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Lead Intelligence

by com.timzinin.api

Lead List Cleaner - Dedupe, DNS & Email Hygiene

lead-list-cleaner
Read-only

Normalize mixed lead rows, deduplicate domains and emails, check DNS/MX evidence, score retained leads, and return every rejection, confidence gap, safe action, and run summary for auditable CRM staging. — $0.02/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoRaw lead rows to clean directly, one object per lead — any shape works (Google Maps scraper export, CSV turned into JSON, anything with an email/website/phone somewhere in it). Takes priority over 'datasetId' and 'datasetUrl'.
checksNoPipeline stages to run, in order. 'dedupe' collapses rows sharing the same domain/email. 'email' verifies syntax + MX. 'domain' verifies the domain is alive. 'score' computes the hotness score used for the 'minScore' cutoff.
maxRowsNoSafety cap on how many input rows this run will process, regardless of source.
minScoreNoDrop leads scoring below this threshold (0-100) from the clean, billed output. They still show up as free rejected rows with a reason.
datasetIdNoPick another Actor's dataset to clean (e.g. a Google Maps or lead-scraper run). Ignored when 'items' is set. Picking it here — not just pasting the ID — is what grants this run's token READ access to that dataset. When this Actor is added as an Integration on another Actor, this field is filled in automatically from the triggering run.
datasetUrlNoPublic URL returning a JSON array of lead rows. Used only when both 'items' and 'datasetId' are empty.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only and non-destructive. The description adds useful context: per-call pricing, detailed output (rejections, confidence gaps, safe actions, run summary), and emphasis on auditability. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single information-dense sentence plus pricing, front-loading the main actions. It is efficient though slightly dense due to the list of outputs.

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?

For a complex tool with no output schema, the description explains return values and context well. Input mechanics are left to the schema, which is fully covered.

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?

All 6 parameters have full schema descriptions, so the description need not add much. It references the checks (dedupe, email, domain, score) but does not expand on parameter details 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 clearly states what the tool does: normalize, deduplicate domains/emails, check DNS/MX, score leads, and provide a run summary. It distinguishes itself from sibling lead-finder tools by focusing on cleaning/hygiene and CRM staging.

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 provides clear context (auditable CRM staging) and the schema adds integration guidance, but it does not explicitly name alternatives or say when not to use this tool.

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

A3.9/5.0
Disambiguation4/5

Each tool targets a distinct lead-intelligence function: scraping careers pages, finding booking gaps, verifying published emails, identifying hiring managers, cleaning lists, and detecting permits. The two vertical gap finders (clinic and restaurant) are structurally similar but clearly separated by domain in both names and descriptions, avoiding real confusion.

Naming Consistency3/5

Most names follow a lowercase hyphenated descriptive pattern (e.g., 'careers-page-scraper', 'lead-list-cleaner'), but 'pricing_info' breaks the pattern by using an underscore. The naming is readable and mostly consistent, yet it lacks a uniform verb_noun convention across the set.

Tool Count5/5

With 8 tools, the server is well-scoped for a lead-intelligence bundle, covering the lead lifecycle from discovery to enrichment to cleaning. This falls comfortably within the ideal 3-15 range, and no tool feels redundant or out of place.

Completeness4/5

The set covers lead discovery (careers, permits, gap finders), enrichment (domain-to-email, hiring-manager-email), and processing (lead-list-cleaner), forming a coherent workflow. Minor gaps exist—like a general web-search lead finder or a tool to update lead statuses—but they don't break the core pipeline.

Resources