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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering safety and idempotency. The description adds useful context about how it works (combining EPSS with criticality) and data sources (NVD, OpenCVE, AWS pricing), which helps the agent understand the tool's behavior beyond the annotations. No contradictions detected.

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 three sentences: a role-based hook, a concise input/output statement, and a use-case sentence. Each sentence adds unique value, and there is no redundant or filler content. It is front-loaded with the core purpose and efficiently 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 presence of an output schema and strong annotations, the description covers the essential operational aspects: purpose, inputs, outputs, and use case. It could be slightly more complete by mentioning when not to use it or comparing to alternatives, but overall it is sufficient for a tool of moderate complexity.

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 already provides 100% coverage for all four parameters, including descriptions for cveIds, awsServices, maxResults, and async. The description mentions the inputs but does not add significant meaning beyond the schema. The role of awsServices in asset criticality is already implied by the schema description, so baseline 3 is appropriate.

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 function: prioritizing unpatched CVEs by combining EPSS exploitability scores with cloud asset criticality. It specifies the input (CVE IDs and AWS service types) and output (ranked patching order with risk scores and cloud impact), and the unique combination of EPSS with asset criticality distinguishes it from sibling tools like cve_security_lookup or vuln_exploitability_forecast.

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 provides context by stating it is 'Ideal for vulnerability management and cloud security posture improvement,' which indicates when to use it. However, it does not explicitly mention alternatives or exclusion criteria compared to other vulnerability tools, so it falls short of a 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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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.