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Uses a schema-based rule engine that matches tool names, parameters, and caller context against predefined policies, blocking or allowing calls based on configurable access control lists and role-based permissions.","intents":["Prevent AI agents from calling dangerous or sensitive tools without explicit approval","Enforce organizational governance policies on which tools LLM agents can access","Implement least-privilege access for autonomous agent systems","Audit which tools were attempted and which were blocked for compliance"],"best_for":["Teams deploying autonomous AI agents in production environments","Organizations with regulatory compliance requirements (SOC2, HIPAA, etc.)","Developers building multi-tenant agent platforms needing per-tenant tool restrictions","Security-conscious teams implementing human-in-the-loop AI workflows"],"limitations":["Authorization decisions are synchronous — adds latency to every tool call (exact overhead depends on rule complexity)","No built-in support for dynamic policy updates without redeployment or server restart","Rule engine limited to static parameter matching — cannot evaluate runtime state or external context","No native integration with external authorization systems (OAuth, SAML, LDAP) — requires custom adapter code"],"requires":["Node.js 16+ or compatible JavaScript runtime","MCP server implementation (Claude SDK, Anthropic MCP, or compatible)","Policy configuration file or programmatic rule definition"],"input_types":["MCP tool call requests (tool name, parameters, caller context)","Authorization policy definitions (JSON/YAML rule sets)"],"output_types":["Authorization decision (allow/deny/require-approval)","Audit log entries with tool call metadata"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tegata__cap_1","uri":"capability://automation.workflow.human.in.the.loop.approval.workflows.for.tool.calls","name":"human-in-the-loop approval workflows for tool calls","description":"Provides a mechanism to flag certain tool calls as requiring human approval before execution, routing them to a notification/approval system that can accept or reject the call. Implements a callback-based approval pattern where blocked calls are queued with context (tool name, parameters, reason for block) and can be asynchronously approved by authorized humans.","intents":["Require manual approval for high-risk tool calls (database mutations, external API calls, financial transactions)","Implement escalation workflows where certain tool calls need supervisor sign-off","Create audit trails showing which tool calls required human intervention and who approved them","Enable safe autonomous operation with guardrails for unpredictable scenarios"],"best_for":["Financial services and healthcare teams deploying AI agents with compliance requirements","Organizations wanting to gradually increase agent autonomy while maintaining safety","Teams building customer-facing AI agents that need to escalate to human support","Developers implementing approval-required workflows for sensitive operations"],"limitations":["Approval workflow is asynchronous — tool execution is delayed until human decision, potentially blocking agent progress","No built-in notification system — requires custom integration with Slack, email, or other alerting platforms","Approval timeout handling not specified — unclear behavior if human doesn't respond within time window","Single approval model — no support for multi-level approval chains or quorum-based decisions"],"requires":["MCP server with tegata middleware installed","Custom approval handler implementation or integration with notification service","Mechanism to deliver approval decisions back to the MCP server (webhook, polling, or message queue)"],"input_types":["Tool call requests flagged for approval","Human approval/rejection decisions with optional comments"],"output_types":["Approval queue with pending tool calls and context","Approval decision with timestamp and approver identity","Audit log of approval decisions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tegata__cap_2","uri":"capability://tool.use.integration.policy.based.tool.call.filtering.with.parameter.validation","name":"policy-based tool call filtering with parameter validation","description":"Evaluates tool calls against declarative authorization policies that can match on tool names, parameter values, parameter types, and caller attributes. Uses a rule matching engine that supports conditions like 'allow tool X only if parameter Y matches pattern Z' or 'deny tool X for all callers except role admin', enabling granular control over which tools can be called with which inputs.","intents":["Restrict database tools to read-only operations for non-admin agents","Prevent API calls to external services with sensitive data in parameters","Enforce parameter constraints (e.g., max file size, allowed domains) at the authorization layer","Create role-based tool access policies where different agents have different tool permissions"],"best_for":["Multi-agent systems where different agents need different tool access levels","Organizations implementing zero-trust security for AI agent tool access","Teams needing to enforce data residency or PII protection policies","Developers building configurable agent platforms with customer-specific tool restrictions"],"limitations":["Policy rule complexity is limited to static matching — cannot evaluate dynamic state or external data sources","Parameter validation is pattern-based — no semantic understanding of parameter meaning or impact","No built-in support for conditional policies based on time-of-day, rate limits, or resource availability","Policy syntax and evaluation performance not documented — unclear how complex rule sets impact latency"],"requires":["Policy definition format (JSON/YAML structure not fully specified in available documentation)","MCP server with tegata middleware","Tool schema definitions for parameter matching"],"input_types":["Tool call requests with full parameter payloads","Policy rule definitions with conditions and actions"],"output_types":["Authorization decision with reason (allowed, denied, or requires-approval)","Validation error details if parameters don't match policy constraints"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tegata__cap_3","uri":"capability://safety.moderation.audit.logging.and.compliance.reporting.for.tool.calls","name":"audit logging and compliance reporting for tool calls","description":"Automatically logs all tool call attempts (allowed, denied, and approval-required) with metadata including caller identity, tool name, parameters, authorization decision, timestamp, and reason for allow/deny. Generates structured audit logs compatible with compliance frameworks, enabling forensic analysis and compliance reporting for regulatory requirements.","intents":["Create immutable audit trails of all tool calls for compliance audits (SOC2, HIPAA, PCI-DSS)","Generate reports showing which tools were called, by whom, and what was approved/denied","Investigate security incidents by reviewing tool call history and authorization decisions","Demonstrate governance controls to auditors and regulators"],"best_for":["Regulated industries (finance, healthcare, government) deploying AI agents","Organizations undergoing SOC2, ISO 27001, or