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ai_model_mcp_package_supply_chain_radar

AI Model & MCP Package Supply Chain Radar: Tracks newly published AI models, MCP servers, and npm/PyPI package supply-chain signals.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ai_model_mcp_package_supply_chain_radar"
  }
}

Input Schema

NameRequiredDescriptionDefault
maxResultsNoMaximum number of structured items to extract.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden of behavioral disclosure and does not meet it. It does not say how far back 'newly published' reaches, which registries or sources are queried, whether results are cached or live, or what the returned items contain; 'tracks' is the only behavioral verb.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single efficient sentence with the resource scope front-loaded and no filler. The one flaw is that the leading phrase restates the tool name almost verbatim before adding any new information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool the definition is minimally viable, but with no annotations and no output schema the description should describe what a result item looks like and over what time window 'newly published' applies. Both of those gaps matter for an agent deciding whether this tool answers its question.

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?

There is a single optional parameter (maxResults) with 100% schema description coverage, so the schema already explains it. The description adds no meaning about what counts toward the limit or how items are ordered or prioritized, which is the baseline-3 outcome when the schema does the work.

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 states a specific verb and resource set beyond the name: it 'tracks newly published AI models, MCP servers, and npm/PyPI package supply-chain signals,' which tells an agent the data domains covered. It does not name any sibling or explain how it differs from adjacent radar tools (e.g., the tech-launch sentiment or hiring radars), leaving differentiation implicit.

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?

There is no when-to-use guidance, no prerequisites, and no named alternatives or exclusions. The only cue is 'newly published,' which vaguely implies a recency-oriented lookup but gives no threshold or condition for choosing this tool.

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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