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Uses a middleware pattern that sits between the agent and MCP tools without requiring modifications to tool implementations, enabling retroactive compliance analysis and forensic investigation of agent behavior.","intents":["I need to see exactly what tools my AI agent called and with what parameters for compliance auditing","I want to investigate what happened when an agent made a mistake or took an unexpected action","I need to prove to regulators that all agent tool calls were logged and authorized"],"best_for":["teams deploying AI agents in regulated industries (finance, healthcare, legal)","enterprises requiring SOC 2 or HIPAA compliance for AI systems","developers building multi-tenant AI platforms with audit requirements"],"limitations":["Audit trail storage is not built-in — requires external persistence layer (database, log aggregation service)","Adds latency to tool calls proportional to audit write speed — typically 10-50ms per call depending on storage backend","Does not capture internal LLM reasoning or prompt content, only tool boundaries"],"requires":["Node.js 16+","MCP-compatible agent framework (Claude SDK, LangChain MCP integration, or custom MCP client)","Writable storage backend for audit logs (PostgreSQL, MongoDB, S3, or similar)"],"input_types":["MCP tool call requests (JSON-RPC format with method, params, id)","tool metadata and schema definitions"],"output_types":["structured audit log entries (JSON with timestamp, caller, tool name, parameters, result, duration)","audit trail queryable by time range, tool name, or agent identity"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_1","uri":"capability://safety.moderation.policy.based.tool.call.authorization.and.gating","name":"policy-based tool call authorization and gating","description":"Enforces declarative policies that allow or deny tool invocations based on rules matching agent identity, tool name, parameter values, time windows, or rate limits. Policies are evaluated synchronously before tool execution using a rule engine that supports conditions like 'only allow database writes between 2-4 AM UTC' or 'deny access to sensitive_data_export for agents without admin role'. Integrates with external identity/authorization systems via pluggable adapters.","intents":["I want to prevent my agent from calling certain dangerous tools unless it has explicit authorization","I need to enforce time-based restrictions on when agents can perform sensitive operations","I want to rate-limit tool calls per agent to prevent resource exhaustion or abuse"],"best_for":["teams managing multiple AI agents with different permission levels","organizations enforcing least-privilege access for AI systems","platforms providing AI agent services to external customers with tenant isolation"],"limitations":["Policy evaluation adds 5-20ms latency per tool call depending on rule complexity","No built-in policy versioning or rollback — requires external version control integration","Policy language is custom DSL — requires learning new syntax, not standard REGO or Opa"],"requires":["Node.js 16+","MCP server integration with imara middleware","Policy definitions in imara policy format (JSON or YAML)","Optional: external identity provider (OAuth2, SAML, or custom)"],"input_types":["policy rules (declarative conditions and actions)","agent context (identity, roles, attributes)","tool call metadata (name, parameters, timestamp)"],"output_types":["authorization decision (allow/deny with reason)","policy violation events (for logging and alerting)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_2","uri":"capability://safety.moderation.real.time.policy.violation.detection.and.alerting","name":"real-time policy violation detection and alerting","description":"Monitors tool call streams in real-time to detect policy violations, suspicious patterns (e.g., unusual parameter values, repeated failures, rate limit breaches), and compliance anomalies. Violations trigger configurable alerts (webhooks, email, Slack, PagerDuty) with context about the violation, the agent, and recommended remediation. Uses pattern matching and threshold-based detection to identify deviations from normal behavior.","intents":["I want to be immediately notified when an agent attempts to violate a security policy","I need to detect when an agent is behaving abnormally or potentially compromised","I want to set up automated incident response when certain tool call patterns occur"],"best_for":["security teams monitoring AI agent behavior in production","compliance officers tracking policy violations for audit reports","on-call engineers needing real-time alerts for agent misbehavior"],"limitations":["Alert fatigue risk if thresholds are not tuned carefully — requires baseline profiling of normal agent behavior","No machine learning-based anomaly detection — uses only rule-based and threshold detection","Alert delivery is best-effort — no guaranteed delivery for webhook/email notifications"],"requires":["Node.js 16+","imara policy enforcement layer active","alert destination configuration (webhook URL, email address, Slack