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Company Intelligence Tools — Zinin M2M Hub

Company Hiring Radar

company-hiring-radar
Read-only

Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNoHighlight roles whose title, department or function contains any of these words (e.g. `sales`, `marketing`, `growth`). Matched roles are returned separately in `matchedRoles`.
companiesYesOne entry per company. Best form is `provider:token` — e.g. `greenhouse:stripe`, `lever:spotify`, `ashby:ramp`. The token is the company's slug on its job board (the part in the careers URL). A bare token like `stripe` auto-detects the provider.
newWindowDaysNoA role counts as new if it was first published within this many days.
maxConcurrencyNoHow many companies to check in parallel.

TDQS

A4.5/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false, indicating safe read-only access. The description adds useful context about API usage and cost ($0.01/call, x402 USDC) but does not explain pricing model details, rate limits, or pagination behavior for larger company batches. The description is consistent 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?

The description is a single, dense sentence with no filler. It front-loads the action and resource, then enumerates signals, then sets boundaries. Every clause adds value, and the brevity does not sacrifice clarity.

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?

Given 4 parameters with 100% schema coverage, no output schema, and simple structure, the description is complete. It covers what inputs produce, how results are categorized (matched roles, remote share, new by window), and caveats (no login/scraping needed). An agent can confidently invoke the tool without needing additional context.

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

Parameters4/5

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

Schema coverage is 100% with good descriptions for each parameter. The description goes further by explaining how these parameters combine to produce the signal, e.g., 'newWindowDays' is used to filter 'new' postings. It also clarifies the provider:token format for companies, which is essential for correct invocation. However, there is no description of default behavior when 'keywords' or 'newWindowDays' are omitted beyond schema defaults.

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 uses specific verbs like 'Pull every open role' and clearly identifies the resource as 'a company's public job board from Greenhouse, Lever, Ashby' with concrete signals like 'role count, which functions are growing, remote share, and what's new.' It is clearly distinguishable from siblings like 'layoff-tracker' or 'hiring-trend-index' which cover different hiring-related concepts.

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

Usage Guidelines5/5

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

The description explicitly tells the agent when to use this tool: to turn job postings into a 'buying/expansion signal.' It also states what it does NOT require ('No login, no scraping, no proxies'), which helps agents exclude this tool for scenarios requiring authentication or custom scraping. The emphasis on public job boards further clarifies when the tool is appropriate.

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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