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Uses a hash-chain or merkle-tree approach to create tamper-evident audit trails where any modification to prior decisions is cryptographically detectable, enabling compliance with EU AI Act transparency and accountability requirements.","intents":["I need to prove to regulators exactly what decision an AI system made and why, with cryptographic proof that the record hasn't been altered","I want to create an immutable audit trail of all tool calls my LLM agent made so I can trace failures or misuse back to specific decisions","I need to generate compliance receipts that satisfy EU AI Act documentation requirements for high-risk AI systems"],"best_for":["regulated enterprises building high-risk AI systems (financial services, healthcare, hiring)","compliance teams needing audit-ready decision logs for regulatory inspection","AI governance teams implementing accountability frameworks for LLM agents"],"limitations":["Receipt generation adds latency per tool call (exact overhead unknown without benchmarks)","Requires external storage or database for receipt persistence — no built-in state management","Cryptographic verification requires access to signing keys and certificate chain","No built-in integration with regulatory reporting systems — receipts must be manually exported or integrated"],"requires":["Node.js 16+ (typical for npm packages)","MCP-compatible host (Claude, custom LLM framework, or MCP server)","Cryptographic library support (likely Node.js crypto or similar)","Storage backend for receipt persistence (database, file system, or cloud storage)"],"input_types":["MCP tool call metadata (tool name, parameters, model context)","Mandate or governance policy identifier","Model output and decision context"],"output_types":["Signed receipt object (JSON with signature)","Audit trail entry (structured log)","Compliance report (formatted for regulatory submission)"],"categories":["tool-use-integration","safety-moderation","governance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-clgplatformmcp__cap_1","uri":"capability://tool.use.integration.real.time.mandate.enforcement.for.tool.call.authorization","name":"real-time mandate enforcement for tool call authorization","description":"Intercepts MCP tool calls before execution and validates them against a set of governance mandates (policies, rules, constraints) in real-time, blocking or modifying calls that violate policy. Implements a policy evaluation engine that can enforce constraints like rate limits, tool whitelists/blacklists, parameter validation, and conditional access rules based on model state, user context, or decision history.","intents":["I need to prevent my LLM agent from calling certain tools or APIs in specific contexts (e.g., no financial transfers without human approval)","I want to enforce rate limits or quota policies on tool usage to prevent abuse or cost overruns","I need to conditionally allow tool calls based on the current mandate or governance policy in effect"],"best_for":["teams deploying LLM agents in production with strict governance requirements","organizations needing to enforce compliance policies across multiple AI systems","developers building agentic systems where tool access must be dynamically controlled"],"limitations":["Policy evaluation latency depends on mandate complexity — no published performance benchmarks","Mandate definitions must be pre-configured; no dynamic policy generation from natural language","No built-in support for distributed policy consensus — assumes single policy source of truth","Blocked tool calls are rejected entirely; no built-in fallback or alternative tool routing"],"requires":["MCP-compatible host or server","Governance mandate definitions (format and schema unknown from description)","Policy evaluation engine (likely bundled, but integration points unclear)"],"input_types":["MCP tool call request (tool name, parameters, context)","Governance mandate or policy definition","Model state or decision context"],"output_types":["Authorization decision (allow/deny/modify)","Modified tool call (if parameters adjusted)","Rejection reason or policy violation details"],"categories":["tool-use-integration","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-clgplatformmcp__cap_2","uri":"capability://tool.use.integration.mcp.protocol.wrapper.with.governance.metadata.injection","name":"mcp protocol wrapper with governance metadata injection","description":"Wraps standard MCP tool definitions and invocations to automatically inject governance metadata (mandate IDs, policy context, decision timestamps, audit identifiers) into the protocol layer. Extends MCP's native schema to carry governance context through the entire tool call lifecycle, enabling downstream systems to understand the governance context in which each decision was made without requiring separate metadata channels.","intents":["I want to extend MCP tool definitions to include governance context without modifying the underlying LLM or tool implementations","I need to pass governance mandate information through the MCP protocol so all downstream systems understand the policy context","I want to automatically tag all tool calls with audit identifiers and decision context for compliance tracking"],"best_for":["teams integrating governance into existing MCP-based systems without rewriting tool code","organizations standardizing on MCP and needing governance as a protocol-level concern","developers building MCP servers that must comply with governance frameworks"],"limitations":["Wrapper adds overhead to every MCP call (latency impact unknown)","Requires MCP host/server compatibility — may not work with all MCP implementations","Metadata injection is transparent to the underlying tools — tools cannot directly access governance context without additional integration","No built-in schema versioning — breaking changes to governance metadata could require protocol updates"],"requires":["MCP-compatible host or server","Node.js 16+","Understanding of MCP protocol and tool schema"],"input_types":["Standard MCP tool definition (JSON schema)","MCP tool call request","Governance mandate or policy context"],"output_types":["Extended MCP tool definition with governance metadata","MCP tool call with injected governance context","Audit-enriched tool invocation record"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-clgplatformmcp__cap_3","uri":"capability://safety.moderation.eu.ai.act.compliance.documentation.generation","name":"eu ai act compliance documentation generation","description":"Automatically generates compliance documentation artifacts (decision logs, impact assessments, audit reports) formatted to satisfy EU AI Act requirements for high-risk AI systems. Aggregates decision receipts, mandate enforcement records, and governance metadata into structured reports that demonstrate transparency, accountability, and human oversight requirements mandated by the regulation.","intents":["I need to generate compliance documentation for regulatory inspection that proves my AI system meets EU AI Act transparency requirements","I want to automatically create audit reports showing that my AI system's decisions were made under appropriate governance mandates","I need to demonstrate to regulators that my system has human oversight and accountability mechanisms in place"],"best_for":["EU-based organizations deploying high-risk AI systems (financial, healthcare, hiring, law enforcement)","compliance teams preparing for regulatory audits or inspections","legal teams documenting AI governance for regulatory submission"],"limitations":["Report format and content requirements may not align with all regulatory interpretations of EU AI Act","No built-in integration with regulatory submission systems — reports must be manually uploaded or exported","Compliance is only as strong as the underlying governance implementation — cannot generate compliance if mandates are not enforced","Report generation requires complete decision history — gaps in audit trail will result in incomplete compliance documentation"],"requires":["Complete decision receipt history from tamper-evident receipt capability","Mandate enforcement records showing policy violations and approvals","Governance metadata from all tool invocations"],"input_types":["Decision receipts (signed audit trail)","Mandate enforcement logs","Governance metadata and policy definitions"],"output_types":["Compliance report (PDF or structured format)","Decision log (formatted for regulatory review)","Impact assessment document","Audit trail export (for regulatory submission)"],"categories":["safety-moderation","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-clgplatformmcp__cap_4","uri":"capability://memory.knowledge.decision.context.preservation.and.retrieval.for.audit.trails","name":"decision context preservation and retrieval for audit trails","description":"Captures and stores the complete decision context for every tool call (model version, prompt, parameters, output, timestamp, user/system context) in a structured format that can be retrieved and analyzed for audit purposes. Implements a queryable audit store that allows filtering and searching decisions by mandate, tool, timestamp, or outcome, enabling post-hoc analysis of system behavior and decision patterns.","intents":["I need to retrieve the exact prompt and parameters that led to a specific tool call for debugging or compliance review","I want to analyze patterns in my AI system's decisions to identify potential bias or policy violations","I need to reconstruct the complete decision context for a specific tool call to explain it to regulators or users"],"best_for":["compliance teams conducting post-hoc audits of AI system behavior","developers debugging unexpected tool call patterns or failures","researchers analyzing decision patterns to identify bias or governance gaps"],"limitations":["Storage requirements scale with decision volume — no built-in data retention or archival policies","Query performance depends on underlying storage backend — no published performance benchmarks","Context preservation is only as complete as the information captured at decision time — missing context cannot be reconstructed","No built-in anonymization or data minimization — compliance with GDPR data retention requirements requires external implementation"],"requires":["Storage backend for decision context (database, file system, or cloud storage)","Decision receipt generation (from tamper-evident receipt capability)","Query interface or API for audit trail access"],"input_types":["Complete decision context (model, prompt, parameters, output, metadata)","Query filters (mandate, tool, timestamp, outcome)"],"output_types":["Decision record (structured format with full context)","Query results (filtered decision set)","Decision analysis report (patterns, statistics)"],"categories":["memory-knowledge","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-clgplatformmcp__cap_5","uri":"capability://tool.use.integration.model.identity.and.context.binding.for.tool.calls","name":"model identity and context binding for tool calls","description":"Binds tool calls to specific model identifiers and execution context (user ID, request ID, session ID) so that decisions can be traced back to the specific model instance and user interaction that triggered them. Implements context propagation through the MCP call stack to ensure governance metadata is associated with the correct model and user.","intents":["I need to know which specific model made a particular decision so I can audit its behavior","I want to track all decisions made by a specific model instance to identify patterns or issues","I need to associate tool calls with the user who triggered them for accountability"],"best_for":["multi-model systems where different models have different trust levels or capabilities","organizations tracking per-model performance and compliance metrics","teams implementing user-level accountability for AI-driven decisions"],"limitations":["Model identity must be provided by the caller — no automatic model detection or inference","Context propagation depends on correct implementation by MCP client/server — if context is lost in the call stack, binding fails","No built-in support for model versioning — unclear how to handle model updates or rollbacks"],"requires":["model identifier (string or UUID)","execution context (user ID, request ID, session ID)","MCP client or server implementation that supports context propagation"],"input_types":["model identifier","user context (user ID, request ID, session ID)","tool call metadata"],"output_types":["bound tool call with model and user context","context metadata for audit trail"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (typical for npm packages)","MCP-compatible host (Claude, custom LLM framework, or MCP server)","Cryptographic library support (likely Node.js crypto or similar)","Storage backend for receipt persistence (database, file system, or cloud storage)","MCP-compatible host or server","Governance mandate definitions (format and schema unknown from description)","Policy evaluation engine (likely bundled, but integration points unclear)","Node.js 16+","Understanding of MCP protocol and tool schema","Complete decision receipt history from tamper-evident receipt capability"],"failure_modes":["Receipt generation adds latency per tool call (exact overhead unknown without benchmarks)","Requires external storage or database for receipt persistence — no built-in state management","Cryptographic verification requires access to signing keys and certificate chain","No built-in integration with regulatory reporting systems — receipts must be manually exported or integrated","Policy evaluation latency depends on mandate complexity — no published performance benchmarks","Mandate definitions must be pre-configured; no dynamic policy generation from natural language","No built-in support for distributed policy consensus — assumes single policy source of truth","Blocked tool calls are rejected entirely; no built-in fallback or alternative tool routing","Wrapper adds overhead to every MCP call (latency impact unknown)","Requires MCP host/server compatibility — may not work with all MCP implementations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:24:09.020Z","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-clgplatformmcp","compare_url":"https://unfragile.ai/compare?artifact=npm-clgplatformmcp"}},"signature":"LuuOtQfuPEWVvAnKALwiZU2szoORNQ3O3uXw9C7MvlJ+HyWpNjX+f92srQl6a26SVsh3h/PPty7VbZs0MlYhDw==","signedAt":"2026-06-21T02:04:12.859Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-clgplatformmcp","artifact":"https://unfragile.ai/npm-clgplatformmcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-clgplatformmcp","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"}}