Market and Regulatory Data Feeds — Zinin M2M Hub

Korea G2B Contract Demand Radar

g2b-contract-demand-radar

Convert official PPS contract aggregates into buyer-agency demand priorities. Unofficial, independent Actor; not affiliated with or endorsed by any named source publisher. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	Yes	A complete or current PPS report year. Runtime rejects future years even though the schema leaves room for later releases.
`objective`	Yes	Controls transparent peer-percentile weights for spend, frequency, and average contract ticket.	balanced
`requestId`	Yes	A new stable idempotency key for this exact analysis. Reusing it with different criteria is rejected.
`resultLimit`	Yes	Twenty to forty ranked signals. The Actor never starts source work unless the complete requested batch is affordable.
`maxTotalChargeUsd`	Yes	Must cover Actor start plus the entire requested signal batch. Partial paid batches are not started.
`minimumContractCount`	Yes	Agencies below this customer threshold remain visible but receive a monitor-below-threshold action.
`watchAgencyNameContains`	Yes	Up to ten non-sensitive substrings. A matching agency receives a disclosed five-point watch boost; matching never changes source facts.
`minimumContractAmountKrw`	Yes	Agencies below this aggregate annual-spend threshold remain visible with an explicit threshold result.

TDQS

A3.6/5.0

Behavior4/5

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

Beyond annotations, the description discloses important behavioral details: it is an 'Unofficial, independent Actor; not affiliated with or endorsed by any named source publisher' and states pricing as '$0.05/call, x402 (USDC on base).' This adds meaningful context about cost and trust, complementing the 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 exceptionally concise, leading with the core purpose in one sentence, followed by a brief disclaimer and pricing. No wasted words; it is appropriately front-loaded and scannable.

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?

Although the description is brief, it is supplemented by a rich input schema that describes output as 'one whole paid batch of 20–40 explainable buyer-agency demand signals.' Together they provide adequate context for selecting and invoking the tool, though the description alone lacks usage-alternative guidance.

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% — every parameter has a detailed description with examples, defaults, and constraints. The tool description itself adds no parameter-specific semantics, so the baseline score of 3 applies.

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 clearly specifies the tool's function: 'Convert official PPS contract aggregates into buyer-agency demand priorities.' It names the resource (PPS contract aggregates) and the output (demand priorities), making it distinct from generic tender alerts, though it does not explicitly contrast with sibling tools.

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 provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or conditions. The implied use case for Korean public procurement demand analysis comes from the tool title and schema context, not the description itself.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Market and Regulatory Data Feeds — Zinin M2M Hub tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0

Disambiguation2/5

Many tools have overlapping purposes, with over a dozen real-estate scrapers and half a dozen job boards differentiated only by geography. While descriptions are clear, an agent would struggle to pick the correct tool without prior knowledge of the specific site or region, leading to frequent misselection.

Naming Consistency2/5

Tool names use a mix of lowercase-hyphenated (boss-az, clinical-trials-monitor), underscore (pricing_info), and long descriptive phrases (official-gazette-regulatory-action-router). No consistent verb_noun pattern exists; some start with source domains, others with action nouns. This lack of predictability makes navigation confusing.

Tool Count3/5

36 tools is on the heavy side for a server that could have been more focused. While a 'data hub' can justify many endpoints, the high number of near-identical scrapers (12+ real estate, 6+ job boards) suggests bloat rather than well-scoped functionality. A leaner set with parameterized regional filters would be more appropriate.

Completeness2/5

The claimed domain 'Market and Regulatory Data Feeds' is poorly served: there are no stock/forex/commodity price feeds, few global regulatory sources (only FDA, SEC, EU tenders), and many tools are for job and property listings which are tangential. The set feels like a random aggregation rather than a coherent surface, with obvious gaps for core market and regulatory data.

Resources

Need Help?