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Uses a passthrough architecture that wraps stdin/stdout streams, parsing incoming JSON-RPC messages and applying rule-based filtering or modification before forwarding to the actual MCP server implementation.","intents":["I need to enforce access control policies on which tools an LLM can call through MCP","I want to audit and log all tool invocations before they execute against my backend services","I need to rate-limit or throttle specific tool calls to prevent abuse or resource exhaustion","I want to inject governance rules without modifying my existing MCP server code"],"best_for":["teams deploying LLM agents in regulated environments requiring tool call governance","developers building multi-tenant AI platforms needing per-user tool access control","organizations implementing security policies for AI tool usage without server-side changes"],"limitations":["Stdio-based transport limits throughput compared to HTTP/WebSocket alternatives — suitable for single-connection scenarios only","Governance logic must be defined upfront; no dynamic policy updates without process restart","No built-in persistence for audit logs — requires external logging infrastructure integration","Limited to MCP protocol semantics; cannot enforce governance on non-MCP tool invocations"],"requires":["Node.js 16+ for stdio stream handling","MCP-compatible client and server implementations","JSON-RPC 2.0 message format support"],"input_types":["JSON-RPC 2.0 messages (MCP protocol)","Tool call requests with parameters","Configuration/policy definitions"],"output_types":["JSON-RPC 2.0 responses","Tool execution results","Governance decision logs"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_1","uri":"capability://safety.moderation.tool.call.request.validation.and.schema.enforcement","name":"tool call request validation and schema enforcement","description":"Validates incoming tool call requests against defined schemas before forwarding to the MCP server, checking parameter types, required fields, and constraint violations. Uses JSON Schema or similar validation patterns to ensure tool invocations conform to governance policies, rejecting non-compliant requests with structured error responses that maintain MCP protocol compatibility.","intents":["I want to prevent tools from being called with invalid or malicious parameters","I need to enforce that certain required parameters are always provided for compliance","I want to validate parameter types and ranges before tools execute against production systems"],"best_for":["teams requiring strict input validation for tools that modify critical data","regulated industries needing parameter compliance enforcement","developers protecting backend services from malformed tool invocations"],"limitations":["Validation rules must be pre-defined; no runtime schema discovery from MCP server","Complex nested object validation may require custom rule definitions","Validation latency adds ~5-10ms per request depending on schema complexity"],"requires":["Schema definitions for each tool (JSON Schema format or equivalent)","MCP tool metadata with parameter specifications"],"input_types":["Tool call requests with parameters","Schema definitions"],"output_types":["Validation pass/fail decisions","Error messages with validation failure details"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_2","uri":"capability://safety.moderation.tool.call.access.control.with.role.based.policies","name":"tool call access control with role-based policies","description":"Enforces role-based access control (RBAC) on tool invocations by mapping client identities or contexts to allowed tool sets, blocking unauthorized tool calls before they reach the MCP server. Implements policy matching logic that evaluates tool names, user roles, or other context attributes against a governance ruleset, returning permission-denied responses for unauthorized access attempts.","intents":["I need different users or agents to have access to different subsets of available tools","I want to restrict sensitive tools (like database deletion) to only admin users","I need to enforce tool access based on user roles or organizational context"],"best_for":["multi-tenant AI platforms with per-user tool access restrictions","enterprises deploying LLM agents with role-based security models","teams protecting sensitive tools from unauthorized access"],"limitations":["Requires client identity/context to be passed through MCP protocol — may need custom headers or metadata","No built-in integration with external identity providers; policies must be statically defined or loaded at startup","Cannot enforce row-level or data-level access control — only tool-level granularity"],"requires":["Client identity or role information in request context","Policy definitions mapping roles to allowed tools"],"input_types":["Tool call requests with client context","Role-to-tool mapping policies"],"output_types":["Access granted/denied decisions","Permission error responses"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_3","uri":"capability://automation.workflow.tool.call.rate.limiting.and.quota.enforcement","name":"tool call rate limiting and quota enforcement","description":"Applies rate limiting and quota policies to tool invocations, tracking usage