token, etc.)","optional: baseline data for threshold tuning"],"input_types":["policy violation events","tool call metrics (rate, duration, error rate)","agent behavior patterns"],"output_types":["alert notifications (JSON payload to webhook, email, Slack message)","violation summary with context (agent, tool, policy, timestamp, parameters)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_3","uri":"capability://data.processing.analysis.compliance.report.generation.and.audit.export","name":"compliance report generation and audit export","description":"Generates structured compliance reports from audit logs covering tool usage, policy violations, authorization decisions, and agent behavior over configurable time windows. Supports multiple export formats (JSON, CSV, PDF) and can filter by agent, tool, policy, or violation type. Reports include summary statistics, violation timelines, and evidence trails suitable for regulatory submission or internal compliance reviews.","intents":["I need to generate a compliance report for regulators showing all agent tool calls over the past quarter","I want to export audit data to analyze which policies are most frequently violated","I need to create evidence documentation for a security incident investigation"],"best_for":["compliance officers preparing audit reports for regulators","security teams documenting incident investigations","data analysts studying agent behavior patterns and policy effectiveness"],"limitations":["Report generation performance degrades with large audit logs (>1M entries) — may require pagination or time-range filtering","PDF export requires external PDF generation library — not included in core package","Reports are static snapshots — no real-time dashboard or interactive exploration"],"requires":["Node.js 16+","populated audit trail from prior tool call interception","access to audit log storage backend","optional: PDF generation library (e.g., puppeteer) for PDF export"],"input_types":["audit log query parameters (time range, agent filter, tool filter, violation type)","report format preference (JSON, CSV, PDF)","grouping/aggregation preferences"],"output_types":["compliance report (JSON, CSV, or PDF format)","summary statistics (total calls, violation count, policy breakdown)","detailed violation timeline with evidence"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_4","uri":"capability://tool.use.integration.agent.identity.and.context.propagation.through.mcp.calls","name":"agent identity and context propagation through mcp calls","description":"Automatically captures and propagates agent identity, user context, and request metadata through the MCP call chain, enriching audit logs and policy decisions with caller information. Supports multiple identity sources (JWT tokens, API keys, OAuth2 bearer tokens) and extracts claims/attributes for use in policy rules. Implements context injection via MCP request headers or metadata fields without requiring agent code changes.","intents":["I want to know which user or agent initiated each tool call for accountability","I need to enforce policies based on the agent's identity, role, or organizational context","I want to correlate tool calls with upstream user requests for end-to-end tracing"],"best_for":["multi-tenant AI platforms needing per-tenant isolation and attribution","enterprises tracking agent actions back to specific users or service accounts","teams implementing audit trails for regulatory compliance"],"limitations":["Identity extraction is adapter-based — requires custom implementation for non-standard identity formats","Context propagation relies on MCP server cooperation — cannot extract identity from tools that don't support metadata headers","No built-in identity caching — each tool call re-validates identity, adding latency"],"requires":["Node.js 16+","MCP server with metadata/header support","identity provider configuration (JWT issuer, API key store, OAuth2 endpoint)","optional: custom identity adapter for proprietary identity formats"],"input_types":["identity credentials (JWT token, API key, OAuth2 bearer token)","agent context metadata (user ID, organization, roles, attributes)"],"output_types":["enriched audit log entries with agent identity and context","identity claims available to policy rules for authorization decisions"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_5","uri":"capability://automation.workflow.tool.call.performance.monitoring.and.metrics.collection","name":"tool call performance monitoring and metrics collection","description":"Collects detailed performance metrics for each tool call including execution duration, latency percentiles, error rates, and resource usage. Metrics are aggregated by tool, agent, and time window and exposed via a metrics API or exported to monitoring systems (Prometheus, Datadog, CloudWatch). Enables performance-based alerting (e.g., alert if tool latency exceeds 5 seconds) and capacity planning.","intents":["I want to monitor the performance of my agent's tool calls and detect slowdowns","I need to identify which tools are causing bottlenecks in my agent's execution","I want to set up alerts if tool