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mcp-knowledge

by io.github.getgapup

vuln_exploitability_forecast

Read-onlyIdempotent

As a CTO, assess the exploitability risk of CVEs using EPSS scores and cloud asset exposure data. Input a CVE ID (e.g., CVE-2021-44228) to receive exploitability likelihood, affected cloud services, and threat intelligence context. Returns structured risk metrics for prioritization. Sources: CVE NVD, OpenCVE, GitHub Advisories. Pass async:true to avoid timeout.

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.
cveIdYes
cloudProviderNo
includeDetailsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cveIdYes
statusYes
sourcesYes
warningsYes
epssScoreNo
lastUpdatedNo
cloudExposureNo
epssPercentileNo

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already specify readOnlyHint, openWorldHint, idempotentHint. The description adds context about return values (exploitability likelihood, affected cloud services, threat intelligence) and async behavior. No contradictions; description enhances transparency beyond 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 four sentences, each adding value: purpose, input, output, sources, async. No redundant text. Front-loaded with key purpose. Efficient and 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?

The tool has 4 params and an output schema (exists but not shown). Description covers main purpose, input, output nature, and async option. It omits cloudProvider and includeDetails, but overall provides sufficient context given the output schema availability. Minor gaps prevent a 5.

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

Parameters2/5

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

Schema description coverage is low (25%). The description adds meaning for cveId (example format) and async (purpose), but fails to explain cloudProvider (enum) and includeDetails (boolean with default). This leaves half the parameters undocumented, requiring compensation that is insufficient.

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 states the tool assesses exploitability risk of CVEs using EPSS scores and cloud asset exposure data, with a specific verb ('assess') and resource ('CVEs'). It distinguishes from siblings like cve_security_lookup by focusing on exploitability and cloud exposure, though not explicitly naming alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives implicit usage guidance (input CVE ID, pass async:true to avoid timeout) and mentions sources. However, it does not explicitly differentiate from sibling tools like cve_security_lookup or vuln_patch_priority_engine, nor does it state when not to use 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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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.

Resources