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Etherscan

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description discloses that it calls ai_visibility_check for each entity, ranks results, and surfaces most/least recognized. It also explains conditional model usage and API key requirements. This adds significant behavioral context not captured in 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 three sentences, starting with the core action, then mechanics, then use case and return format. Every sentence adds value with no repetition or fluff.

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 multi-entity, multi-model complexity, the description covers key aspects: ranking, fields (score, confidence, signal density), conditional API key, and context usage. It does not mention potential rate limits or pagination, but those are unlikely for a read-only probe. Still, the return format description compensates for missing output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all parameters. The tool description adds extra semantics: first entity is treated as 'subject', models default to 'workers-ai', context applies to all probes. This enhances understanding beyond schema alone.

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 by probing each with ai_visibility_check and ranking by score. It specifies the resource (AI visibility) and action (compare side-by-side), and distinguishes from sibling ai_visibility_check (single entity) by focusing on multiple entities. The use case example reinforces purpose.

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 gives a specific use case ('competitive AI-marketing audits') and implicitly differentiates from single-entity checks via ai_visibility_check. It does not explicitly state when not to use or list alternatives, but the context is clear. The note about first entity as 'subject' provides usage nuance.

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

A3.8/5.0
Disambiguation1/5

Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with only marginal differences, and the Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) heavily overlaps in its goal of finding betting edges. An agent would struggle to pick the right tool without reading every description in detail.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: verb_noun (get_balance, list_transactions), noun phrases (entity_profile, recent_changes), brand prefixes (pipeworx_trending, polymarket_edges), and adjectival forms (deep_research, compare_entities). The naming is readable but does not follow a single predictable convention.

Tool Count2/5

With 36 tools, the count exceeds the 25+ threshold for 'too many' even for a general-purpose data server. The situation is worsened by the fact that the server is named Etherscan but only 5 of the 36 tools relate to Ethereum/blockchain, making the count unjustified for the apparent purpose.

Completeness4/5

For the de facto domain (Pipeworx data routing, prediction-market research, entity profiles, claim validation, subscriptions, memory), the tool surface is quite comprehensive: it covers lookup, research, comparison, grounding, and monitoring. Missing are a few edge operations (e.g., no Etherscan transaction-by-hash tool), but the broader domain is well-covered with only minor gaps.

Resources