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disify

by io.github.pipeworx-io

Check Domain

check_domain
Read-onlyIdempotent

Check if a domain is associated with disposable or temporary email services. Returns risk assessment, a 0-100 confidence score and the signals behind it. Pass your own Disify key as _apiKey — the keyless anonymous quota is shared and usually spent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesThe domain name to check, e.g. "mailinator.com".
_apiKeyNoOptional but recommended: your own Disify API key (BYO — Pipeworx does not supply one). Without it the call uses the anonymous tier, whose daily quota is shared across all Pipeworx callers and is usually exhausted. Get one at disify.com.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesThe domain that was checked
dns_validYesWhether DNS records exist for the domain
disposableYesWhether the domain is associated with disposable email services
whitelistedYesWhether the domain is whitelisted

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations, the description discloses what the tool returns (risk assessment, confidence score, signals) and warns that the keyless anonymous quota is shared and usually spent. The BYO API key guidance is actionable and non-obvious.

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 short sentences front-load the purpose, then explain outputs, then cover the key requirement. Every sentence adds value and there is no filler.

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?

For a simple two-parameter read-only lookup with an output schema, the description covers the use case, result semantics, and credential requirement. Nothing essential is missing.

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

Parameters3/5

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

Input schema coverage is 100%, so both parameters are already documented in the schema with detailed descriptions. The description reinforces the _apiKey quota warning but adds no new parameter-level semantics beyond that repetition.

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 names a clear verb ('Check'), a specific resource ('a domain'), and the exact purpose (disposable/temporary email services). It also mentions the return values (risk assessment, confidence score, signals), which distinguishes it from domain-adjacent siblings like validate_email.

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 use case is explicit: domain-level disposable email detection. It does not explicitly name alternatives or state when not to use it, but the domain-versus-email framing gives clear context for selection.

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

A3.6/5.0
Disambiguation2/5

Several tools have nearly identical or heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) all involve finding/analyzing prediction-market opportunities. deep_research and ask_pipeworx also overlap as general query routers, and ai_visibility_check vs scan_competitor_ai_presence is another confusable pair.

Naming Consistency3/5

All names use snake_case and are descriptive, but verb placement is inconsistent: some are verb-first (check_domain, compare_entities, resolve_entity), others are verb-last or noun-like (ai_visibility_check, entity_profile, pipeworx_trending, bet_research). There is no chaotic camelCase mix, but the pattern is not predictable enough to guess a tool's behavior from its name.

Tool Count2/5

33 tools is far above the typical well-scoped range and the set spans multiple unrelated domains (data lookup, prediction markets, AI visibility, memory, subscriptions, email/domain validation) that have no cohesive purpose under the 'disify' name. Most tools are not related to domain or email checking, making the count feel like a grab bag rather than a focused toolkit.

Completeness2/5

For a server named 'disify', the core domain-validation surface is minimal (only check_domain and validate_email) and misses obvious operations like WHOIS lookup or breach/debounce checks. As a general data toolset it is broad but shallow in each area, with gaps such as entity_profile only supporting US public companies and no update/delete operations for most data resources.

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