{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-mukul975-cve-mcp-server","slug":"mcp-mukul975-cve-mcp-server","name":"cve-mcp-server","type":"mcp","url":"https://github.com/mukul975/cve-mcp-server","page_url":"https://unfragile.ai/mcp-mukul975-cve-mcp-server","categories":["mcp-servers","deployment-infra","code-review-security"],"tags":["cisa-kev","claude-ai","cve","cybersecurity","devsecops","epss","fastmcp","mcp","mitre-attack","model-context-protocol","nvd","osv","python","security","shodan","threat-intelligence","virustotal","vulnerability-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-mukul975-cve-mcp-server__cap_0","uri":"capability://search.retrieval.cve.database.lookup.with.multi.source.aggregation","name":"cve database lookup with multi-source aggregation","description":"Queries CVE records across NVD, OSV, and GitHub Advisory databases simultaneously, aggregating vulnerability metadata (CVSS scores, descriptions, affected versions, patch status) into unified response objects. Implements parallel API calls with fallback routing when primary sources are unavailable, returning structured vulnerability intelligence with source attribution for audit trails.","intents":["Look up a specific CVE identifier to get comprehensive vulnerability details across multiple authoritative sources","Find all CVEs affecting a particular software package or library version","Retrieve vulnerability metadata including CVSS scores, descriptions, and remediation guidance in a single query"],"best_for":["Security engineers performing rapid vulnerability triage during incident response","DevSecOps teams automating dependency scanning in CI/CD pipelines","Threat intelligence analysts building comprehensive vulnerability profiles"],"limitations":["API rate limits on NVD (120 requests/minute) and OSV may cause throttling during bulk queries of 100+ CVEs","Aggregation latency adds 500-1500ms per query due to parallel source polling","Some older CVEs (pre-2005) have incomplete metadata across sources"],"requires":["Network connectivity to NVD, OSV, and GitHub APIs","No authentication required for public CVE data endpoints","Python 3.9+"],"input_types":["CVE identifier (e.g., CVE-2024-1234)","Package name with optional version constraint","Free-text vulnerability description"],"output_types":["JSON structured vulnerability objects with CVSS vectors, affected versions, patch availability","Markdown formatted vulnerability summaries","Raw API responses from individual sources"],"categories":["search-retrieval","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_1","uri":"capability://data.processing.analysis.epss.severity.scoring.and.percentile.ranking","name":"epss severity scoring and percentile ranking","description":"Integrates FIRST's Exploit Prediction Scoring System (EPSS) API to compute exploit likelihood percentiles for CVEs, translating raw CVSS scores into real-world exploitability predictions. Returns percentile rankings (0-100) indicating the probability a vulnerability will be exploited in the wild, enabling risk-based prioritization of remediation efforts over pure severity metrics.","intents":["Determine which vulnerabilities are most likely to be actively exploited to prioritize patching","Compare exploit likelihood across multiple CVEs to allocate security resources efficiently","Understand the gap between theoretical severity (CVSS) and practical exploitation risk (EPSS)"],"best_for":["Security teams with limited patching capacity needing data-driven prioritization","Vulnerability management platforms integrating risk scoring into SLAs","Threat hunters correlating EPSS scores with observed exploit activity"],"limitations":["EPSS scores update daily; real-time scores may lag actual exploit activity by 24-48 hours","Percentile rankings are relative to all CVEs in the dataset, not absolute exploit probability","Coverage limited to CVEs published after 2010; older vulnerabilities lack EPSS data"],"requires":["API access to FIRST EPSS endpoint (free, no authentication)","Valid CVE identifier to query","Network connectivity to EPSS API"],"input_types":["CVE identifier (e.g., CVE-2024-1234)","Batch list of CVE IDs for comparative scoring"],"output_types":["JSON object with EPSS score (0-1), percentile (0-100), and confidence metrics","Structured ranking data for sorting/filtering vulnerability lists"],"categories":["data-processing-analysis","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_10","uri":"capability://automation.workflow.real.time.vulnerability.feed.subscription.and.alerting","name":"real-time vulnerability feed subscription and alerting","description":"Monitors vulnerability feeds (NVD, CISA KEV, OSV, vendor advisories) for new disclosures matching specified criteria (affected products, severity thresholds, threat actor attribution). Implements filtering and deduplication logic to reduce alert fatigue, delivering structured notifications with context (impact