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disify

by io.github.pipeworx-io

Validate Email

validate_email
Read-onlyIdempotent

Verify an email address is properly formatted, has valid DNS records, and isn't disposable or an alias. Returns validation status, 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
emailYesThe email address to validate.
_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
aliasYesWhether the email is an alias
emailYesThe email address that was validated
dns_validYesWhether DNS records exist for the domain
disposableYesWhether the email is from a disposable service
whitelistedYesWhether the email domain is whitelisted
format_validYesWhether the email format is valid

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, and idempotent behavior, but the description adds meaningful runtime context beyond those: this relies on Disify, DNS lookups, and disposable/alias checks, and the result is a live signal rather than a static local verdict. It also discloses the quota-sharing behavior of the anonymous tier. No contradiction with 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?

Three short sentences deliver the core behavior, the output, and the key operational caveat. The most important information is front-loaded, 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?

With a complete input schema, an output schema, and strong annotations, the description only needs to fill behavioral and practical gaps. It does so: what is validated, what is returned, and how to handle API key quota. Nothing essential is missing for an agent to call this correctly.

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?

Schema description coverage is 100%, so the baseline is 3. The description's mention of _apiKey and the shared quota essentially replicates what the schema already explains; it does not add new parameter-level semantics beyond that.

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 specific verb ('Verify') and resource ('an email address'), then defines exactly what validation covers: formatting, DNS records, disposable status, and aliasing. It also states the return payload (status, 0-100 confidence score, signals), which clearly distinguishes it from broader tools like check_domain.

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 clear context for when this tool is appropriate by listing the checks it performs. It also gives important operational guidance: pass your own Disify key because the anonymous quota is shared and usually spent. It does not explicitly name alternatives or when-not-to-use conditions, so it stops short of a 5.

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.

Resources