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vulnerability-exposure-mcp

vulnerability-exposure-mcp

by MRFrazer25
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Investigate target

investigate_target
Read-onlyIdempotent

Classifies a public IP, domain, URL, CVE ID, or software version, then runs baseline threat lookups and returns an investigation_id with next steps for vulnerability exposure analysis.

Instructions

Start here. Classifies the target (public IP, domain or URL, CVE ID, or "software version" such as "nginx 1.20.1") and runs the baseline lookups in code: Shodan, VirusTotal, AbuseIPDB, the registry owner and the keyless threat lists for an IP; DNS, VirusTotal, and the same IP checks for a domain's primary IP. Returns an investigation_id to pass to every other tool, the baseline results and next_steps.

depth sets the tool-call budget and detail: "Quick Scan" (8 calls), "Standard Analysis" (15), "Comprehensive Investigation" (25), "Expert Deep Dive" (40). Private, reserved and cloud-metadata IPs are refused.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "target": "<target>"
    },
    "name": "investigate_target"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNoHow thorough to be: sets the tool-call budget and how much detail each result keeps.Standard Analysis
targetYesWhat to investigate: a public IP ('8.8.8.8'), a domain or URL ('example.com'), a CVE ID ('CVE-2021-44228') or a product and version ('nginx 1.20.1', 'OpenSSH 8.9p1 Ubuntu-3ubuntu0.6').

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.1
    • addedInput schema / properties / depth / description
      Added value: +"How thorough to be: sets the tool-call budget and how much detail each result keeps."
    • addedInput schema / properties / target / description
      Added value: +"What to investigate: a public IP ('8.8.8.8'), a domain or URL ('example.com'), a CVE ID ('CVE-2021-44228') or a product and version ('nginx 1.20.1', 'OpenSSH 8.9p1 Ubuntu-3ubuntu0.6')."
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the exact refusal behavior (private, reserved and cloud-metadata IPs), the concrete call budget per depth tier (8/15/25/40), and the return contract (investigation_id, baseline results, next_steps). This is precisely the extra context annotations cannot convey.

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?

The critical instruction ("Start here") is front-loaded and each subsequent sentence carries distinct information. The enumeration of specific providers is somewhat verbose, but it justifies itself by clarifying what the baseline covers.

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?

With no output schema, the description takes on the burden and delivers: it names the returned artifacts and the token needed by downstream tools, and covers refusal cases and budget semantics. Nothing an agent needs to invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline would be 3, but the description adds material meaning: it quantifies depth as a tool-call budget per tier and expands the target examples to product/version strings the schema only hints at. It does not add anything about depth's effect on retained detail beyond the schema's own wording.

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?

States a specific verb (classifies and runs baseline lookups) plus the exact target types accepted, and frames itself as the entry point relative to the sibling tools (resolve_domain, shodan_search, virustotal_check, etc.) that it invokes in code. An agent can place it immediately in the workflow.

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?

"Start here" plus "Returns an investigation_id to pass to every other tool" gives clear sequencing guidance and implicitly tells the agent not to call the individual baseline tools directly. It stops short of explicitly stating when to skip this tool in favor of a targeted sibling call, so it is not a full 5.

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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