assessment, remediation guidance, affected asset count) to configured channels (email, Slack, webhook).","intents":["Get notified immediately when a new CVE affects our critical systems","Monitor CISA KEV for vulnerabilities with federal patching deadlines","Track vulnerability disclosures for specific threat actors or attack patterns"],"best_for":["Security operations centers (SOCs) monitoring for emerging threats","Incident response teams tracking active exploitation campaigns","Compliance teams ensuring timely awareness of regulatory-relevant vulnerabilities"],"limitations":["Feed latency varies by source; CISA KEV updates daily, NVD updates continuously, vendor advisories may lag","Filtering logic requires careful tuning to balance alert coverage and false positive reduction","Alert fatigue is common if thresholds are too sensitive; requires ongoing refinement","Subscription management and state tracking require persistent storage (database or external service)"],"requires":["Persistent storage for subscription state and alert history (database or external service)","Notification channel configuration (email, Slack webhook, HTTP endpoint)","Vulnerability feed access (NVD, CISA KEV, OSV, vendor advisories)","Python 3.9+"],"input_types":["Filter criteria (affected products, severity thresholds, threat actor names)","Notification channel configuration","Alert frequency preferences"],"output_types":["Structured alert notifications with vulnerability context","Alert history and statistics","Subscription management interface"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_11","uri":"capability://memory.knowledge.threat.actor.and.campaign.attribution.linking","name":"threat actor and campaign attribution linking","description":"Correlates vulnerability data with threat actor profiles, known attack campaigns, and malware families using MITRE ATT&CK, VirusTotal, and threat intelligence feeds. Maps CVEs to specific threat groups (e.g., APT28, Lazarus) known to exploit them, enabling threat-driven vulnerability prioritization and incident correlation. Implements entity linking to connect disparate threat intelligence sources into coherent threat profiles.","intents":["Identify which threat actors are known to exploit a specific CVE","Correlate a breach indicator (malware hash, C2 IP) with known threat groups and their CVE arsenal","Prioritize vulnerabilities based on threat actor sophistication and targeting patterns"],"best_for":["Threat intelligence teams conducting adversary-centric analysis","Incident responders correlating breach indicators with known threat actors","Red team operators researching threat actor TTPs and exploit chains"],"limitations":["Threat actor attribution is often uncertain; multiple groups may use the same exploits or tools","Attribution data is maintained by community researchers and may contain errors or outdated information","Some threat groups are tracked under multiple names (aliases), complicating correlation","Attribution requires cross-referencing multiple sources; no single authoritative source exists"],"requires":["CVE identifier, malware hash, or threat actor name","Access to MITRE ATT&CK, VirusTotal, and threat intelligence feeds","Python 3.9+"],"input_types":["CVE identifier","Threat actor name or APT identifier","Malware hash or family name","Campaign name or incident identifier"],"output_types":["JSON object with threat actor profiles, known exploits, and campaign details","Structured list of CVEs used by a specific threat group","Entity linking graph showing relationships between threats, exploits, and campaigns"],"categories":["memory-knowledge","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_12","uri":"capability://tool.use.integration.mcp.protocol.integration.and.tool.registration","name":"mcp protocol integration and tool registration","description":"Implements the Model Context Protocol (MCP) server specification, exposing all 27 security tools as callable functions with standardized JSON-RPC interfaces. Handles request routing, parameter validation, error handling, and response serialization according to MCP specification. Enables seamless integration with Claude and other MCP-compatible clients through automatic tool discovery and schema advertisement.","intents":["Enable Claude to call security tools directly without manual API integration","Expose security capabilities through a standardized protocol compatible with multiple LLM clients","Manage tool versioning and backward compatibility as security APIs evolve"],"best_for":["Organizations deploying Claude with security automation requirements","MCP-compatible LLM applications needing standardized security tool integration","Security teams building custom AI agents with vulnerability intelligence capabilities"],"limitations":["MCP protocol overhead adds ~50-100ms latency per tool call compared to direct API calls","Tool discovery and schema advertisement require MCP client support; older