per user, tool, or time window and rejecting requests that exceed defined limits. Uses in-memory counters or sliding window algorithms to enforce quotas, returning rate-limit error responses that maintain MCP protocol compatibility while preventing resource exhaustion or abuse.","intents":["I want to prevent any single user from making too many tool calls and exhausting resources","I need to enforce per-tool rate limits to protect expensive backend operations","I want to implement fair-use policies that limit tool usage per time period"],"best_for":["SaaS platforms offering LLM agent capabilities with usage-based pricing","teams protecting expensive backend services from overuse","developers implementing fair-use policies for shared tool access"],"limitations":["In-memory quota tracking does not persist across process restarts — requires external state store for durability","Distributed deployments need shared quota state; single-process proxy cannot coordinate across instances","Sliding window algorithms add ~2-5ms latency per request for counter updates","No built-in support for quota refunds or adjustments"],"requires":["Rate limit configuration (requests per time window, per user/tool)","Optional: external state store (Redis, database) for distributed quota tracking"],"input_types":["Tool call requests with user/client context","Rate limit configuration"],"output_types":["Rate limit decision (allowed/rejected)","Quota status and remaining limits"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_4","uri":"capability://automation.workflow.tool.call.audit.logging.and.observability","name":"tool call audit logging and observability","description":"Captures detailed audit logs of all tool invocations passing through the proxy, recording request parameters, execution results, governance decisions, and timestamps. Emits structured log events that can be forwarded to external logging systems, providing visibility into tool usage patterns, policy violations, and execution outcomes for compliance and debugging purposes.","intents":["I need to audit all tool calls for compliance and security investigations","I want to track which tools are being used most frequently to optimize my tool set","I need to debug tool execution failures and understand what parameters were used"],"best_for":["regulated industries requiring comprehensive audit trails for tool usage","teams investigating security incidents or policy violations","developers optimizing tool usage and identifying unused tools"],"limitations":["No built-in log persistence — logs must be streamed to external systems (stdout, file, logging service)","Logging adds ~5-10ms latency per request depending on log verbosity and I/O","Sensitive data in parameters may be logged unless explicitly redacted","High-volume tool usage may generate large log volumes requiring external storage"],"requires":["External logging infrastructure (stdout, file, Datadog, CloudWatch, etc.)","Optional: log filtering/redaction rules for sensitive data"],"input_types":["Tool call requests and responses","Governance decisions and policy evaluations"],"output_types":["Structured audit log events (JSON format)","Compliance-ready audit trails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_5","uri":"capability://tool.use.integration.mcp.protocol.message.transformation.and.enrichment","name":"mcp protocol message transformation and enrichment","description":"Transforms or enriches MCP protocol messages as they pass through the proxy, adding metadata, modifying parameters, or injecting context information. Implements message interception hooks that allow policies to rewrite tool call requests (e.g., adding user context to parameters) or responses (e.g., filtering sensitive fields) while maintaining protocol compatibility.","intents":["I want to automatically inject user context or tenant information into tool calls","I need to filter sensitive fields from tool responses before returning to the client","I want to normalize or transform parameters across different tool implementations"],"best_for":["multi-tenant systems needing automatic context injection","teams protecting sensitive data by filtering response fields","developers normalizing tool interfaces across heterogeneous implementations"],"limitations":["Message transformation logic must be pre-defined; no dynamic transformation rules","Complex transformations may break protocol compatibility if not carefully implemented","Transformation latency adds ~3-8ms per request depending on transformation complexity","No built-in schema validation after transformation — requires careful rule design"],"requires":["Transformation rule definitions","Understanding of MCP protocol message structure"],"input_types":["MCP protocol messages (JSON-RPC format)","Transformation rules"],"output_types":["Transformed MCP protocol messages"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-vloex-mcp-proxy__cap_6","uri":"capability://automation.workflow.governance.policy.configuration.and.management","name":"governance policy configuration and management","description":"Provides a configuration interface for defining and managing governance policies (access control, rate limits, validation rules, audit settings) that are applied to tool calls. 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