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Market Data

by com.timzinin.api

Tender Change Alert

tender-alert

Watches public tender feeds for a saved filter and returns only what changed since the previous check — new tenders, closed ones, changed deadlines. The first run on a new filter creates the baseline and says so. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax number of notices gov-tender-radar returns per query, per run. Higher means more thorough coverage but more of OUR compute (never billed to you beyond the per-tender price below).
countryNoISO 3-letter buyer-country code to AND into every query, e.g. "DEU", "FRA". Leave empty for all EU countries.
queriesYesKeywords or TED expert-query expressions defining which tenders to watch — same syntax as gov-tender-radar (e.g. "software", "classification-cpv=72000000", "cloud AND classification-cpv=72*"). Every scheduled run re-checks these same queries and reports ONLY tenders not seen on a previous run for this watch.
max_itemsNoCaps how many NEW-tender rows a single run will deliver and charge for, even if more were found.
baseline_keyNoA name for THIS watch, so you can run several independent tender watches from one Actor (e.g. "eu-it-services", "germany-construction") without one overwriting another's memory of what's already been seen. Each name is scoped to YOUR OWN Apify account — nobody else's watch is visible to you and vice versa. 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

A4.5/5.0
Behavior5/5

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

Even with annotations indicating non-read-only and open world, the description adds valuable context: the first run creates a baseline, only changes are returned, and the pricing ($0.05/call). This explains stateful behavior and cost beyond what annotations or schema provide.

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?

Two sentences plus a cost note. Every sentence earns its place: the first explains core functionality and output, the second addresses first-run behavior, and the cost line adds critical operational info. Information is front-loaded.

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?

The description is complete for a stateful change-monitoring tool. It explains the type of results (new tenders, closed ones, changed deadlines), the baseline creation on first run, and pricing. Although there is no output schema, the description adequately conveys what to expect.

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 has 100% description coverage for all 5 parameters, so the schema carries the full semantic burden. The tool description does not add parameter-specific meaning beyond mentioning 'saved filter' conceptually, which is already covered by the query parameter description.

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 it 'Watches public tender feeds for a saved filter and returns only what changed since the previous check' — a specific verb, resource, and unique focus on deltas. This distinguishes it from sibling search tools like gov-tender-radar by emphasizing change detection rather than initial search.

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 implies the tool is for ongoing monitoring ('returns only what changed since the previous check' and first-run baseline behavior) and is distinct from a one-time search. However, it does not explicitly name alternatives or state when not to use it, so it lacks explicit exclusions.

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

B3.4/5.0
Disambiguation3/5

Many tools are distinct by region/source (e.g., otodom-warsaw vs. imovirtual-lisbon), but there are overlapping categories: multiple real estate, job, SEC, and crypto tools. Watchers/alerts (job-alert, re-new-listing-alert, tender-alert) could be confused with their corresponding search tools, and token tools (live-price-oracle, token-launch-radar, rug-pull-scorer) have similar purposes.

Naming Consistency2/5

Names follow no consistent pattern: some are hyphenated source-location (emlakjet-istanbul), some are generic descriptors (job-alert, pricing_info uses underscore), and some are verbose phrases (official-gazette-regulatory-action-router). There is no consistent verb_noun or noun structure, making it hard to predict what a tool does from its name.

Tool Count2/5

With 36 tools, the server is in the 'too many' range (25+). The broad 'Market Data' theme partially justifies the count, but it feels bloated with many single-country listings and overlapping watchers that could be consolidated.

Completeness3/5

The domain is loosely defined as 'market data,' spanning real estate, jobs, tenders, SEC filings, crypto, and clinical trials. While it covers niche areas well, there are notable gaps: no general stock/ETF quotes, no forex/commodities data, and no cross-country aggregate search. The mix of scrapers and alerts leaves the surface feeling uneven.

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