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mcp-knowledge

by io.github.getgapup

vuln_patch_priority_engine

Read-onlyIdempotent

As a CTO, quickly prioritize unpatched CVEs by combining exploitability scores (EPSS) with cloud asset criticality. Input a list of CVE IDs and your AWS service types (e.g., EC2, RDS) to receive a ranked patching order with risk scores and estimated cloud impact. Uses public NVD, OpenCVE, and AWS pricing data. Ideal for vulnerability management and cloud security posture improvement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
cveIdsYesList of CVE identifiers to analyze (e.g., ["CVE-2021-44228", "CVE-2023-3824"])
maxResultsNoMaximum number of prioritized CVEs to return (default: 10)
awsServicesNoAWS service types affected by these CVEs (e.g., ["EC2", "RDS", "Lambda"])

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
prioritizedCvesNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark the tool as readOnly, idempotent, and openWorld. The description adds value by detailing data sources (NVD, OpenCVE, AWS pricing) and outputs (ranked order, risk scores, cloud impact), providing behavioral context beyond 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.

Conciseness4/5

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

The description is three sentences, front-loaded with the value proposition. It efficiently covers input, output, and data sources. Minor redundancy (e.g., 'As a CTO' could be omitted) but overall well-structured.

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 has no output schema details shown, the description adequately covers inputs, outputs, and data sources. It explains what the tool returns (ranked patching order) and its ideal use cases, making it sufficiently complete for an AI agent.

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 baseline is 3. The description adds meaning by explaining that cveIds and awsServices are used together for prioritization, and that maxResults controls output count. This contextualizes the parameters beyond their schema descriptions.

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 the tool's purpose: 'prioritize unpatched CVEs by combining exploitability scores (EPSS) with cloud asset criticality.' This specific verb-resource pair differentiates it from sibling tools like cve_security_lookup, which is a basic lookup, and vuln_exploitability_forecast, which focuses on scoring alone.

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 positions the tool as a solution for CTOs needing quick prioritization of unpatched CVEs, ideal for 'vulnerability management and cloud security posture improvement.' While it implies when to use it, it does not explicitly state when not to use it or name alternative tools, but the context is clear enough for an AI agent.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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