API clients cannot use MCP tools","Error handling and retry logic must be implemented at the MCP layer; upstream API failures propagate to client","MCP specification is evolving; version compatibility may require updates as protocol matures"],"requires":["MCP-compatible client (Claude, other LLM applications)","Python 3.9+","FastMCP framework (dependency)"],"input_types":["JSON-RPC requests conforming to MCP specification","Tool parameters in JSON format"],"output_types":["JSON-RPC responses with tool results","Tool schema definitions for client discovery","Error responses with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_13","uri":"capability://tool.use.integration.api.credential.management.and.multi.provider.support","name":"api credential management and multi-provider support","description":"Implements secure credential management for 21+ external APIs (NVD, EPSS, CISA KEV, Shodan, VirusTotal, etc.) with support for environment variables, configuration files, and secure credential stores. Handles API key rotation, rate limit tracking, and provider failover logic. Enables seamless switching between API providers (e.g., multiple VirusTotal API keys for rate limit distribution) without code changes.","intents":["Securely manage API credentials for 21+ security data sources without hardcoding","Distribute API quota across multiple keys to avoid rate limiting","Automatically failover to backup API providers if primary source is unavailable"],"best_for":["Enterprise deployments requiring secure credential management and audit trails","Teams managing multiple API keys across different providers","Environments with strict credential rotation and compliance requirements"],"limitations":["Credential management adds complexity; misconfiguration can expose API keys","Rate limit tracking is approximate; actual limits may vary by provider and API endpoint","Failover logic requires manual configuration of backup providers; automatic discovery is not supported","Credential rotation requires manual updates; no automated key rotation is implemented"],"requires":["API keys for desired data sources (NVD, EPSS, CISA KEV, Shodan, VirusTotal, etc.)","Environment variable or configuration file support","Python 3.9+"],"input_types":["API key configuration (environment variables, config files, credential stores)","Provider selection and failover preferences"],"output_types":["Authenticated API requests to configured providers","Rate limit and quota tracking data"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_14","uri":"capability://automation.workflow.error.handling.and.graceful.degradation.with.fallback.routing","name":"error handling and graceful degradation with fallback routing","description":"Implements comprehensive error handling with automatic fallback routing when primary data sources are unavailable. Catches API failures, rate limits, timeouts, and malformed responses, routing requests to alternative providers or returning cached/partial results. Provides detailed error context to clients enabling informed decision-making when data is incomplete or unavailable.","intents":["Continue operating when one data source is temporarily unavailable","Get partial results from available sources rather than complete failure","Understand why data is incomplete or unavailable for audit and troubleshooting"],"best_for":["Production deployments requiring high availability and resilience","Security teams that cannot afford tool downtime during incident response","Environments with unreliable network connectivity or API availability"],"limitations":["Fallback routing adds latency (500-1500ms) as requests are retried across providers","Partial results may be incomplete or inconsistent across sources","Error context is verbose; clients must parse detailed error information","Caching of fallback results may return stale data if primary source remains unavailable"],"requires":["Multiple API providers configured for fallback routing","Optional: caching layer for fallback results","Python 3.9+"],"input_types":["API requests to primary provider"],"output_types":["Results from primary provider if available","Fallback results from alternative providers if primary fails","Detailed error context explaining data availability"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_15","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output.formatting","name":"structured data extraction and schema-based output formatting","description":"Implements schema-based output formatting for all tool responses, ensuring consistent JSON structure across 27 different APIs with varying response formats. Parses and normalizes heterogeneous API responses into unified data models (e.g., all vulnerability records conform to a standard schema regardless of source). Enables reliable downstream processing by Claude and other clients through guaranteed output structure.","intents":["Get consistent JSON output format across all 27 