Company Intel

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

A4/5.0

Behavior4/5

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

The description discloses stateful behavior (baseline creation, only returning changes) and cost details ($0.05/call, x402/USDC on base), which go beyond the annotations. The annotations already indicate non-read-only and non-destructive, and the description adds useful context about the tool's operational characteristics.

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 extremely concise—two sentences plus a brief cost note—and every element adds value. It front-loads the core purpose and key behavioral detail while keeping the text minimal and focused.

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 simplicity, the schema richness, and the annotations, the description covers the essential purpose and behavior. The main gap is that it does not describe the output format (since there is no output schema), but the core usage and stateful aspects are sufficiently explained for an agent to select and use the tool.

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 provides 100% coverage with detailed descriptions for all parameters, including the filtering and baseline semantics. The description does not add parameter-specific meaning beyond what the schema already explains, so a 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 uses a specific verb ('Watches') and clearly defines the tool's unique behavior: monitoring funding announcements for a saved filter and returning only changed results since the previous check. This distinguishes it from sibling tools like funding-round-tracker, which likely tracks all announcements rather than just deltas.

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

Usage Guidelines3/5

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

The description implies the intended use case—monitoring for new or changed funding announcements—and explains the first-run baseline behavior. However, it does not explicitly state when to prefer this tool over alternatives such as funding-round-tracker, nor does it provide any exclusion criteria.

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

A3.8/5.0

Disambiguation3/5

Several tools overlap in signal space: hiring-radar, hiring-trend-index, layoff-tracker, and intent-signal-aggregator all touch hiring; funding-alert vs funding-round-tracker, sanctions-screening vs sanctions-update-alert, and rollup tools vs individual checks create boundary ambiguity. However, each has a distinct output format, so descriptions help.

Naming Consistency4/5

Tool names are consistently lowercase with hyphens and descriptive noun phrases (e.g., company-hiring-radar, litigation-check), but pricing_info breaks the pattern with snake_case and a non-descriptive name.

Tool Count3/5

With 20 tools, the server feels heavy and covers a wide range of premium data services, but each tool does target a distinct data source or workflow, so it's borderline rather than excessive.

Completeness4/5

The surface covers company identity, hiring, litigation, sanctions, funding, and patents well, but lacks direct financials, ownership structure, and general news monitoring beyond funding/layoffs, leaving some sales-intelligence gaps.

Resources

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