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Endoflife

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds that it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized, providing process details beyond annotations. No contradiction.

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 purpose, no wasted words. Efficiently communicates core action and use case.

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?

Even without an output schema, the description specifies the return structure (ranked list with score, confidence, signal density per entity), making it complete for an agent to understand what the tool provides.

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 context that the first entity is treated as the 'subject' for narrative, which adds value beyond the schema.

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 compares AI visibility across multiple entities side-by-side, using specific verbs 'compare' and 'scan'. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic) by focusing on competitive AI-marketing audits.

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 explicitly notes usefulness for competitive AI-marketing audits and gives a concrete use case ('does Claude know about us as well as our competitors?'). It implies not for single-entity checks (use ai_visibility_check) but does not explicitly state exclusions.

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

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