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A community-built, comprehensive MCP server for the SecurityScorecard API. Not affiliated with SecurityScorecard, Inc.
This server has 8 tools with schemas and descriptions present, but exhibits significant quality gaps. All tools have descriptions, but many are verbose with emoji decoration and lack conciseness. Input schemas are present and mostly well-formed with enums for response_mode parameters. However, parameter descriptions are minimal for several tools (e.g., 'Security finding category to filter by' for category in get_findings_by_category is too generic). No output schemas are documented, the rubric requires documentation of what each tool returns, and none are visible in the source. Error handling is not evident in the provided code. The naming follows verb_noun convention well (get_*, create_*, analyze_*, generate_*), but descriptions are decorated with emoji and marketing language rather than being precise about LLM-actionable intent. The generic 'call_api_endpoint' tool is a code smell, it exposes API complexity rather than abstracting it into domain-specific tools. Response mode parameters are well-designed with explicit token budgets (10-20 for minimal, 200-300 for standard), showing good token awareness, but this is undermined by lack of output schema documentation.
🚨 SECURITY RISKS: Comprehensive security risk analysis with intelligent prioritization. Analyzes critical vulnerabilities, risk patterns, and provides actionable remediation guidance with flexible response modes.
Generic endpoint for calling SecurityScorecard API with custom parameters
🎯 IMPROVEMENT PLAN: Generate security improvement recommendations. INTELLIGENT RESPONSES: Use 'minimal' for simple questions like 'what should I fix first?' (50-100 tokens). Use 'standard' for improvement summary (300-500 tokens). Use 'detailed' for full roadmap.
Generate comprehensive remediation report with actionable recommendations
Get detailed security findings for specific assets
Retrieve comprehensive asset inventory including domains and IP addresses
No output schemas documented for any tool. The rubric requires 'Document the output schema. LLMs need to know what fields to expect so they can plan downstream tool calls and extract the right data.' Without visible return schemas, LLMs cannot reason about chaining tools or extracting required fields.
Generic 'call_api_endpoint' tool exposes API surface complexity. The rubric states 'Each tool should do exactly one thing.' This tool allows arbitrary HTTP methods and endpoints, making it a escape hatch rather than a composable primitive. LLMs cannot reason about side effects (GET vs POST vs DELETE) reliably.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 50 | 2026-07-28+ | v2 |
Retrieve security findings filtered by category with detailed analysis
📊 SECURITY STATUS: Get comprehensive security score, grade, and key metrics with intelligent response modes. Supports minimal responses for quick queries and detailed analysis for comprehensive security overviews.
Parameter descriptions are generic and lack actionable detail. 'Security finding category to filter by' does not enumerate valid categories or explain what categories exist.
Descriptions decorated with emoji (📊, 🚨, 🎯) and marketing language reduce clarity. Descriptions should be concise and actionable for LLMs, not user-facing marketing copy. The rubric baseline is 194 chars; these descriptions are longer and less precise than optimal.
No error handling guidance visible in tool definitions or source code snippets. The rubric requires 'Error responses must tell the LLM what to do next.' No try-catch blocks or error classification patterns are evident in the provided code.
The 'domain' parameter appears in 6 of 8 tools but has minimal description in several. LLMs tend to reuse example values literally.' The default should be empty or null, forcing the LLM to provide a real domain.