Company Intelligence Tools — Zinin M2M Hub

Funding Round Alert

funding-alert

Watches funding announcements for a saved filter and returns only what changed since the previous check. The first run on a new filter creates the baseline and says so. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max number of filings requested per query, per run.
`queries`	Yes	Company names or sector keywords to watch for new SEC Form D filings (e.g. "artificial intelligence", "biotech", or a specific company name). Every scheduled run re-checks these same queries and reports ONLY filings not seen on a previous run for this watch.
`max_items`	No	Caps how many NEW-filing rows a single run will deliver and charge for, even if more were found.
`sinceDays`	No	Only consider filings from the last N days when checking for matches — keep this generous (well beyond your run schedule) so a filing near the edge of the window is never missed because of clock drift between runs.
`baseline_key`	No	A name for THIS watch, so you can run several independent filing watches from one Actor (e.g. "ai-startups", "biotech-seed") without one overwriting another's memory of what's already been seen. Each name is scoped to YOUR OWN Apify account. The prefilled value is only there so this Actor's own daily test run has a stable, obviously-a-test name; replace it with your own watch name.

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the annotations (readOnly=false, destructive=false), the description discloses key behavioral traits: it is stateful (remembers previous checks), the first run creates a baseline, and it only returns deltas. It also adds cost information. This significantly enhances understanding of side effects and expected behavior.

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: the first states the core function in a compact, front-loaded manner; the second adds essential cost/mechanism details. No wasted words.

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?

The description covers the purpose, statefulness, first-run behavior, and cost. There is no output schema, but the description does not specify the exact structure of returned data. However, given the schema's rich parameter descriptions and the simplicity of the tool, it is mostly 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?

The input schema covers all parameters with detailed descriptions (100% coverage), including baseline_key and sinceDays. The tool description itself does not add parameter-level meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 states a specific verb ('watches') and resource ('funding announcements for a saved filter') and clearly distinguishes from siblings by noting it returns only changes since the previous check. It also mentions the baseline behavior, setting it apart from a plain tracker.

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 clearly implies when to use this tool: for repeated monitoring of saved filters to get only new/changed announcements. It also explains first-run behavior (baseline creation). However, it does not explicitly name alternatives or say when not to use it, though the differentiation from 'funding-round-tracker' is implicit.

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.

Resources

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