security tools","Parse and normalize data from APIs with different response structures","Enable reliable downstream processing by guaranteeing output schema"],"best_for":["Developers building applications on top of the MCP server","Automation workflows requiring reliable data structure for parsing","Teams integrating security tools into larger platforms"],"limitations":["Schema normalization may lose provider-specific metadata or nuances","Mapping heterogeneous APIs to unified schemas requires manual schema design and maintenance","Schema evolution requires careful versioning to avoid breaking downstream consumers","Some APIs return data that doesn't fit standard schemas; edge cases require special handling"],"requires":["Schema definitions for all supported data types","JSON serialization/deserialization libraries","Python 3.9+"],"input_types":["Raw API responses from 21+ providers"],"output_types":["Normalized JSON objects conforming to unified schema","Structured arrays of records with consistent field names and types"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_16","uri":"capability://automation.workflow.caching.and.response.memoization.for.performance.optimization","name":"caching and response memoization for performance optimization","description":"Implements intelligent caching of API responses with configurable TTLs (time-to-live) based on data volatility. Caches stable data (CVE descriptions, MITRE ATT&CK mappings) with long TTLs (24+ hours), while caching volatile data (EPSS scores, CISA KEV status) with short TTLs (1-4 hours). Reduces API calls and latency for repeated queries, enabling faster response times and lower API quota consumption.","intents":["Speed up repeated queries for the same CVE or vulnerability","Reduce API quota consumption by caching stable data","Improve response latency for common queries"],"best_for":["High-volume deployments with repeated queries for the same vulnerabilities","Teams with limited API quota needing to maximize query efficiency","Interactive tools requiring sub-second response times"],"limitations":["Caching introduces staleness; cached data may be outdated if source updates frequently","Cache invalidation is complex; incorrect TTLs may cause stale data to be served","Cache storage requires persistent storage (Redis, database, or local filesystem)","Cache size grows unbounded without eviction policies; requires memory management"],"requires":["Caching backend (Redis, database, or local filesystem)","Configurable TTL policies for different data types","Python 3.9+"],"input_types":["API requests (cached transparently)"],"output_types":["Cached responses with metadata indicating cache hit/miss","Cache statistics and performance metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_17","uri":"capability://safety.moderation.logging.audit.trails.and.compliance.documentation","name":"logging, audit trails, and compliance documentation","description":"Implements comprehensive logging of all API calls, tool invocations, and results with structured logging format (JSON) suitable for log aggregation and analysis. Maintains audit trails showing who queried what data, when, and what results were returned. Enables compliance documentation for regulatory requirements (HIPAA, SOC 2, PCI-DSS) by providing detailed records of security tool usage and data access.","intents":["Maintain audit trails of all security tool usage for compliance and forensics","Debug tool failures and API errors through detailed logs","Generate compliance reports showing security tool usage and data access patterns"],"best_for":["Regulated organizations requiring audit trails and compliance documentation","Security teams needing forensic records of tool usage during incidents","Teams troubleshooting tool failures and API errors"],"limitations":["Logging adds overhead (~10-50ms per request) and storage requirements","Sensitive data in logs (API keys, query results) requires careful handling and redaction","Log retention policies must balance compliance requirements with storage costs","Log analysis and correlation requires log aggregation infrastructure (ELK, Splunk, etc.)"],"requires":["Logging infrastructure (file-based, syslog, or log aggregation service)","Structured logging library (Python logging module)","Log retention and analysis tools","Python 3.9+"],"input_types":["Tool invocations and API calls (logged automatically)"],"output_types":["Structured JSON logs with request/response data","Audit trail reports showing tool usage patterns","Compliance documentation"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_18","uri":"capability://tool.use.integration.documentation.and.tool.discovery","name":"documentation and tool discovery","description":"Provides auto-generated documentation for all 27 tools including parameter descriptions, example invocations, and expected outputs. Implements tool discovery endpoint that Claude can query to understand available tools and