other compliance certifications","Teams needing to demonstrate AI governance to stakeholders and auditors","Security teams investigating unauthorized or suspicious agent behavior"],"limitations":["Audit log storage and retention not specified — no built-in persistence, requires external log aggregation","Log format and schema not documented — unclear what metadata is captured and how it's structured","No built-in log encryption or tamper-proofing — requires external security controls for compliance","No log search or query interface — requires integration with external logging platforms (ELK, Datadog, etc.)"],"requires":["MCP server with tegata middleware","External log storage system (file system, database, or log aggregation service)","Log shipping configuration if using external logging platform"],"input_types":["Tool call requests and authorization decisions from tegata middleware"],"output_types":["Structured audit log entries (JSON or similar format)","Compliance reports and audit summaries"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tegata__cap_4","uri":"capability://tool.use.integration.role.based.access.control.rbac.for.agent.tool.permissions","name":"role-based access control (rbac) for agent tool permissions","description":"Implements role-based authorization where agents or callers are assigned roles (e.g., 'admin', 'analyst', 'viewer') and tools are restricted to specific roles. Uses a role-to-tool mapping system where authorization decisions are made by checking if the caller's role has permission for the requested tool, enabling simple but scalable access control for multi-agent systems.","intents":["Assign different tool access levels to different agents based on their function","Implement least-privilege access where agents only get tools they need","Manage permissions at scale across many agents without per-agent configuration","Quickly revoke or grant tool access by changing role definitions"],"best_for":["Organizations with multiple AI agents performing different functions","Teams wanting simple, understandable access control without complex policies","Developers building agent platforms with multi-tenant or multi-user support","Operations teams managing agent permissions without deep security expertise"],"limitations":["RBAC is coarse-grained — cannot express complex conditions like 'analyst can read data but not delete'","Role assignment mechanism not specified — unclear how roles are assigned to agents or callers","No support for dynamic roles or time-based role changes","Role hierarchy and inheritance not documented — unclear if roles can be nested or composed"],"requires":["Role definitions and role-to-tool mappings","Mechanism to assign roles to agents or callers (identity provider integration or manual configuration)","MCP server with tegata middleware"],"input_types":["Agent identity or caller context","Tool call requests","Role definitions and permissions"],"output_types":["Authorization decision based on role-to-tool mapping","Audit log with role information"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tegata__cap_5","uri":"capability://tool.use.integration.mcp.middleware.integration.and.transparent.tool.call.interception","name":"mcp middleware integration and transparent tool call interception","description":"Integrates with MCP servers as a middleware layer that transparently intercepts all tool call requests before they reach tool implementations. Uses the MCP protocol's request/response model to inject authorization checks without requiring changes to tool code or client code, enabling drop-in authorization enforcement for existing MCP servers.","intents":["Add authorization to existing MCP servers without modifying tool implementations","Enforce consistent authorization policies across multiple MCP tools","Integrate authorization into MCP client-server communication transparently","Enable authorization as a separate concern from tool logic"],"best_for":["Teams with existing MCP servers wanting to add authorization without refactoring","Organizations standardizing on MCP and needing consistent governance across tools","Developers building MCP-based agent platforms with authorization requirements","DevOps teams deploying MCP servers with security policies"],"limitations":["Middleware integration pattern not fully documented — unclear how to configure tegata with different MCP server implementations","Performance overhead of middleware layer not quantified — latency impact on tool calls unknown","No support for streaming tool responses — authorization is enforced on request only","Compatibility with all MCP server implementations not verified — may require custom adapters"],"requires":["MCP server implementation compatible with middleware pattern","Node.js runtime with MCP protocol support","Configuration to wire tegata middleware into MCP request/response pipeline"],"input_types":["MCP tool call requests (CallToolRequest messages)"],"output_types":["MCP tool call responses (CallToolResult or error messages)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or compatible JavaScript runtime","MCP server implementation (Claude SDK, Anthropic MCP, or compatible)","Policy configuration file or programmatic rule definition","MCP server with tegata middleware installed","Custom approval handler implementation or integration with notification service","Mechanism to deliver approval decisions back to the MCP server (webhook, polling, or message queue)","Policy definition format (JSON/YAML structure not fully specified in available documentation)","MCP server with tegata middleware","Tool schema definitions for parameter matching","External log storage system (file system, database, or log aggregation service)"],"failure_modes":["Authorization decisions are synchronous — adds latency to every tool call (exact overhead depends on rule complexity)","No built-in support for dynamic policy updates without redeployment or server restart","Rule engine limited to static parameter matching — cannot evaluate runtime state or external context","No native integration with external authorization systems (OAuth, SAML, LDAP) — requires custom adapter code","Approval workflow is asynchronous — tool execution is delayed until human decision, potentially blocking agent progress","No built-in notification system — requires custom integration with Slack, email, or other alerting platforms","Approval timeout handling not specified — unclear behavior if human doesn't respond within time window","Single approval model — no support for multi-level approval chains or quorum-based decisions","Policy rule complexity is limited to static matching — cannot evaluate dynamic state or external data sources","Parameter validation is pattern-based — no semantic understanding of parameter meaning or impact","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T14:23:59.874Z","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-tegata","compare_url":"https://unfragile.ai/compare?artifact=npm-tegata"}},"signature":"ELqEEufYGU+bkcsLQCgsrQgn0yJbL4NjRCY4K6521vGLb9DrjfsGYufWeIHu1qUofa38gVhU3TnKtBtM7SkhBg==","signedAt":"2026-06-20T02:02:06.330Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-tegata","artifact":"https://unfragile.ai/npm-tegata","verify":"https://unfragile.ai/api/v1/verify?slug=npm-tegata","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"}}