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by OBrandon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Clean an email list (syntax, MX, disposable)

clean_email_list
Read-onlyIdempotent

Validate email lists at DNS level to flag invalid syntax, missing MX records, disposable/role/free domains, typos, and duplicates before outreach.

Instructions

Check a list of email addresses at DNS level: valid syntax, whether the domain has mail (MX) records, disposable/temporary domains, role accounts (info@, sales@), free providers, and typo suggestions (gmial.com -> gmail.com). Each item: email, result (valid / invalid / risky / unknown), reason, score 0-100 and the individual checks. Honest limit: the mailbox itself is NOT checked (no SMTP probe), so a well-formed address on a working domain can still bounce. Duplicates are removed. Cost: $0.20 per 1,000 unique emails on the user's Apify account. Latency: usually 5-30 s.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "clean_email_list"
  }
}

Input Schema

NameRequiredDescriptionDefault
emailsNoEmail addresses to check, up to 1,000 per call.
csv_urlNoOr a public CSV link (e.g. Google Sheets 'publish to web' CSV) with an email column.
max_results_returnedNoHow many result rows to return to the conversation.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare this a safe, idempotent, read-only open-world call, and the description layers on genuinely non-obvious behavior: duplicates are removed, cost is $0.20 per 1,000 unique emails on the user's Apify account, latency is typically 5-30 s, and the mailbox is not SMTP-probed. The honest-limit sentence in particular prevents an agent from over-trusting a "valid" verdict.

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?

Dense but front-loaded: the core check list comes first, then result shape, then the honest limitation, then cost and latency. Every sentence carries operational information, with no filler or restatement of the tool name.

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 an output schema present, the description needn't explain return values, and it still sketches the per-item fields. Annotations cover safety, schema covers inputs, and the description covers the remaining unknowns an agent needs — cost, latency, dedup behavior, and the absence of SMTP verification.

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 coverage is 100%, so the three parameters are already documented, and the description adds only modest meaning — that csv_url needs an email column and that counting is per unique email. It never addresses max_results_returned (default 200 against up to 1,000 inputs), so an agent could submit 1,000 addresses without realizing only a fifth come back by default.

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 and resource ("Check a list of email addresses at DNS level") and enumerates exactly what is verified: syntax, MX records, disposable domains, role accounts, free providers, and typo suggestions. That level of specificity clearly separates it from the unrelated extraction siblings (app_reviews, invoice, pdf_tables) without needing to name them.

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?

It gives clear usage context — pass either an inline list or a CSV link, up to 1,000 per call — and states an important caveat that the mailbox itself is not probed, which tells the agent when a "valid" result can still bounce. It stops short of explicit when-not-to-use guidance or pointing to any alternative tool for SMTP-level verification.

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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