their capabilities. Supports both human-readable documentation (Markdown) and machine-readable schemas (JSON Schema).","intents":["Understand what tools are available and what they do","Learn how to invoke a specific tool with correct parameters","Review example invocations and expected outputs","Discover new tools that might solve a problem"],"best_for":["New users learning the tool ecosystem","Developers integrating cve-mcp-server into custom applications","Teams documenting security automation workflows"],"limitations":["Documentation is auto-generated from code; may be incomplete or outdated","Example invocations are static; may not reflect all use cases","Tool discovery is read-only; no dynamic tool registration","No interactive documentation (e.g., Swagger UI)"],"requires":["Python 3.9+ runtime"],"input_types":["none (documentation is static)"],"output_types":["Markdown (human-readable)","JSON Schema (machine-readable)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_2","uri":"capability://search.retrieval.cisa.known.exploited.vulnerabilities.kev.catalog.search","name":"cisa known exploited vulnerabilities (kev) catalog search","description":"Queries CISA's authoritative KEV catalog of vulnerabilities with confirmed active exploitation in the wild, returning structured records of vulnerability IDs, exploit availability, due dates for federal agency patching, and threat actor attribution when available. Implements real-time filtering against the continuously updated CISA KEV dataset to identify which vulnerabilities in a portfolio have documented exploits.","intents":["Identify which vulnerabilities affecting our systems are actively being exploited in the wild","Check if a CVE has a CISA KEV due date (mandatory patching deadline for federal contractors)","Correlate vulnerability inventory against known-exploited list to flag critical gaps"],"best_for":["Federal contractors and government agencies subject to CISA KEV patching mandates","Enterprise security teams needing to distinguish between theoretical and actively-exploited vulnerabilities","Incident response teams correlating breach indicators with known-exploited CVEs"],"limitations":["KEV catalog contains only vulnerabilities with confirmed active exploitation; absence from KEV does not mean a CVE is safe","Due dates are binding only for federal agencies; private sector use is advisory","Lag of 1-7 days between initial exploitation discovery and CISA KEV publication"],"requires":["Network connectivity to CISA KEV API (free, no authentication)","CVE identifier or vulnerability name to query"],"input_types":["CVE identifier (e.g., CVE-2024-1234)","Batch list of CVE IDs for KEV status checking"],"output_types":["JSON object with KEV status, due date, exploitation details, and threat actor attribution","Boolean flag indicating if CVE is in CISA KEV catalog","Structured list of all KEV entries matching filter criteria"],"categories":["search-retrieval","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_3","uri":"capability://memory.knowledge.mitre.att.ck.framework.mapping.and.tactic.correlation","name":"mitre att&ck framework mapping and tactic correlation","description":"Maps CVEs and vulnerabilities to MITRE ATT&CK tactics, techniques, and sub-techniques, enabling threat modeling by linking exploits to adversary behavior patterns. Queries the MITRE ATT&CK knowledge base to return structured technique IDs, tactic categories (e.g., Initial Access, Privilege Escalation), and associated threat groups known to use specific attack chains, facilitating threat-driven vulnerability prioritization.","intents":["Understand which ATT&CK tactics a vulnerability enables (e.g., does CVE-X allow privilege escalation or lateral movement?)","Map our vulnerability inventory to adversary TTPs to identify which threats we're exposed to","Correlate threat group activity with vulnerabilities they're known to exploit"],"best_for":["Threat intelligence teams building adversary-centric vulnerability profiles","Red team operators using ATT&CK framework for attack simulation planning","Security architects designing defenses aligned with MITRE ATT&CK coverage"],"limitations":["Not all CVEs have documented ATT&CK mappings; coverage is incomplete for older or niche vulnerabilities","Mappings are maintained by community contributors and may lag behind new techniques","Tactic mappings are often one-to-many (single CVE enabling multiple tactics), requiring careful interpretation"],"requires":["Network connectivity to MITRE ATT&CK API or local knowledge base","CVE identifier or vulnerability description to query","Familiarity with ATT&CK framework terminology (tactics, techniques, sub-techniques)"],"input_types":["CVE identifier","Vulnerability description or attack chain narrative","Threat group name or APT identifier"],"output_types":["JSON object with ATT&CK tactic IDs, technique names, and sub-technique details","Structured list of threat groups known