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Company Intelligence Tools — Zinin M2M Hub

Lead List Qualifier

lead-list-qualifier

Scores domains for buying readiness from several of our own signal sources in one call. Identity confidence is reported honestly as guessed when no override is supplied. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainsYesCompany domains to score (e.g. "stripe.com"). Each domain fires 4 parallel checks (tech stack, hiring, funding mentions, contact info), so this is capped at 25 per run.
role_keywordsNoHighlight domains currently hiring for roles matching these words (passed to our company-hiring-radar Actor, e.g. "sales", "marketing"). Leave empty to skip role matching.
company_name_overridesNoBy default this Actor guesses each domain's ATS token / company name from the domain itself (e.g. "stripe.com" -> "stripe") for the hiring and funding checks — this is a best-effort heuristic, not a verified identity, and can be wrong. Use this field to override the guess for specific domains: {"my-startup.io": "mystartupinc"}.

TDQS

A3.6/5.0
Behavior4/5

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

The description adds useful behavioral context beyond annotations: it costs $0.05/call and reports identity confidence honestly as guessed when no override is supplied. However, it does not mention side effects (e.g., parallel checks, rate limits) which are only found in the schema descriptions. Annotations already indicate it's not read-only and not idempotent, so no contradiction.

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?

The description is two sentences plus a price/capability line, tightly packed with the core purpose and key caveat (identity reporting). It is front-loaded with the most important information and contains no filler.

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?

Given the tool's moderate complexity (3 parameters, nested objects) and no output schema, the description provides sufficient context about the tool's cost and identity handling. The rich schema fills in the parameter details. However, the description alone might not fully convey the parallel checks and cap, but that lives in the schema, so overall it's complete.

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 itself does not explain any parameters; it relies on the schema's detailed descriptions for domains, role_keywords, and company_name_overrides. No additional semantic meaning is added by the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Scores') and resource ('domains') for a clear outcome ('buying readiness'), and notes it draws from 'several of our own signal sources in one call.' This makes the core purpose clear, though it doesn't explicitly distinguish from sibling tools like intent-signal-aggregator or company-lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for consolidated lead qualification but provides no explicit guidance on when to use it versus alternatives. No when-to-use/when-not-to-use conditions or alternative tool names are given. The schema references company-hiring-radar but the description itself lacks comparative guidance.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists between company-lookup and company-registry-enricher, and between individual signal tools and composite rollups. The descriptive names help differentiate, but the boundary between one-off screenings and alert/rollup tools requires careful reading.

Naming Consistency4/5

Tool names follow a consistent pattern of hyphenated lowercase nouns (e.g., company-lookup, funding-alert, sanctions-screening). The one exception, pricing_info, uses an underscore, creating a minor deviation from the otherwise uniform naming style.

Tool Count4/5

With 20 tools, the server is on the higher end of typical scope but justified for a comprehensive company intelligence bundle. Each tool covers a distinct or complementary aspect of company research, so the count feels purposeful rather than bloated.

Completeness5/5

The toolset covers company lookup, registry, hiring, funding, sanctions, litigation, patents, contacts, new company detection, and email verification—a broad and well-rounded surface for due diligence and sales intelligence. Composite tools like intent-signal-aggregator and lead-list-qualifier tie these together effectively, leaving no major dead ends.

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