and context purpose for disambiguation, exceeding basic schema info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes LLMs about an entity and scores visibility (0-100). It specifies the default model and optional Anthropic. However, it does not explicitly differentiate from similar sibling tools like 'scan_competitor_ai_presence', leaving some ambiguity.

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 describes use cases (AI-marketing audits, brand checks, competitive monitoring), giving clear context for when to use. It lacks explicit when-not-to-use guidance or alternatives, but the stated use cases are specific enough for effective selection.

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

All Endoflife toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

The tool set mixes three near-identical ask_pipeworx variants, multiple overlapping prediction-market tools (polymarket_edges, polymarket_arbitrage, bet_research), and three endoflife tools buried among 31 unrelated Pipeworx tools. This makes distinguishing between tools genuinely confusing, especially when several appear to route to the same underlying data.

Naming Consistency3/5

Most names use lowercase snake_case, but the verb-noun pattern is inconsistent: some are verb_noun (list_products, get_product), others noun_noun (polymarket_edges, bet_research), and a few are bare verbs (recall, forget). The style is readable but does not follow a single predictable convention.

Tool Count2/5

With 34 tools, the count is far too high for a server named 'Endoflife'—only three tools actually relate to endoflife.date tracking. The remaining 31 tools belong to a separate Pipeworx platform, making the tool count an extreme over-scoping for the apparent purpose.

Completeness4/5

The endoflife subset is complete: list_products, get_product, and get_cycle cover the full lifecycle of discovering and retrieving release/support timelines with no dead ends. The broader Pipeworx toolkit also appears fairly comprehensive for its own domain, but the mixed set makes it hard to assess a single coherent surface.

Resources