to exploit the vulnerability","Markdown formatted threat modeling summary"],"categories":["memory-knowledge","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_4","uri":"capability://search.retrieval.shodan.iot.and.exposed.service.reconnaissance","name":"shodan iot and exposed service reconnaissance","description":"Integrates Shodan API to search for internet-exposed devices, services, and infrastructure matching specified criteria (IP ranges, ports, banners, hostnames). Returns structured results including device metadata (OS, service version, geolocation), vulnerability indicators, and exposure risk scores. Enables reconnaissance of attack surface by identifying which systems running vulnerable software are publicly accessible.","intents":["Find all internet-exposed instances of a vulnerable service (e.g., all exposed Apache 2.4.49 servers) in our IP range","Identify which of our known vulnerabilities are exploitable from the internet vs. internal-only","Discover unexpected exposed services or legacy systems that shouldn't be internet-facing"],"best_for":["Penetration testers and red teamers mapping external attack surface","Security teams conducting asset inventory and exposure validation","Incident responders correlating breach indicators with Shodan scan data"],"limitations":["Requires paid Shodan API key (free tier has limited query credits); bulk reconnaissance can exhaust credits quickly","Shodan index lags real-time; newly exposed services may not appear for 24-48 hours","Results are limited to services with identifiable banners; services with hidden or obfuscated banners may be missed","Geolocation data is approximate and may be inaccurate for VPN/proxy-routed traffic"],"requires":["Shodan API key (paid subscription required for most queries)","Python 3.9+","Network connectivity to Shodan API"],"input_types":["IP address or CIDR range","Port number or port range","Service banner or version string","Hostname or domain","Shodan query syntax (advanced filtering)"],"output_types":["JSON object with device metadata, service versions, geolocation, and vulnerability indicators","Structured list of exposed hosts matching criteria","Risk scoring data for exposure prioritization"],"categories":["search-retrieval","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_5","uri":"capability://search.retrieval.virustotal.malware.and.threat.intelligence.correlation","name":"virustotal malware and threat intelligence correlation","description":"Queries VirusTotal API to check files, URLs, and IP addresses against 90+ antivirus engines and threat intelligence sources, returning detection ratios, malware family classifications, and behavioral analysis results. Enables correlation of vulnerability exploits with known malware payloads, identifying which CVEs are actively weaponized and distributed in the wild.","intents":["Check if a suspicious file or URL is flagged as malicious by multiple antivirus engines","Correlate a CVE with known malware families that exploit it (e.g., is CVE-X used by Emotet?)","Analyze IP addresses for malware C2 infrastructure or known threat actor activity"],"best_for":["Incident responders analyzing malware samples and correlating with CVE exploits","Threat intelligence teams tracking weaponization of vulnerabilities","Security teams validating if suspicious files are related to known threats"],"limitations":["Requires VirusTotal API key (free tier limited to 4 requests/minute; paid tier required for bulk analysis)","Detection ratios vary widely across antivirus engines; no single engine is authoritative","False positives are common; high detection ratio does not guarantee maliciousness","VirusTotal data is community-sourced and may contain misclassifications"],"requires":["VirusTotal API key (free or paid)","File hash (MD5, SHA-1, SHA-256), URL, or IP address to query","Python 3.9+"],"input_types":["File hash (MD5, SHA-1, SHA-256)","URL","IP address","Domain name"],"output_types":["JSON object with detection ratio, malware family classifications, and behavioral analysis","Structured list of antivirus engines flagging the sample","Threat intelligence metadata including known C2 associations"],"categories":["search-retrieval","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_6","uri":"capability://search.retrieval.nvd.vulnerability.database.full.text.search.and.filtering","name":"nvd vulnerability database full-text search and filtering","description":"Implements direct querying of the National Vulnerability Database (NVD) with support for complex filtering by CVE ID, CWE (Common Weakness Enumeration), CVSS score ranges, publication date ranges, and affected product/vendor combinations. Returns paginated results with complete vulnerability records including descriptions, references, and configuration data, enabling comprehensive vulnerability research and portfolio analysis.","intents":["Search NVD for all vulnerabilities affecting a specific product and version