call latency degrades beyond acceptable thresholds"],"best_for":["platform teams operating AI agents at scale needing performance visibility","developers optimizing agent performance and tool selection","operations teams managing SLAs for AI agent services"],"limitations":["Metrics collection adds 5-10ms overhead per tool call for instrumentation","No built-in time-series database — metrics must be exported to external monitoring system for long-term retention","Percentile calculations (p95, p99) require in-memory buffering — may consume significant memory for high-volume agents"],"requires":["Node.js 16+","imara middleware active","optional: monitoring system integration (Prometheus, Datadog, CloudWatch, etc.)"],"input_types":["tool call execution events (start time, end time, success/failure)","resource usage data (if available from MCP server)"],"output_types":["performance metrics (duration, latency percentiles, error rate, throughput)","metrics API endpoint or export format (Prometheus, JSON, CloudWatch format)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-imara__cap_6","uri":"capability://safety.moderation.tool.call.result.validation.and.schema.enforcement","name":"tool call result validation and schema enforcement","description":"Validates tool call results against expected schemas or patterns before returning them to the agent, catching malformed responses, missing fields, or type mismatches. Supports JSON Schema validation, custom validation functions, and configurable error handling (fail-open, fail-closed, or transform). Enables early detection of tool bugs or API changes that would otherwise propagate errors downstream.","intents":["I want to ensure tools return data in the expected format before my agent processes it","I need to detect when a tool's API has changed or is returning unexpected data","I want to automatically transform or sanitize tool results before the agent sees them"],"best_for":["teams integrating with external APIs that may change or fail unexpectedly","developers building robust agent systems that need to handle tool failures gracefully","platforms providing tool integrations to customers and needing quality guarantees"],"limitations":["Schema validation adds 5-15ms latency per tool call depending on schema complexity","No built-in schema inference — schemas must be manually defined or imported from tool documentation","Custom validation functions are synchronous only — cannot perform async validation (e.g., checking against a database)"],"requires":["Node.js 16+","imara middleware active","JSON Schema definitions or custom validation functions for each tool"],"input_types":["tool call results (JSON, text, or structured data)","validation schemas (JSON Schema format or custom validator functions)"],"output_types":["validated results (passed through if valid, transformed if configured, or error if invalid)","validation error details (schema violations, missing fields, type mismatches)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","MCP-compatible agent framework (Claude SDK, LangChain MCP integration, or custom MCP client)","Writable storage backend for audit logs (PostgreSQL, MongoDB, S3, or similar)","MCP server integration with imara middleware","Policy definitions in imara policy format (JSON or YAML)","Optional: external identity provider (OAuth2, SAML, or custom)","imara policy enforcement layer active","alert destination configuration (webhook URL, email address, Slack token, etc.)","optional: baseline data for threshold tuning","populated audit trail from prior tool call interception"],"failure_modes":["Audit trail storage is not built-in — requires external persistence layer (database, log aggregation service)","Adds latency to tool calls proportional to audit write speed — typically 10-50ms per call depending on storage backend","Does not capture internal LLM reasoning or prompt content, only tool boundaries","Policy evaluation adds 5-20ms latency per tool call depending on rule complexity","No built-in policy versioning or rollback — requires external version control integration","Policy language is custom DSL — requires learning new syntax, not standard REGO or Opa","Alert fatigue risk if thresholds are not tuned carefully — requires baseline profiling of normal agent behavior","No machine learning-based anomaly detection — uses only rule-based and threshold detection","Alert delivery is best-effort — no guaranteed delivery for webhook/email notifications","Report generation performance degrades with large audit logs (>1M entries) — may require pagination or time-range filtering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-04-22T08:11:33.708Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-imara","compare_url":"https://unfragile.ai/compare?artifact=npm-imara"}},"signature":"GzW1LUs68pEnebUO4WHBbhIrYl1jgum0QBO2iVK9qNT9dRVA/ybzENDFgNbyE141Q2JVwmzXXQ+BgBeyg269DQ==","signedAt":"2026-06-23T02:16:22.945Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-imara","artifact":"https://unfragile.ai/npm-imara","verify":"https://unfragile.ai/api/v1/verify?slug=npm-imara","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"}}