Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

cve_security_lookup

Read-only

Look up CVE vulnerability data for enterprise security teams, DevSecOps and SOC analysts. Supports two modes: exact CVE ID lookup (e.g. 'CVE-2024-3094') or keyword search by product/vendor (e.g. 'openssl', 'Apache Tomcat'). Cross-references four authoritative keyless sources: NVD NIST (official CVE database, CVSS v3 scores, affected CPEs), CISA KEV (Known Exploited Vulnerabilities catalog — exploit_in_wild flag), EPSS FIRST (exploit probability 0-1), GitHub Security Advisories (ecosystem-specific: npm/pypi/maven). Returns structured vulnerability records with CVSS v3 scores, affected product version ranges, CWE weakness classification, references and exploitation status. Signals engine produces P0/P1/P2 alerts: P0=CVSS>=9 + active exploitation, P1=CVSS>=7 or EPSS>=70%, P2=CWE pattern clusters. Relevant for EU NIS2 and DORA supply chain risk obligations. Optional env: NVD_API_KEY (raises NVD rate-limit 5→50 req/30s), GITHUB_TOKEN (raises GHSA GraphQL rate-limit). Cache TTL 6h. SLA <=25s p95.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoOverride auto-detection: "lookup" for exact CVE ID, "search" for product/vendor keyword.
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.
queryYesCVE ID (e.g. "CVE-2024-3094") or product/vendor keyword (e.g. "openssl", "Apache Tomcat"). Mode is auto-detected from the CVE-YYYY-XXXXX pattern.
max_resultsNoMaximum number of vulnerabilities to return (default 20, max 50).
severity_minNoMinimum CVSS v3 severity to include in results (default: no filter).
published_afterNoISO date YYYY-MM-DD — only include CVEs published after this date. Defaults to 365 days ago for search mode.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
queryYes
statusYes
signalsYes
sourcesYes
quality_scoreYes
vulnerabilitiesYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint, destructiveHint) match the read-only lookup behavior. The description adds significant context: caching (TTL 6h), rate limits with env vars, async mode, SLA, and the three-tier alerting system. No contradictions.

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 well-structured and front-loaded with the core purpose, then covers modes, sources, outputs, env vars, cache, SLA, and regulations. Every sentence adds value without redundancy or unnecessary length.

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?

Given the tool's complexity (multi-source, multi-mode, alerting), the description is comprehensive: it covers input parameters, output structure (P0/P1/P2), caching, rate limits, and even regulatory relevance. Output schema exists but is not provided, so no need to describe returns.

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 beyond the schema: explains auto-detection of mode from query format, describes the async parameter behavior, and clarifies severity_min filtering. This elevates the score to 4.

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 it looks up CVE vulnerability data and supports two modes: exact CVE ID lookup and keyword search by product/vendor. It is distinct from sibling tools by targeting security teams and referencing authoritative sources.

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 explains when to use each mode (exact ID vs keyword) and mentions optional env vars for rate limits, cache TTL, and SLA. However, it does not explicitly state when not to use the tool or suggest direct alternatives among the many siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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