combination","Find vulnerabilities in a particular weakness category (e.g., all SQL injection CVEs published in 2024)","Filter vulnerabilities by CVSS score range to identify high-severity issues in our environment"],"best_for":["Vulnerability researchers conducting comprehensive threat landscape analysis","Security teams performing dependency audits and supply chain risk assessment","Compliance teams documenting vulnerability discovery and remediation timelines"],"limitations":["NVD API rate limits (120 requests/minute) may throttle bulk queries","NVD data lags vendor disclosures by 24-72 hours; zero-day vulnerabilities may not appear immediately","Some vendor-specific vulnerability details are incomplete in NVD; vendor advisories may be more authoritative","CWE mappings are inconsistent across CVEs; filtering by CWE may miss related vulnerabilities with different classifications"],"requires":["Network connectivity to NVD API (free, no authentication)","Python 3.9+"],"input_types":["CVE ID or ID range","CWE identifier","CVSS score range (e.g., 7.0-10.0)","Publication date range","Product/vendor name with optional version"],"output_types":["JSON array of vulnerability records with full metadata","Paginated results for large result sets","Structured filtering and sorting options"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_7","uri":"capability://search.retrieval.osv.open.source.vulnerabilities.ecosystem.specific.lookup","name":"osv (open source vulnerabilities) ecosystem-specific lookup","description":"Queries the OSV database (maintained by Google and the Linux Foundation) for vulnerabilities in open-source packages across multiple ecosystems (npm, PyPI, Maven, Cargo, Go, Pub, NuGet, RubyGems). Returns ecosystem-specific metadata including affected version ranges, patch availability, and ecosystem-native advisory links. Enables rapid identification of vulnerable dependencies in software supply chains.","intents":["Check if a specific open-source package version has known vulnerabilities","Find all vulnerable versions of a dependency to determine safe upgrade targets","Correlate dependency inventory against OSV to identify supply chain risks"],"best_for":["Developers and DevSecOps teams scanning dependencies in CI/CD pipelines","Software composition analysis (SCA) tools integrating vulnerability data","Open-source maintainers tracking vulnerabilities in their projects"],"limitations":["OSV coverage is strongest for popular ecosystems (npm, PyPI); coverage is sparse for niche ecosystems","Vulnerability data is community-sourced; some entries may be incomplete or inaccurate","OSV does not provide CVSS scores for all vulnerabilities; severity assessment requires cross-referencing with NVD","Update frequency varies by ecosystem; some advisories lag vendor disclosures by weeks"],"requires":["Network connectivity to OSV API (free, no authentication)","Package name and version in ecosystem-native format (e.g., npm package name)","Python 3.9+"],"input_types":["Package name (npm, PyPI, Maven, Cargo, Go, Pub, NuGet, RubyGems)","Package version or version range","Ecosystem identifier"],"output_types":["JSON object with affected version ranges, patch availability, and advisory links","Structured list of vulnerabilities affecting a package","Ecosystem-native remediation guidance"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_8","uri":"capability://planning.reasoning.vulnerability.impact.assessment.and.remediation.guidance","name":"vulnerability impact assessment and remediation guidance","description":"Synthesizes vulnerability data from multiple sources (CVE, CVSS, EPSS, CISA KEV, MITRE ATT&CK) to generate structured impact assessments and remediation recommendations. Analyzes vulnerability characteristics (exploitability, affected systems, patch availability, workarounds) to produce prioritized remediation guidance tailored to organizational context (industry, asset inventory, compliance requirements).","intents":["Get a comprehensive impact assessment for a vulnerability affecting our environment","Determine the best remediation strategy (patch, workaround, compensating control) for a specific CVE","Prioritize remediation efforts across multiple vulnerabilities based on risk and feasibility"],"best_for":["Security teams making remediation prioritization decisions under resource constraints","Incident commanders assessing impact during active exploitation scenarios","Compliance teams documenting vulnerability assessment and remediation timelines"],"limitations":["Remediation guidance is generic; organization-specific constraints (legacy systems, business criticality) require manual adjustment","Patch availability and workaround effectiveness vary by product; guidance may not apply to all affected systems","Impact assessment relies on accurate asset inventory; missing or misclassified assets will produce inaccurate results","Synthesizing data from multiple sources adds 1-2 seconds