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Attom

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds useful behavioral context: default free model, payment model for Anthropic, and return format with score, confidence, signals, raw_response. 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?

Three concise sentences: first states core function, second adds model and key details, third lists use cases. No filler, front-loaded, and every sentence earns its place.

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?

Given no output schema, the description explicitly describes the return format (per-model and combined view). With 4 parameters (1 required) and no nested objects, the description covers all necessary information including use cases and model options.

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 the baseline is 3. The description adds extra meaning: explains the default model, clarifies that `_apiKey` is only needed for Anthropic, and notes that 'context' helps disambiguate. This 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 provides a specific verb ('Probe'), resource ('LLMs'), and outcome ('score visibility 0-100 per model'), clearly differentiating from sibling tools like 'ask_pipeworx' (Q&A) and 'scan_competitor_ai_presence' (similar but not same). Use cases (AI-marketing audits, pre-launch checks) further clarify 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 explains when to use the tool (audits, brand checks, monitoring) and gives concrete guidance on the default model and optional API key for Anthropic. However, it does not explicitly state when not to use it or compare to specific alternatives among the siblings.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and ask_pipeworx_grounded shares the same router. The universal ask_pipeworx router also subsumes many domain-specific tools (attom_*, entity_profile, etc.), making it unclear when to use the specialist tools versus the catch-all.

Naming Consistency3/5

All names are snake_case and mostly descriptive, but conventions vary: ask_* and attom_* prefixes coexist with bare verbs (remember, forget, subscribe), noun phrases (entity_profile, polymarket_edges), and adjective-prefixed names (recent_alerts, recent_changes). The pattern is readable but not uniform.

Tool Count2/5

39 tools is well over the 25+ threshold for a heavy surface, especially for a server named 'Attom' that also includes memory, subscriptions, feedback, npm scanning, and AI-visibility tools beyond real estate. Many tools could be consolidated (e.g., the three ask_pipeworx variants, the six polymarket tools).

Completeness4/5

The real estate domain is well covered (search, detail, AVM, rental AVM, sales history, trends, assessment, schools), and the broader data platform includes discovery, grounded answers, entity profiles, comparisons, claim validation, subscriptions, and memory. Minor gaps exist only around edge features like OAuth-gated subscription persistence.

Resources