latency per assessment"],"requires":["CVE identifier and affected product/version information","Optional: organizational context (industry, asset inventory, compliance requirements)","Access to all upstream data sources (NVD, EPSS, CISA KEV, MITRE ATT&CK)"],"input_types":["CVE identifier","Affected product and version","Organizational context (optional)"],"output_types":["Structured impact assessment with severity, exploitability, and business risk ratings","Prioritized remediation recommendations with feasibility assessment","Markdown formatted executive summary"],"categories":["planning-reasoning","threat-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mukul975-cve-mcp-server__cap_9","uri":"capability://data.processing.analysis.batch.vulnerability.portfolio.analysis.and.reporting","name":"batch vulnerability portfolio analysis and reporting","description":"Processes lists of CVE IDs, package names, or IP addresses in bulk to generate comprehensive vulnerability portfolio reports. Implements parallel processing of multiple queries with aggregation of results into summary statistics (total vulnerabilities, severity distribution, exploitability trends, remediation recommendations). Outputs structured reports suitable for executive briefings, compliance documentation, or automated SLA tracking.","intents":["Analyze our entire vulnerability inventory to identify trends and gaps","Generate compliance reports showing vulnerability discovery, assessment, and remediation timelines","Compare vulnerability exposure across multiple teams or business units"],"best_for":["Security teams conducting periodic vulnerability portfolio reviews","Compliance teams generating audit reports and SLA documentation","Executive leadership tracking security metrics and trends"],"limitations":["Batch processing of 1000+ items may take 5-10 minutes depending on API rate limits","Report generation requires accurate input data; garbage-in-garbage-out applies to portfolio analysis","Aggregation statistics may mask important outliers or high-risk individual vulnerabilities","Requires sufficient API quota across all data sources; bulk analysis can exhaust rate limits"],"requires":["List of CVE IDs, package names, or IP addresses (CSV, JSON, or plain text)","Access to all upstream data sources with sufficient API quota","Python 3.9+"],"input_types":["CSV or JSON file with CVE IDs, package names, or IP addresses","Plain text list (one item per line)","Structured query parameters for filtering"],"output_types":["JSON structured portfolio report with summary statistics","Markdown formatted executive summary","CSV export of detailed vulnerability records","Charts and graphs (if visualization support is available)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Network connectivity to NVD, OSV, and GitHub APIs","No authentication required for public CVE data endpoints","Python 3.9+","API access to FIRST EPSS endpoint (free, no authentication)","Valid CVE identifier to query","Network connectivity to EPSS API","Persistent storage for subscription state and alert history (database or external service)","Notification channel configuration (email, Slack webhook, HTTP endpoint)","Vulnerability feed access (NVD, CISA KEV, OSV, vendor advisories)","CVE identifier, malware hash, or threat actor name"],"failure_modes":["API rate limits on NVD (120 requests/minute) and OSV may cause throttling during bulk queries of 100+ CVEs","Aggregation latency adds 500-1500ms per query due to parallel source polling","Some older CVEs (pre-2005) have incomplete metadata across sources","EPSS scores update daily; real-time scores may lag actual exploit activity by 24-48 hours","Percentile rankings are relative to all CVEs in the dataset, not absolute exploit probability","Coverage limited to CVEs published after 2010; older vulnerabilities lack EPSS data","Feed latency varies by source; CISA KEV updates daily, NVD updates continuously, vendor advisories may lag","Filtering logic requires careful tuning to balance alert coverage and false positive reduction","Alert fatigue is common if thresholds are too sensitive; requires ongoing refinement","Subscription management and state tracking require persistent storage (database or external service)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.37102034202768297,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-05-02T12:18:04Z"},"community":{"stars":516,"forks":85,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-mukul975-cve-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-mukul975-cve-mcp-server"}},"signature":"53RNxTmaRwutnnvIl6SICYAPK7TNSZbQhOlb28hHmvB8jX+rGMLAGpc7lC3+fa0RiKvPCBVtewMNIGFGeCGmBg==","signedAt":"2026-06-23T06:45:02.437Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-mukul975-cve-mcp-server","artifact":"https://unfragile.ai/mcp-mukul975-cve-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-mukul975-cve-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}